Wearable sensors for continuous monitoring of vital signs for extended periods of weeks or months are expected to revolutionize healthcare services in the home and workplace as well as in hospitals and nursing homes. This invited paper describes recent research progress in wearable health monitoring technology and its clinical applications, with emphasis on blood pressure and circulatory monitoring. First, a finger ring-type wearable blood pressure sensor based on photo plethysmogram is presented. Technical issues, including motion artifact reduction, power saving, and wearability enhancement, will be addressed. Second, sensor fusion and sensor networking for integrating multiple sensors with diverse modalities will be discussed for comprehensive monitoring and diagnosis of health status. Unlike traditional snap-shot measurements, continuous monitoring with wearable sensors opens up the possibility to treat the physiological system as a dynamical process. This allows us to apply powerful system dynamics and control methodologies, such as adaptive filtering, single- and multi-channel system identification, active noise cancellation, and adaptive control, to the monitoring and treatment of highly complex physiological systems. A few clinical trials illustrate the potentials of the wearable sensor technology for future heath care services.

A flexibility-based distributed computing strategy (DCS) for structural health monitoring (SHM) has recently been proposed which is suitable for implementation on a network of densely distributed smart sensors. In that approach, a hierarchical strategy is proposed in which adjacent smart sensors are grouped together to form sensor communities. Structural health monitoring is done without relying on central data acquisition and processing. The main purpose of this paper is to experimentally verify this flexibility-based DCS approach. The damage locating vector method that forms foundation of the DCS approach is reviewed. An overview of the DCS approach is presented. This flexibility-based approach is then experimentally verified employing a 5.6 m long three-dimensional truss structure. To simulate damage in the structure, the original truss members are replaced by ones with a reduced cross section. Both single and multiple damage scenarios are studied. Experimental results show that the DCS approach can successfully detect the damage at local elements using only locally measured information.

In a collaborative project at Lehigh and Carnegie Mellon, a MEMS acoustic emission sensor was designed and fabricated as a suite of six resonant-type capacitive transducers in the frequency range between 100 and 500 kHz. Characterization studies showed good comparisons between predicted and experimental electro-mechanical behavior. Acoustic emission events, simulated experimentally in steel ball impact and in pencil lead break tests, were detected and source localization was demonstrated. In this paper we describe the application of the MEMS device in structural testing, both in laboratory and in field applications. We discuss our findings regarding housing and mounting (acoustic coupling) of the MEMS device with its supporting electronics, and we then report the results of structural testing. In all tests, the MEMS transducers were used in parallel with commercial acoustic emission sensors, which thereby serve as a benchmark and permit a direct observation of MEMS device functionality. All tests involved steel structures, with particular interest in propagation of existing cracks or flaws. A series of four laboratory tests were performed on beam specimens fabricated from two segments (Grade 50 steel) with a full penetration weld (E70T-4 electrode material) at midspan. That weld region was notched, an initial fatigue crack was induced, and the specimens were then instrumented with one commercial transducer and with one MEMS device; data was recorded from five individual transducers on the MEMS device. Under a four-point bending test, the beam displayed both inelastic behavior and crack propagation, including load drops associated with crack instability. The MEMS transducers detected all instability events as well as many or most of the acoustic emissions occurring during plasticity and stable crack growth. The MEMS transducers were less sensitive than the commercial transducer, and did not detect as many events, but the normalized cumulative burst count obtained from the MEMS transducers paralleled the count obtained from the commercial transducer. Waveform analysis of signals from the MEMS transducers provided additional information concerning arrivals of P-waves and S-waves. Similarly, the analysis provided additional confirmation that the acoustic emissions emanated from the damage zone near the crack tip, and were not spurious signals or artifacts. Subsequent tests were conducted in a field application where the MEMS transducers were redundant to a group of commercial transducers. The application example is a connection plate in truss bridge construction under passage of heavy traffic loads. The MEMS transducers were found to be functional, but were less sensitive in their present form than existing commercial transducers. We conclude that the transducers are usable in their current configuration and we outline applications for which they are presently suited, and then we discuss alternate MEMS structures that would provide greater sensitivity.

Presented in this paper is the environmental testing of Wireless Intelligent Sensor and Actuator Network (WISAN) currently under development at Clarkson University for the use of long-term structural health monitoring of civil infrastructure. The wireless sensor nodes will undergo controlled mechanical vibration and environmental testing in the laboratory. A temperature chamber will be used to perform temperature cycle tests on the sensor nodes. The temperature chamber will also houses a small shaker capable of introducing mechanical loading under the controlled temperature cycle tests. At low temperatures, the resistance of the electronics processing and storage characteristics will be studied. Also, the testing will look at volume expansion and degradation of characteristics due to freezing, degradation of functions and performance, and mechanical characteristics caused by contraction. At high temperatures, temperature-related changes in sensor nodes due to excessively high temperatures will be investigated. Also studied will be the effects of temperature cycles, including the thermal stresses induced in the nodes and housing and the distortion caused due to expansion and contraction, fatigue, cracks, and changes in electrical characteristics due to mechanical displacement. And finally, mechanical vibration loading will be introduced to the WISAN sensor nodes. Mechanical looseness, fatigue destruction, wire disconnection, damage due to harmonic vibration, defective socket contact, joint wear, destruction due to harmonics, lead breakage, occurrence of noise and abnormal vibration, cracking will be monitored. The eventual goal of the tests is to verify WISAN's performance under anticipated field conditions in which the sensors will be deployed.

Since the recognition of the advantages of using Lamb Waves in nondestructive testing (NDT), Lamb Waves and other guided waves have attracted more and more attention nowadays. Unlike waves used in conventional ultrasonic inspection, such as bulk longitudinal and shear waves, which propagate in the region of structure immediately around the transmitting transducer, Lamb Waves can propagate over a long distance. Previously in our work, monolithic interdigitated PVDF transducers for generating surface acoustic waves have been built by using photolithography technology. These sensors have been tested to be able to successfully excite and receive Lamb Waves. As an extension of previous work, this paper focuses on the implementation of interdigitated PVDF sensors on some laminated structures made of carbon fiber composite materials with flaws of different sizes. Experiments have been conducted, and results from PVDF IDTs have been compared with those from PZTs. Efforts have been spent on identifying the components of Lamb waves propagated in these sample structures and evaluating the differences in the received signals.

We examine exploiting the inherent piezoresistivity of a polysilicon compliant mechanism to provide feedback sensing of the mechanism displacement. As the piezoresistive compliant mechanism deflects to produce motion its resistance changes producing a usable signal. The goal of this work is to improve the transient response of a thermal actuator through piezoresistive feedback control. Implementing feedback control significantly improves the actuators transient response. The actuator response time to step inputs is reduced from 800μs to 230μs with proportional control alone. The system bandwidth was increased from 500~Hz to 4~kHz with proportional control. The large overshoot in the step response or the resonant peak in the frequency response can be reduce by an appropriately tuned 2~kHz notch prefilter.

This paper presents the modeling and design optimization of a micromachined floating element piezoresistive shear stress sensor for the time-resolved, direct measurement of fluctuating wall shear stress in a turbulent flow. The sensor structure integrates side-implanted diffused resistors into the silicon tethers for piezoresistive detection. A theoretical nonlinear mechanical model is combined with a piezoresistive model to determine the electromechanical sensitivity. Lumped element modeling (LEM) is used to estimate the resonant frequency. Finite element modeling is employed to verify the mechanical models and LEM results. Two dominant noise sources, 1/f noise and thermal noise, were considered to determine the noise floor. These models were then leveraged to obtain optimal sensor designs for several sets of specifications. The cost function is the minimum detectable shear stress that is formulated in terms of sensitivity and noise floor. This cost function is subjected to the constraints of geometry, linearity, bandwidth, power and resistance. The results indicate the possibility of designs possessing dynamic ranges of greater than 85dB.

Structural health monitoring (SHM) is important for reducing maintenance costs while increasing safety and reliability. Piezoelectric wafer active sensors (PWAS) used in SHM applications are able to detect structural damage using Lamb waves. PWAS are small, lightweight, unobtrusive, and inexpensive. PWAS achieve direct transduction between electric and elastic wave energies. PWAS are essential elements in the Lamb-wave SHM with pitch-catch, pulse-echo, phased array system and electromechanical impedance methods. This paper starts with the state of the art on the impedance method for PWAS applications. Then, finite element impedance model for free and bonded PWAS with different sizes and shapes will be given. Experiments showed that the real part and imaginary part of PWAS had different usage. Applications of impedance-based structural health monitoring indicate impedance method as a good candidate for damage detection and sensor durability verification for SHM smart sensor.

This project investigates the magnetomechanical sensing behavior of iron-gallium alloys in response to applied bending loads in order to provide an experimental and analytic framework for implementing this material in novel sensor applications at the nanoscale. A series of experiments are conducted on millimeter sized cantilevered beams to verify that the material is mechanically sound as well as magnetically active in this loading configuration, with results showing a change in magnetic induction of as much as 0.3 T occurring at twice the frequency of beam vibration. These results agree well with an analytic system model based on nonlinear free energy terms. Initial work has begun on visualizing and characterizing arrays of iron-gallium nanowires, with an atomic force microscope providing preliminary images as well as force and deflection data.

One of the major problems in sensor fusion is that sensors frequently provide spurious observations which are difficult to predict and model. The spurious data from sensors must be identified and eliminated from the sensor fusion since its incorporation in the fusion pool might lead to inaccurate estimation. This paper presents a sensor fusion strategy based on Bayesian approach that can automatically identify the inconsistency in sensor data so that the spurious sensor data can be eliminated from the sensor fusion process. The proposed method adds a term to the commonly used Bayesian technique that represents the probabilistic estimate corresponding to the event that the data is not spurious conditioned upon the data and the true state. This term has the effect of increasing the variance of the posterior distribution when data from one of the sensors is inconsistent with respect to the other. The proposed strategy was verified with the help of extensive computations performed on simulated data from three sensors. A comparison was made between two different fusion schemes: centralized fusion in which data from all sensors were fused at once, and decentralized or sequential Bayesian method which provided opportunity to identify and eliminate spurious data from the fusion process. The simulations verified that the proposed strategy was able to identify spurious data, and its elimination from the fusion process led to better estimation.

We present UV-induced intrinsic Fabry-Perot interferometric (IFPI) fiber sensors and a frequency-division-multiplexing (FDM) scheme for quasi-distributed temperature and strain sensing. We present a spectrum-based measurement system with a swept laser source to measure the fringe patterns of IFPI sensors serially arranged along a single fiber. The FDM scheme is based on the multiplexing of sub-carrier frequencies generated by the frequency-modulation of a continuouswave light source. IFPI sensors with different optical path differences (OPD) will have different sub-carrier frequencies. We use band pass filters to select individual frequency component and use frequency-estimation based signal processing algorithms to determine the OPD of each sensor. Experimental results for multiplexed temperature and strain sensing are demonstrated. The performance of the multiplexing system is discussed.

In this note, we revisit the problem of actuator/sensor placement in large civil infrastructures and flexible space structures within the context of spatial robustness. The positioning of these devices becomes more important in systems employing wireless sensor and actuator networks (WSAN) for improved control performance and for rapid failure detection. The ability of the sensing and actuating devices to possess the property of spatial robustness results in reduced control energy and therefore the spatial distribution of disturbances is integrated into the location optimization measures. In our studies, the structure under consideration is a flexible plate clamped at all sides. First, we consider the case of sensor placement and the optimization scheme attempts to produce those locations that minimize the effects of the spatial distribution of disturbances on the state estimation error; thus the sensor locations produce state estimators with minimized disturbance-to-error transfer function norms. A two-stage optimization procedure is employed whereby one first considers the open loop system and the spatial distribution of disturbances is found that produces the maximal effects on the entire open loop state. Once this "worst" spatial distribution of disturbances is found, the optimization scheme subsequently finds the locations that produce state estimators with minimum transfer function norms. In the second part, we consider the collocated actuator/sensor pairs and the optimization scheme produces those locations that result in compensators with the smallest norms of the disturbance-to-state transfer functions. Going a step further, an intelligent control scheme is presented which, at each time interval, activates a subset of the actuator/sensor pairs in order provide robustness against spatiotemporally moving disturbances and minimize power consumption by keeping some sensor/actuators in sleep mode.

The capability and sensitivity of an electromagnetic (EM) sensor to be used as a non destructive evaluation (NDE) technique to detect and monitor corrosion in structural steels has been evaluated. Three structural carbon steels: AISI 1018, AISI 1045, and Stress Proof, were used for the study. The effect of corrosion on the magnetic properties of the steels was evaluated. Correlation curves and equations relating mass loss due to corrosion at early stages and magnetic property are presented. Based on the results it is established that the EM sensor has the potential to be used as a reliable NDE tool to detect corrosion at early stages based on the variation in magnetic saturation. These results are used to estimate and monitor the degree of damage in terms of mass loss.

A frequency sweep from 50 to 200 kHz of guided mechanical waves have been conducted to detect and assess corrosion damage in steel reinforced mortar specimens with seeded defects and in specimens undergoing accelerated corrosion using impressed current. The sweep was conducted by invoking primarily the fundamental longitudinal mode of propagation, i.e., the L(0,1) mode. The decay of waveform energy (indicative of attenuation) at different frequencies is presented and discussed in terms of corrosion damage. Experimental results indicate that the percentage of corrosion damage can be detected and evaluated invoking the fundamental longitudinal mode of propagation.

A guided wave approach to characterize the evolution of fresh mortar during the first twenty-four hours of hydration is presented. Using a through-transmission system, the method measures the energy leakage of the first torsional wave mode from a circular steel bar to the surrounding mortar. The evolution of mortar properties are discussed and correlated with the attenuation of the guided wave. The study shows that the technique is useful for monitoring the microstructure development and the curing rate of varying water-cement ratios (w/c = 0.40, 0.50, and 0.60). The effects of chemical (accelerant and retardant) and mineral (silica fume and fly ash) admixtures on the guided wave energy leakage are also presented and discussed. The guided wave approach is sensitive to changes in the water-cement ratio and to the addition of admixtures. Mortar compressive strength measurements at 12, 18, and 24 hours of hydration for the different mortar mixtures are presented. A correlation relating the energy leakage of the guided wave the strength development of the mortar is discussed.

At first, two kinds of miniature LRB models are prepared, one model has normal lead damper, the other has abnormal one. And they are scanned by ultrasonic wave using ultrasonic transducers and AE sensors. The output signals obtained through the inside are different from each other. Infinite route is assumed as ultrasonic wave propagation line. But, in this examination, we found that the main route is some layers of steel. Then, the tomography construction is adopted to detect the condition of lead plug in one layer of steel plate. The full acoustic wave solution in an arbitrary two-dimensional object is computed, which is subjected to specified acoustic sources and material parameters. This algorithm is basically a finite difference method. As a result, it is successful to know about ultrasonic wave propagation in one layer of steel and consistent correlation with the output signal. The feature vectors are created by calculation of second power average of the amplitude, and these vectors are used as the data for pattern recognition on the basis of these information. The accuracy of pattern recognition is significantly enhanced in consistent correlation with the condition of lead plug. In this study, active damage detection method to evaluate the status of LRB was proposed. It is possible to evaluate internal states of LRB by using ultrasonic wave.

Many researches have been reported on the condition monitoring of civil infrastructures by means of piezoelectric sensors. Most of them made use of the impedance change of the piezoelectric device in relation to the creation of internal damages to the structure. The impedance measurement is a well accepted method in the piezoelectric sensor area, and has been proved by many authors to be useful for civil structure diagnosis. However, the impedance measurement normally requires sophisticated equipment and analysis technology. For more general and wide application of the piezoelectric diagnosis tool, a new methodology is desired to overcome the limitations of the impedance measurement. This paper presents the feasibility of a piezoelectric oscillator sensor to detect the damages in civil infrastructures. The oscillator sensor is composed of an electronic feedback oscillator circuit and a piezoelectric thickness mode vibrator to be attached to the structure of interest. Damage to the structure causes a change in the impedance spectrum of the structure, which results in a corresponding change of the resonant frequency of the structure. The oscillator sensors can instantly detect the frequency change in a very simple manner. Feasibility of the piezoelectric oscillator sensor was verified in this work with a sample aluminum plate where artificial cracks of different depth were imposed in sequence. Validity of the measurement was confirmed through comparison of the experimental data with the results of finite element analyses of the plate with cracks. Performance of the oscillator sensor was also compared with that of its conventional counterpart, i.e. impedance measurement, to manifest the superiority of the oscillator sensor.

A modified one-sided measurement technique is proposed for Rayleigh wave (R-wave) velocity measurement in concrete. The scattering from heterogeneity may affect the waveforms of R-waves in concrete, which may make the R-waves dispersive. Conventional one-sided techniques do not consider the scattering dispersion of R-waves in concrete. In this study, the maximum energy arrival concept is adopted to determine the wave velocity by employing its continuous wavelet transform. Experimental study was performed to show the effectiveness of the proposed method. The present method is applied to monitor the strength development of early-age concrete. A series of experiments were performed on early-age concrete specimens with various curing conditions. Results reveal that the proposed method can be effectively used to measure the R-wave velocity in concrete structures and to monitor the strength development of early-age concrete.

This paper experiments on each of two control algorithms which are adapted into a unified control algorithm, in order to decide which is better for semi-active control of civil structures using a squeeze mode smart damper. These algorithms are Lyapunov algorithm and a clipped optimal algorithm, both of which are superior in efficiency and reliability to any existing semi-active control algorithms. Such adaptation makes it easier to develop a control algorithm because it accommodates both continuous and discrete time signals at the same time, and to analyze the control characteristics in the case of broadly distributed natural frequency by securing enlarged stable regions. In order to prove its validity, we performed vibration control tests using a prototype steel plate girder bridge. Since the model is a reduced one, we also scaled EI Centro earthquake wave to the same scale as the reduced model bridge. Various performance indexes have been used to see which algorithm is most effective in control. Also, other experiments were performed to define the control characteristics which would enable us to see how all control conditions--displacement control, force control, and acceleration control--work with each control algorithm. Those experiments showed that each control algorithm works differently according to each different control condition. It is found that Lyapunov algorithm of the two is more effective for semi-active control in the unified control system. Therefore, it is necessary to design a control system according to structural conditions and circumstances.

Since the concept of structural control was proposed by J.P.T. Yao, tremendous progress has been made over the last three decades toward making active structural control a viable technology for enhancing structural functionality and safety against natural hazards such as strong earthquakes and high winds. However, because of the high dimensionality and multiple-input-multi-output nature of civil structure model, it is difficult to design a control strategy to achieve desired stability, robustness with centralized control method. Hierarchical decentralized control strategy is employed in this paper in response to this difficulty. In this approach, the structure to be controlled is divided into a set of substructures. A hierarchical decentralized control consists of two levels: the low-level subsystem by which each substructure is controlled independently by local controllers and local information, and the high-level system that we call it global control system, which takes the outputs of every substructure as its input, and eliminates interconnection among substructures. With LQR active control algorithm in Matlab environment, the centralized control and hierarchical decentralized control strategy are implemented on a 20-storey shear building under dynamic excitation. The simulated results show that both the centralized control and hierarchical decentralized control are able to control the vibration of building. Compared with centralized control, hierarchical decentralized control method has a good control effect with preferably suppressed vibration response and less control force. The study in this paper presents a promising technology for structural vibration control.

This paper investigates the feasibility and efficacy of an MR damper-based control system introducing an electromagnetic induction (EMI) part, for suppressing vibration of building structures subjected to seismic loadings. In the proposed control system, the EMI part composed of a permanent magnet and a coil converts the kinetic energy of the relative motion between a building and a damper into the electric energy, which is used for a change in damping characteristics of the MR damper. Since the EMI part can be used as a controller, which determines the command voltage input according to structural responses, as well as a power source, the proposed control system can be much more compact, convenient, and economic than a conventional active/semiactive system that needs a power supply, a controller and sensors. To verify the feasibility and efficacy of the proposed control system, a shaking table test of a small-scale building model employing the MR damper with the EMI part is conducted. The performance of the proposed control system is compared with that of conventional semiactive control systems using an MR damper.

An optimal placement of MR dampers using genetic algorithm (GA) is put forward in this paper in order to reduce the vibration responses of high-rise building under wind load. The shear dynamic model and equation of motion of the structural system are set up and some parameters of the system are determined based on the model considering the torsion effects of the building. Moreover, an optimal installation model for MR dampers based on genetic algorithm is set up. To simulate the vibration procedures under wind load, a 12-story reinforced concrete eccentric frame structure is used as an example to show the optimal steps and response control effect. The results of the simulation show that genetic algorithm can be used effectively and economically in the optimal installation design of MR dampers in high-rise eccentric buildings to decrease the structural vibration responses induced by wind load.

This paper presents a smart passive damping system (SPDS) for reducing stay cable vibrations. Stay cables, such as used in cable-stayed bridges, are prone to vibration due to their low inherent damping characteristics. Recently some studies have shown that semiactive control systems using Magnetorheological(MR) dampers can potentially achieve higher performance levels and adaptability with few of the detractions as compared their passive counterparts. However, most semi-active and active control systems that use MR dampers require additional power supplies, controllers, and sensors, adding complexity into the system. The complexity may not be desirable to effectively control many large civil structures. This paper proposes a novel SPDS with MR dampers. The smart passive device includes an electromagnetic induction (EMI) system to power the MR damper and adjust itself to external loadings. Thus, SPDS dose not require any control system. The numerical study considered 12.56m stay cable to evaluate the dynamic performance of the SPDS for mitigating the vibration of stay cables. Moreover, the performances of the smart passive damping system are compared with those of an equivalent linear viscous damper and an MR damper operated in a pssive-mode. Results showed SPDS has competitive performance with others despite of its simplicity.

An experimental study on vibration control of one stay cable using a magnetorheological fluid (MR) damper is described in the paper. A 14m-long stay cable model, which is a 1:16 scale model of a 220m-long prototype stay cable in the actual structure, is established for the experimental investigation.The planar sinusoidal excitations with the resonant frequencies are generated by the exciter installed perpendicular to the stay cable model at a point near the low anchorage. The modal testing on the unimpeded stay cable is first performed to identify the actual modal properties and the dynamic performances. Then a series of vibration control tests are conducted on the stay cable incorporated with a small-size MR damper near the low anchorage under the sinusoidal excitations with the first two modal resonant frequencies. The control efficacies and the dynamic performances of the combined cable/MR damper system corresponding to the different current inputs to the MR damper and the semi-active MR damper are investigated comparatively. The experimental results of the vibration control of the stay cable model indicate that the semi-active MR damper can achieve much better control efficacy than the passive MR dampers supplied with constant currents, and the reason can be attributed to the pseudo-negative stiffness generated by the semi-active MR damper.

The electromechanical (E/M) impedance method is a powerful technique in active structural health monitoring (SHM). E/M impedance method utilizes as its main apparatus an impedance analyzer that reads the in-situ E/M impedance of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. Present-day impedance analyzer equipments (e.g.HP4194) are bulky, heavy and expensive laboratory equipment that cannot be carried into the field for on-site structural health monitoring. This paper presented the development of a compact and low-cost impedance analyzer system. First, two types of impedance measurement approaches were evaluated in a PC-based simplified impedance analyzer system. It was found that the first approach, which measures impedance frequency by frequency, is very accurate but is not time-efficient and needs more efforts to be implemented. As for the second approach, which measures impedance using broad-band excitation and transfer function method, provides a good compromise among the measurement time-efficient, accuracy and implementation efforts. Experimental results show that this approach can be used for E/M impedance method for structural health monitoring. Second, to eliminate the PC in the measurement system, a DSP-based impedance analyzer system is proposed for further miniaturization. The system hardware configuration and design, software state flow for impedance measurement, and preliminary testing were presented in details.

For some time, the smart materials and structures community has focused on transducer effects, and the closest advance into actually having the "structure" show signs of intelligence is implementing adaptive control into a smart structure. Here we examine taking this a step further by attempting to combine embedded computing into a smart structure system. The system of focus is based on integrated structural health monitoring of a panel which consists of a completely wireless, active sensing systems with embedded electronics. We propose and discuss an integrated autonomous sensor "patch" that contains the following key elements: power harvesting from ambient vibration and temperature gradients, a battery charging circuit, local computing and memory, active sensors, and wireless transmission. These elements should be autonomous, self contained, and unobtrusive compared to the system being monitored. Each of these elements is discussed as a part of an integrated system to be used in structural health monitoring applications.

We show that with a low number of sensors one can estimate, in real time, the full state of distributed parameter systems like the velocity fields of magnetohydrodynamic flows (liquid metals or plasmas in fusion) or the displacement distribution in cantilever beams (as employed in atomic force microscopy). It suffices to place the sensors at the boundary of the domain of the distributed parameter system-for example, electrodes placed at the wall of an MHD flow, or a laser sensor of the beam tip displacement. The estimation of the infinite-dimensional state of the system is performed using observers designed with the method of "backstepping" which guarantees their exponential convergence to the unsteady motion of the true system. The observer design is performed without any spatial discretization-directly on the non-reduced partial differential equation models. The observer gains do not require the solution of any high-dimensional nonlinear equations, like Riccati equations used in Kalman filters. Instead, the observer gains require the solution of a linear hyperbolic PDE, which is readily solvable both numerically and symbolically. We present simulation results that illustrate the use of our observers.

Monitoring the shape of morphing is essential for their effective and safe operation. However, current sensing systems such as fiber optic sensors are expensive, rigid, and unsuitable for monitoring large shape changes without being susceptible to failure or performance degradation. Therefore, a new class of sensors that does not suffer from these serious limitations is presented. The proposed sensor system relies in its operation on a specially configured distributed network of wires that are embedded in the composite fabric of these structures. The output of the sensor network is wirelessly transmitted to a control processor to compute the linear and angular deflections, the shape, and maps of the strain distribution and power flow over the entire surface of the morphing. The deflection and shape information are vital to ascertain that the structure is properly deployed and that its surfaces are operating wrinkle-free. The strain map ensures that the structure is not loaded excessively to adversely affect its service life. While the power flow map provides a metric that uniquely identifies the structural health in a manner that mimics biological systems which tend to redistribute the load and redirect its path away from the injured sites. The equations governing the operation of the sensor network are developed for a beam-like morphing structure using the non-linear theory of finite elements. The resulting equations will provide the sensor with its unique interpolation capabilities that make it possible to map the linear and angular deflection and strain fields as well as the power flow distribution over the entire surface of the morphing structure. The theoretical and experimental characteristics of the sensor network are determined under static and dynamic loading conditions. The results obtained are used to demonstrate the merits and potential of this new class of sensors as a viable means for monitoring the static and dynamic deflections of 1-D morphing structures. Integration of the proposed sensor network with the supporting electronics and with arrays of flexible actuators will enable the development of a self-contained, actively controlled, and autonomously operating new generation of morphing.

This paper presents a new class of piezoelectric based energy harvesting power sources for use in gun-fired munitions or other similar applications requiring high G survivability . These power sources are designed to harvest energy from the firing acceleration as well as vibratory motion and spinning of munitions during their flight and convert it to electrical energy that could be used directly by power consuming electronics onboard munitions or stored. The power sources are designed to withstand firing accelerations in excess of 100,000 G. The power sources have been shown to have the potential of completely eliminating the need for chemical batteries in many fuzing applications, while having the added advantage of providing for considerably more safety and long shelf life. Prototypes of a number of designs of this class of energy harvesting power sources for various power requirements have been constructed and successfully tested in the laboratory and by the U. S. Army (ARDEC) using air guns.

Prototype sensors have been developed to detect the onset of corrosion in steel reinforced concrete using non-invasive techniques. These sensors are designed to be extremely simple and low cost. The sensors are embedded in the concrete and are powered and interrogated through the use of inductively coupled magnetic fields. A new conductivity sensor is proposed, based on the design of the corrosion sensor. The conductivity sensor design is examined using circuit simulations and initial experimental results. Both sensors could be used together in a corrosion monitoring system.

As wireless sensor has emerged as a promising technology in recent years, active sensing which integrates actuation capability in a wireless sensor unit for detecting localized damage has been brought into sight in structural health monitoring. However, the inefficiency of conventional energy conversion system is a major constraint for the utilization of reactive actuator, such as piezoelectric, in the wireless sensor unit since power source is limited. This paper proposes a highly efficient low power switching amplifier to drive piezoelectric disc in the high frequency for wireless sensor applications.

The development of an innovative wireless strain sensing technology has a great potential to extend its applications in manufacturing, civil engineering and aerospace industry. This paper presents a novel wireless and powerless strain sensor with a multi-layer thick film structure. The sensor employs a planar inductor (L) and capacitive transducer (C) resonant tank sensing circuit, and a strain sensitive material of a polarized polyvinylidene fluoride (PVDF) piezoelectric thick film to realize the wireless strain sensing by strain to frequency conversion and to receive radio frequency electromagnetic energy for powering the sensor. The prototype sensor was designed and fabricated. The results of calibration on a strain constant cantilever beam show a great linearity and sensitivity about 0.0013 in a strain range of 0-0.018.

An optimized sensor design and sensor placement strategy will be extremely beneficial to both safety ensuring and cost reduction considerations of structural health monitoring systems. A new framework for structural health monitoring sensor placement optimization was recently developed at Pennsylvania State University based on genetic and evolutionary computation. The formulation of the optimization problem is to minimize the damage misdetection rate as well as to minimize the number of sensors by searching the optimized patterns of sensor placement topology on the feasible region of the structure being monitored. Two types of SHM sensors are considered. One is a single sensor scenario; the other is an actuator-damage-sensor scenario. The program was applied to a sample sensor placement problem of an aging aircraft wing. Optimized sensor placement designs are obtained. The tradeoff relationship between the sensor performance, sensor number, and the overall sensor network performance are also presented in this paper.

We have developed a technique to realize dense sensor/actuator networks that can be deployed on the centimeter to meter-scale in a cost-effective manner. The two-dimensional network is entirely processed in a CMOS foundry before it is "stretched" to the target size and "cut" to the right shape. Our technique therefore combines the maturity of CMOS fabrication techniques with the ability to quickly assemble a fully interconnected 2D sensor network of arbitrary size and shape. Both sensor node placement and network wiring are achieved automatically. The sensor networks are rugged because no wire bonds or solder connections are needed and can be seamlessly integrated into structural materials. The high node count per unit area and large amount of electronic functionality that can be integrated per node will lead to a new class of materials and structures that are truly intelligent.

This paper reports our progress toward development of a unilateral vestibular prosthesis. The sensing element of the prosthesis is a custom designed one-axis MEMS gyroscope. Similarly to the natural semicircular canal, the microscopic gyroscope senses angular motion of the head and generates voltages proportional to the corresponding angular accelerations. Then, voltages are sent to the pulse generating unit where angular motion is translated into voltage pulses. The voltage pulses are converted into current pulses and are delivered through specially designed electrodes, conditioned to stimulate the corresponding vestibular nerve branch. Our preliminary experimental evaluations of the prosthesis on a rate table indicate that the device's output matches the average firing rate of vestibular neurons to those in animal models reported in the literature. The proposed design is scalable; the sensing unit, pulse generator, and the current source can be potentially implemented on a single chip using integrated MEMS technology.

The nondestructive evaluation (NDE) of adhesively bonded structures is a complex process. Earlier work has confirmed that ultrasonic waves are influenced by the properties of the material in which they travel. Acousto-ultrasonic methods have been widely used by previous researchers to generate ultrasonic waves in plates and bonded structures for flaw detection, visualization, and measurements of the local properties of the jointed materials. This paper will present the methods and principles used for generation and propagation of ultrasonic guided waves (Lamb waves) using piezoelectric wafer active sensors (PWAS).

We introduce a technology for robust and low maintenance sensor networks capable of detecting micro-g accelerations in a wide frequency bandwidth (above 1,000 Hz). Sensor networks with such performance are critical for navigation, seismology, acoustic sensing, and for the health monitoring of civil structures. The approach is based on the fabrication of an array of highly sensitive accelerometers, each using a Fabry-Perot cavity with transparent passbands at specific wavelengths that allows for embedded optical detection and serialization. A unique feature of this approach is that no local power source is required for each individual sensor. Instead one global light source is used, providing an optical input signal which propagates through an optical fiber network from sensor to sensor. The information from each sensor is embedded into the transmitted light as a wavelength division multiplexed signal. We present for the first time the preliminary demonstration of a system of two linear serialized wavelength division multiplexed Fabry-Perot sensors of less then 1.5dB loss per device. The sensors are formed using an optical thin film multilayer structure that takes advantage of the natural non-uniformity in deposited thin films to allow serialization.

Structural health monitoring (SHM) technology may be applied to composite bonded repairs to enable the continuous through-life assessment of the repair efficacy. This paper describes an SHM technique for the detection of debonding in composite bonded patches using fibre optic Bragg grating strain sensors. A two sided doubler repair is examined in this paper. A finite element study was conducted which showed that the strain in the debonded region changed significantly compared to the undamaged state. A differential strain approach was used to facilitate the detection of debonds, where two sensors were strategically positioned so that their strain differential increased with the disbond length. With the use of matching gratings, this technique greatly reduces the interrogation equipment requirement by converting spectral information into an intensity-modulated signal, thus allowing a threshold value to be set to indicate imminent critical repair failure. An experimental investigation was conducted, using carbon/epoxy patches to carbon/epoxy substrates, to validate the theoretically predicted results. The experimental measurements agreed well with the numerical findings, indicating that the proposed scheme has great potential as a simple monitoring technique for composite bonded repairs.

Among various optical fiber distance sensing techniques, optical fiber sensors based on white-light Fabry-Perot interferometry offer the simplest configuration, the highest sensitivity, and the most precise distance measurement. Because a whitelight interferometric sensor can overcome the directional ambiguity problem encountered by the other interferometric techniques, it is especially attractive for applications where absolute distance measurement is desired. However, conventional two-fringe matching data demodulation technique can induce a half wavelength error to the distance measurement. In this paper, we present an improved fringe matching data interrogation technique that can overcome this problem. The data interrogation scheme consists of two steps: a rough estimation of the distance and the phase shift using the two fringe matching technique and a more precise estimation of the distance and the phase shift using the correctional terms derived from the fringe difference. Simulation results indicate that the algorithm can converge to the correct distance with sub-Armstrong accuracy over a large dynamic range.

In this paper, a sensor network system based on an active sensing scheme is introduced to identify cracks which may occur at a welded zone of a steel truss member. The active sensing network system offers special potential for real world applications, as it is light, cheap, and useful as a built-in system. Four pairs of pitch-catch Lamb wave signals are utilized from the active sensing network system. In order to extract damage sensitive features from the dispersive Lamb waves, a robust wavelet transform is applied to the original response signals. Peak values in the wavelet coefficients corresponding to the A0 Lamb mode are only considered for application to the damage index. The root-mean-square change of the peak values due to damage is proposed as a damage index. In addition, a least-square curve-fitting algorithm is applied to the damage indices in order to obtain a practical damage indicator with a threshold value that presents the damage tolerance. Finally, damage localization is carried out by investigating the change rates of the damage index according to each damage step. The applicability of the proposed methods has been demonstrated by an experimental study.

Signal processing algorithms for guided wave pulse echo-based SHM must be capable of isolating individual reflections from defects in the structure, if any, which could be overlapping and multimodal. In addition, they should be able to estimate the time-frequency centers, the modes and individual energies of the reflections, which would be used to locate and characterize defects. Finally, they should be computationally efficient and amenable to automated processing. This work addresses these issues with a new algorithm employing chirplet matching pursuits followed by a mode correlation check for single point sensors. Its theoretical advantages over conventional time-frequency representations in all aspects are elaborated and these are demonstrated using numerical simulations and experiments in isotropic plate structures. The issue of in-plane triangulation is then discussed and experimental work done to explore this issue is presented. This work concludes with a description of how the algorithm can be extended to composite plate structures.

The inspection of building structures, especially bridges, is currently made by visual inspection. The few non-visual methodologies make use of wired sensor networks, which are relatively expensive, vulnerable to damage, and time consuming to install. Systems based on wireless sensor networks should be both cost efficient and easy to install, scalable and adaptive to different type of structures. Acoustic emission techniques are an additional monitoring method to investigate the status of a bridge or some of its components. It has the potential to detect defects in terms of cracks propagating during the routine use of structures. However, acoustic emissions recording and analysis techniques need powerful algorithms to handle and reduce the immense amount of data generated. These algorithms are developed on the basis of neural network techniques and - regarding localization of defects - by array techniques. Sensors with low price are essential for such monitoring systems to be accepted. Although the development costs of such a system are relatively high, the target price for the entire monitoring system will be several thousands Euro, depending on the size of the structure and the number of sensors necessary to cover the most important parts of the structure. Micro-Electro-Mechanical-Systems and hybrid sensors form the heart of Motes (network nodes). The network combined multi-hop data transmission techniques with efficient data pre-processing in the nodes. Using this technique, monitoring of large structures in civil engineering becomes very efficient including the sensing of temperature, moisture, strain and other data continuously. In this paper, the basic principles of a wireless monitoring system equipped with MEMS sensors is presented along with a first prototype. The authors work on details of network configuration, power consumption, data acquisition and data aggregation, signal analysis and data reduction is presented.

An extensive program of full-scale ambient vibration testing has been conducted to measure the dynamic response of a 240 meter cable-stayed bridge - Gi-Lu Bridge in Nan-Tou County, Taiwan. A MEMS-based wireless sensor system and a traditional microcomputer-based system were used to collect and analyze ambient vibration data. A total of four bridge modal frequencies and associated mode shapes were identified for cables and the deck structure within the frequency range of 0~2Hz. The experimental data clearly indicated the occurrence of many closely spaced modal frequencies. Most of the deck modes were found to be associated with the cable modes, implying a considerable interaction between the deck and cables. The results of the ambient vibration survey were compared to modal frequencies and mode shapes computed using three-dimensional finite element modeling of the bridge. For most modes, the analytical and the experimental modal frequencies and mode shapes compare quite well. Based on the findings of this study, a linear elastic finite element model for deck structures and beam element with P-Delta effect for the cables appear to be capable of capturing much of the complex dynamic behavior of the bridge with good accuracy.

A novel sensor failure detection method is developed in this paper. Sensor failure considered in this paper can be any type of measurement error that is different from the true structural response. The sensors are divided into two groups, sensors that correctly measure the structural responses, are termed as reference sensors, and sensors that may fail to correctly measure the structural responses, are termed as uncertain sensors henceforth. A sensor error function, one for each uncertain sensor, is formulated to detect the corresponding uncertain sensor failure in real-time, using the measurements from reference sensors and the uncertain sensor being monitored. The sensor error function is derived using the indirect and direct approaches. In the indirect approach, the error function is obtained from the state space model in combination with the inverse model and interaction matrix formulation. The input term is eliminated from the error function by applying inverse model and the interaction matrix is applied to eliminate the state and all uncertain sensor measurement terms excepted the examined uncertain sensor from the error function. In the direct approach, the coe±cients of the error function can be directly calculated from the healthy measurement data from the examined uncertain sensor and all reference sensors without having to know the state-space model of the system. Thus the need to know the state-space model of the plant can be bypassed. The sensor failure detection formulations are investigated numerically using a four degree-of-freedom spring-mass-damper system and experimentally using a 4m long NASA 8-bay truss structure. It is shown by means of numerical and experimental results that the developed sensor failure formulations correctly detect the instants of sensor failure and can be implemented in real structural systems for sensor failure detection.

This paper introduces a heuristic methodology for designing multilayer feedforward neural networks to model the types of nonlinear functions common to many engineering mechanics applications. It is well known that a perfect way to determine the ideal architecture to initialize neural network training has not yet been established. This could be because this challenging issue can only be properly addressed by looking into the features of the function to be approximated and thus might be hard to tackle in a general sense. In this study, the authors do not presume to provide a universal method approximate an arbitrary function, rather the focus is given to modeling nonlinear hysteretic restoring forces, a significant domain function approximation problem. The governing physics and mathematics of nonlinear hysteretic dynamics as well as the strength of the sigmoidal basis function are exploited to determine both an efficient neural network architecture (e.g., the number of hidden nodes) as well as effective initial weight and bias values for those nodes. Training examples are presented to demonstrate and validate the proposed initial design methodology. Comparisons are made between the proposed methodology and the widely used Nguyen-Widrow Initialization. Future work is also identified.

Many damage detection and system identification approaches benefit from the availability of both acceleration and displacement measurements. This is particularly true in the case of suspected nonlinear behavior and permanent deformations. In civil and mechanical structural modeling accelerometers are most often used, however displacement sensors, such as non-contact optical techniques as well as GPS-based methods for civil structures are becoming more common. It is suggested, where possible, to exploit the inherent redundancy in the sensor information and combine the collocated acceleration and displacement measurements in a manner which yields highly accurate motion data. This circumvents problematic integration of accelerometer data that causes lowfrequency noise amplification, and potentially more problematic differentiation of displacement measurements which amplify high-frequency noise. Another common feature of displacement based sensing is that the high frequency resolution is limited, and often relatively low sampling rates are used. In contrast, accelerometers are often more accurate for higher frequencies and thus higher meaningful sampling rates are often available. The fusion of these two data types must therefore combine data sampled at different frequencies. A multi-rate Kalman filtering approach is proposed to solve this problem. In addition, a smoothing step is introduced to obtain improved accuracy in the displacement estimate when it is sampled at lower rates than the corresponding acceleration measurement. Through trials with simulated data the procedure's effectiveness is shown to be quite robust at a variety of noise levels and relative sample rates for this practical problem.

As carbon fiber-reinforced polymer (CFRP) laminates have been widely accepted as valuable materials for retrofitting civil infrastructure systems, an appropriate assessment of bonding conditions between host structures and CFRP laminates becomes a critical issue to guarantee the performance of CFRP strengthened structures. This study attempts to develop a continuous performance monitoring system for CFRP strengthened structures by autonomously inspecting the bonding conditions between the CFRP layers and the host structure. The uniqueness of this study is to develop a new concept and theoretical framework of nondestructive testing (NDT), in which debonding is detected "without using past baseline data." The proposed baseline-free damage diagnosis is achieved in two stages. In the first step, features sensitive to debonding of the CFPR layers but insensitive to loading conditions are extracted based on a concept referred to as a time reversal process. This time reversal process allows extracting damage-sensitive features without direct comparison with past baseline data. Then, a statistical damage classifier will be developed in the second step to make a decision regarding the bonding condition of the CFRP layers. The threshold necessary for decision making will be adaptively determined without predetermined threshold values. Monotonic and fatigue load tests of full-scale CFRP strengthened RC beams are conducted to demonstrate the potential of the proposed reference-free debonding monitoring system.

Base isolation systems have been used in buildings and bridges as protective systems against earthquakes, and the health monitoring of base isolators is of great importance. The ability to detect damages on-line, based on vibration data measured from the health monitoring system, will ensure the reliability and safety of base-isolated structures. When a base-isolated structure is subject to strong earthquakes, it is important to be able to determine the damage of isolators from the seismic response data either on-line or almost on-line. The problem is quite challenging because the restoring force of the base isolator is inelastic, and only a limited number of sensors can be installed in the structural health monitoring system, indicating that the response data may not be available at all degrees of freedom of the structure and that the external excitations may not be measured. In this paper, we propose a new data analysis method, referred to as the sequential nonlinear least-square estimation with unknown inputs and unknown outputs (SNLSE-UI-UO), for the real time on-line identification of structural damages, including the nonlinear base isolators. In this approach, only a limited number of response data are needed and the external excitations may not be measured. Analytical recursive solutions are derived and presented, which are not available in previous literature. The Bou-Wen model is used for the nonlinear base-isolation system, and the accuracy of the new approach is demonstrated using a base-isolated building. Simulation results demonstrate that the proposed approach is capable of tracking on-line the changes of structural parameters, such as the parameters of base isolators, leading to the identification of structural damages.

A new damage detection technique using the roughness profile of structural mode shape is developed in this study. In this method, the spatially distributed signal (e.g., displacements or mode shapes) of a damaged structure is treated as an engineering surface. Irregularity of the surface induced by damage in beam-type structure is then treated as the roughness of the surface. A simple algorithm is developed to extract the roughness profile from the surface. The location and severity of damage is then determined by a sudden change on the roughness profile. This method is then applied to the mode shapes of cracked and delaminated beams obtained analytically, from which the damage location and size are determined successfully. As verifications, the proposed method is further applied on the experimentally measured curvature mode shapes to detect damage in carbon/epoxy composite beams. The successful detection of crack and delamination damage in the composite beams demonstrates that the new technique developed in this study can be used efficiently and effectively in damage identification and health monitoring of beam-type structures.

This paper presents a variety of methodologies that are used to detect the location and amount of structural damage using dynamic measurements of the input and of the structural response. One approach (and its variations) starts from an identified first order model of a structural system and obtain estimation of the structure's mass, damping and stiffness matrices. For these approaches, both the full instrumentation option and the partial instrumentation option are presented. An alternative approach for the identification of the dynamic characteristics of the structure is based on Evolution Strategies. Once these dynamic characteristics have been determined, structural damage is assessed by comparing the undamaged and damaged estimation of such parameters. Both these methodologies are tested on simulated numerical results and their effectiveness in determining structural damage is evaluated.

The long-term reliability of a threshold corrosion sensor is demonstrated using data collected during two series of exposure tests. The sensors were embedded in concrete and interrogated in a wireless manner using inductive coupling. The frequency signature of the sensor changes after a steel sensing wire corrodes, providing a convenient and noninvasive technique for determining when a threshold amount of corrosion has occurred in a reinforced concrete structure. In the first series of exposure tests, the sensors were embedded in concrete prisms, which were exposed to a variety of temperature and moisture conditions over a six-month period. In the second series of tests, the sensors were embedded in reinforced concrete slabs. The slabs have been subjected to sustained loads and alternating wet and dry cycles for the past year. Data from both test series indicate that the threshold sensors are functioning as designed.

A structure is vulnerable if any small damage will trigger disproportionately large consequences, even leads to a cascade of failure events and progressive collapse. The structural vulnerability performance depends upon the properties, locations of damaged components and the way they are connected to the rest of the structure. In this paper, progressive failure analysis method is utilized for the vulnerability study of cable-stayed bridges. The goal of it is to identify various failure scenarios initiated from the sudden damage of some bridge components. Based on the analysis results, assuming that the behaviors of damaged components can be modeled using plastic hinges, the hybrid element model is introduced to derived the modified stiffness matrix considering both of the effects of bending moments and shear forces. Furthermore, the vulnerability index in terms of the nodal stiffness degradation can be analytically quantified, which could be regarded as an index to determine the bridge failure consequences, so that the vulnerability distribution graph for the whole bridge can be sketched to evaluate the bridge performances from vulnerability viewpoints. This quantitative approach is applicable for structural evaluation under unforeseen attack and illustrated on a typical long-span cable-stayed bridge.

Structural Health Monitoring (SHM), the technology to evaluate the health of the structure (such as earthquake resistance), is getting attention recently. The techniques for evaluating earthquake-proof performance in the building are key elements. However, existing techniques are not so practical as they require professional knowledge on structural engineering. The chaos analysis method, recently proposed by Prof. Okada is an attractive method to meet the need. The method evaluates earthquake-proof performance, using chaos analysis. It is clear that there is correlation between chaos theory and physical parameters. This paper proposes a modified version of chaos analysis to make the method stable and reliable. Findings in the course of this study are as follows. (1) The chaos degree rises when attenuation grows. (2) The chaos degree rises by widening the band of the frequency. (3) The ground motion with large power decides the chaos degree. (4) The chaos degree is not unique for the same structure depending on the inputs. These are due to the results of simulation and full-scale experiments. Using these findings, the computational method of the chaos degree without depending on the characteristics of inputs is established. It is a method using the system identification to estimate the parameter model of building from the result of the response due to any inputs. The influence of the input characteristic can be excluded by this method.

A vibration-based monitoring process is proposed that adopts a different viewpoint than modal-based methods currently practiced. In addition to the usual modal characterization, vibrations can also be described as traveling waves, which propagate in a continuous structure and are reflected and transmitted at a structural discontinuity. While modal responses depend on the global properties of the structure, the reflection and transmission characteristics depend only on the local properties of the structural damage. As a result, structural health monitoring based on wave vibration characteristics is more sensitive and reliable. In this paper, the wave based vibration approach is applied in monitoring both a cracked structural element and a cracked structural joint. Numerical examples are presented.

Concrete may the economical material available for buildings and civil structures due to various important its properties such as high compressive strength, wear resistance, abrasion resistance and durability. The most disadvantages of concrete structural elements are its cracks in flexure. Visual inspection is difficult and provides little detailed information in crack conditions. Recently, a new trend, called smart concrete or structure, has been emerged using various technologies for monitoring of crack conditions of concrete. A method designed to monitor or characterize the crack conditions in concrete beams in flexure using polymerbased composite sensors is conducted in the present work. The embedded polymer-based composite sensor shows a potential to evaluate the conditions of concrete's cracks in beams under flexural loading such as initial and critical crack conditions, using data acquisition system.

A method for impact and damage detection on a plate using strain responses from long continuous sensors and analysis by a neural network technique was presented and verified by numerical simulation. The response characteristics of continuous sensors, which are long ribbon-like sensors, were studied by simulation of wave propagation in a panel. The advantage of the continuous sensor is to improve damage detection by having a large coverage of sensors on the structure using a small number of channels of data acquisition. Strain responses from the continuous sensors were used to estimate the damage location using the neural network technique. Several numerical wave propagation simulation runs for a plate were carried out to train the neural network and verify the proposed method for damage localization. The identified damage locations agreed reasonably well with the exact damage locations. Overall, the approach presented is meant to simplify the instrumentation needed for damage detection by using continuous sensors, a small number of channels of data acquisition, and training a neural network to do the work of locating the damage source.

Recent research has shown that high frequency chaotic excitation and state space reconstruction may be used to identify incipient damage (loss of preload) in a bolted joint. In this study, a new experiment is undertaken with updated test equipment, including a piezostack actuator that allows for precise control of bolt preload. The excitation waveform is applied to a macro-fiber composite (MFC) patch that is bonded to the test structure and is sensed in an active manner using a second MFC patch. A novel prediction error algorithm, based on comparing filtered properties of the guided chaotic waves, is used to determine the damage state of a frame structure and is shown to be highly sensitive to small levels of bolt preload loss. The performance of the prediction error method is compared with standard structural health monitoring damage features that are based on time series analysis using auto-regressive (AR) models.

Target building is an 8-story steel encased reinforced concrete building which was constructed in 1998. In this structural health monitoring system, strong motion observation data is used and accelerometers were installed just after the completion of construction at 1st story, 2nd story, 5th story and 8th story. By use of system identification using ARX model, natural frequency, damping ratio and participation function are calculated, and concentrated equivalent story stiffness can be also determined by using Moore and Penrose generalized inverse matrix. From the identification results, natural frequency and concentrated equivalent story stiffness tend to decrease by the aging. Especially, just after the completion of construction and after a large earthquake, changes of natural frequency and concentrated equivalent story stiffness are very remarkable. From the point of amplitude dependence, natural frequency and concentrated equivalent story stiffness tend to change more by equivalent velocity of input energy than by peak ground acceleration. Analytical frame model is constructed from the structural design documents and concentrated equivalent story load-displacement relationships are obtained by carrying out push-over analysis. By the comparison between analytical and identified concentrated equivalent story stiffness, the structural conditions is estimated. From the identification results, a model using stick-slip elements is proposed. Natural frequency and story stiffness described by this model are consistent with identified results.

A health monitoring system based on the MATLAB Web Server is proposed and its prototype system is developed. In order to acquire the response data automatically, a simple sensor system was also developed. It transfers the data automatically through the Internet and is configurable through the network. The system is now under operation to identify the research needs and the right direction for evolution.

Structural condition assessment of highway bridges is traditionally performed by visual inspections or nondestructive evaluation techniques, which are either slow, unreliable or detects only local flaws. Instrumentation of bridges with accelerometers and other sensors, however, can provide real-time data useful for monitoring the global structural conditions of the bridges due to ambient and forced excitations. This paper reports a video-assisted approach for structural health monitoring of highway bridges, with results from field tests and subsequent offline parameter identification. The field tests were performed on a short-span instrumented bridge. Videos of vehicles passing by were captured, synchronized with data recordings from the accelerometers. For short-span highway bridges, vibration is predominantly due to traffic excitation. A stochastic model of traffic excitation on bridges is developed assuming that vehicles traversing a bridge (modeled as an elastic beam) form a sequence of Poisson process moving loads and that the contact force of a vehicle on the bridge deck can be converted to equivalent dynamic loads at the nodes of the beam elements. Basic information of vehicle types, arrival times and speeds are extracted from video images to develop a physics-based simulation model of the traffic excitation. This modeling approach aims at circumventing a difficulty in the system identification of bridge structural parameters. Current practice of system identification of bridge parameters is often based on the measured response (or system output) only, and knowledge of the input (traffic excitation) is either unknown or assumed, making it difficult to obtain an accurate assessment of the state of the bridge structures. Our model reveals that traffic excitation on bridges is spatially correlated, an important feature that is usually incorrectly ignored in most output-only methods. A recursive Bayesian filtering is formulated to monitor the evolution of the state of the bridge. The effectiveness and viability of this video-assisted approach are demonstrated by the field results.

Novel designs of skin friction and heat flux sensors have been developed based on advanced materials and processing techniques. These sensors produce dynamic, time-resolved, direct measurements of skin friction and heat flux, especially tailored towards turbulent flows. The skin friction sensors use ionic polymer transducers, which contain no moving parts, directly measure shear, and can be surface mounted with minimal flow intrusion. The sensors exhibit measurement accuracy in fluctuating shear on the order of 4.92% over a range of stresses of +/- 3 Pa and signal-to-noise-ratio on the order of 60 dB. The frequency response of the sensor is on the order of 10 kHz. An approach for automatic recalibration and error compensation based on changes of impedance has been developed. This process allows in-situ recalibration of the sensors under varying temperature conditions. The heat flux sensors are made with thin-film deposition which allows fine arrays to be created. The measured Seebeck coefficient (temperature sensitivity) of the deposited metals is 23.5 μV/^oC, which closely matches that of bulk wire.

A model for piezoelectric vibration energy harvesting with a piezoelectric cantilever beam is presented. The model incorporates expressions for variable geometry, tip mass, and material constants, and allows the parameterized determination of the voltage and power produced over a purely resistive load. The model is of a lumped-element form, with the base excitation acceleration and voltage representing the effort variables, and the tip velocity and electrical current representing the flow variables. Subsequent to the model's derivation, experimental results are presented and demonstrate the accuracy of the model. As peak power output for existing vibration configurations is typically of interest, several simple optimization studies are then performed on a simple generator configuration to demonstrate the effects of several of the driving geometric and material parameters.

To measure component-level structural responses due to external loading, strain sensors can provide detailed information pertaining to localized structural behavior. Although current metal foil strain sensors are capable of measuring strain deformations, they suffer from disadvantages including long-term performance issues when deployed in the field environment. This paper presents a novel carbon-nanotube polymer composite thin film that can be tailored for specific strain sensing properties. Beginning at the nano-scale, molecular manipulation of single-walled carbon nanotubes (SWNT) is performed to control chemical fabrication parameters as a means of establishing a relationship with macroscale bulk sensor properties. This novel strain sensor is fabricated using the Layer-by-Layer (LbL) self-assembly process. A rigorous experimental methodology is laid out to subject a variety of thin films to tensile-compressive cyclic loading. In particular, SWNT concentration, polyelectrolyte concentration, and film thickness are varied during the fabrication process to produce a variety of strain sensors. This study correlates fabrication parameters with bulk strain sensor properties; sensor properties including sensitivity (gauge factor), linearity, and hysteresis, are explored.

The design and performance of an electrical instrument that would be useful in estimating the moisture content (mc) of agricultural products such as grain and nuts nondestructively and rapidly is described here. The instrument, here after called the impedance meter, determines the capacitance and phase angle of a sample of the produce (about 100 g), filling the space between two parallel-plate electrodes, at two frequencies 1 and 5 MHz. The measured values were used in a semi-empirical equation to obtain the mc of the sample. In this paper, capacitance and phase angle were determined for in-shell peanuts in the moisture range between 6 and 25% by the impedance meter, and their moisture contents were calculated. The calculated values were compared with the mc values obtained by the standard air-oven method. The estimated values were in good agreement with the standard values. This method is applicable to produce such as corn, wheat and pecans also.

In this paper we demonstrate an integration approach for making high-density microfluidic systems. A complex microfluidic system including both sensors and actuators was constructed on silicon chip. Electrically addressable bubble-based valves were used to regulate the fluid flow. A number of electrolytic bubble sensors were placed in parallel channels (sensing limb) connected with the main flow channel for measurements of open channel pressure in real-time. All the fluidic components were made using a single microfabrication process. The pressure dependence of the bubble-based sensor was systematically investigated by applying an inlet pressure ranging from 101 kPa to 133 kPa, while keeping the outlet pressure at atmosphere. The results show that open flow pressure can be accurately measured using the bubble-based sensor located in an adjacent sensing limb. The bubble-shrinking rate can also serve as a measurable parameter for the pressure in main fluidic channel. The experimental data validated with 3D numerical simulation results. The electrolytic bubble-based approach provides an ability to integrate a large number of microfluidic components on a monolithic lab-chip.

Fault detection and diagnosis (FDD) is applied to mechanical-pneumatic systems to perform intelligent diagnosis of various faults in the system by utilizing the sensory information commonly found in typical systems, such as pressures and flow rates. In this paper, we present research results on intelligent FDD and characterization of MEMS flow sensor. Vectorized maps are created and calibrated for the purpose of intelligent FDD. In addition, maps of N-manifold can be used for redundancy in diagnosis to improve the accuracy and reliability of the methodology. Such redundant vectorized maps provide for explanation of physical significance of the behavior of the system and the formation or detection of faults. As a result, both physical-based and signal-based intelligent fault detection and diagnosis techniques and methodology can be applied for various types of applications. Experimental results suggest that intuitive choices of parameters and features, based on the understanding of physics of the mechanical-pneumatic system, can be applied with success to intelligent detection and diagnosis of faults. Furthermore, with miniaturization, sensors can be readily made and integrated for intelligent diagnosis. Characterization and modeling of such innovative sensor designs are presented. Using new smart multi-function, telemetric, and integrated sensors as "intelligent nodes" in systems will provide necessary sensory information (e.g., pressure, flow, and temperature) for the next-generation diagnosis. The characterization and study of MEMS sensor include: correlation of flow and deflection of sensory element, analysis and modeling, vibration characteristics, fatigue tests, backflow characterization,... etc. Specifically, the results of fatigue tests provide information and feedback for the design and fabrication of the MEMS sensors; more importantly, long fatigue life is essential for the flow sensors to sustain as a transducer. Results of the findings are presented.

In recent years, there are many issues involving safety on old bridges, aircrafts and other structures, which threaten the lives of the people using those structures, as well as the structures themselves. To prevent future failure, various measures are being taken. Structure rehabilitations with carbon fiber reinforced composite patches have been adopted and demonstrated to be an excellent way to enhance/repair the structures and prolong the service life. However, there are still many problems residing in this kind of technology that remain unsolved, for example, the failure of the interface between composite repair patches and their host structures. This is a critical issue that must be addressed in order to show the viability of composite patches. In order to study debond occurring between composite repair patches and their host structures, a structure health monitoring scheme was demonstrated on a concrete bridge model in the laboratory. The system is based on active sensing with diagnostic lamb waves, in which piezoelectric transducers are used as both sensors and actuators. In the test, six SMART Layers, each having eight piezoelectirc transducers, were integrated with two composite repair strips on the deck slab of the concrete bridge model. For the three diagnostic layers with each composite repair patch, two layers were bonded on the top surface of the patch, and the other is embedded at the interface between the composite repair patch and the deck slab of the concrete bridge model. The loading procedure of the test included three phases. First, the bridge model was preloaded to initiate cracks on the deck slabs and the repair patches were then implemented. Second, the load was raised to reach the shear capacity of the girders of the bridge model and then the repair patches were implemented on those girders. Lastly, the structure was loaded to damage the deck slabs. During the test, the initiation and development of debond between composite repair patches and deck slabs were clearly revealed by the active sensing system. It was demonstrated that the active sensing system employed is prompt, robust and a precise technique to monitor the debond process of the composite repair patches for structural rehabilitation. Besides the study of the mechanism of debond between repair patch and host structures, an on line health monitoring system can give the user an indication of the structural health status and alert technicians when it approaches the failure capacity.

This paper presents the design, fabrication and testing results of a Fabry-Perot Micro-Opto-Mechanical Device (FPMOD) used as a vibration sensor. The un-cooled high-temperature operational capability of the FPMOD provides a viable low-cost alternative to sensors that require environmentally controlled packages to operate at high temperature. The FPMOD is a passive MEMS device that consists of a micromachined cavity formed between a substrate and a top thin film structure in the form of a cantilever beam. When affixed to a vibrating surface, the amplitude and frequency of vibration are determined by illuminating the FPMOD with a monochromatic light source and analyzing the back reflected light to determine the deflection of the beam with respect to the substrate. Given the Fabry-Perot geometry, a mechanical transfer function is calculated to permit the substrate motion to be determined from the relative motion of the beam with respect to the substrate. Because the thin film cantilever beam and the substrate are approximately parallel, this convenient two-mirror cavity arrangement needs no alignment, no reference arm, and no sophisticated stabilization techniques. The small size of the FPMOD (85-175μm), the choice of materials in which it can be manufactured (Silicon Nitride and Silicon Carbide), and its simple construction make it ideal for harsh high-temperature applications. Relative displacements in the sub-nanometer range have been measured and close agreement was found between the measured sensor frequency response and the theoretical predictions based on analytical models.

Damage identification using the information of natural frequency shift (before and after damage occurrence) has been studied extensively. For a closed-loop system, the sensitivity of natural frequency shift to structural parameter variation is related to both the closed-loop eigenvalues and eigenvectors. With multiple control inputs/actuators, one may have the freedom of assigning closed-loop eigenvalues as well as eigenvectors by using singular value decomposition (SVD) based eigenstructure assignment technique. In this research, we formulate an algorithm to optimally assign the eigenvalues and eigenvectors that yield enhanced closed-loop natural frequency sensitivity, which leads to improved damage detection performance. Meanwhile, by activating different combinations of the multiple actuators in such system, we may develop a series of closed-loop controls for the same monitored structure for sensitivity enhancement. These multiple closed-loop controls utilizing the same set of hardware will result in a much enlarged dataset of frequency-shift information for damage identification. This proposed methodology can simultaneously solve the two main issues in frequency shift based damage identification: low sensitivity and deficiency of measurement data. Numerical studies on a benchmark beam structure demonstrate that the sensitivity of natural frequency changes to small stiffness reduction due to damage can be significantly enhanced, whereas the optimal assignment of eigenvectors plays a very important role. The effect of measurement noise on the performance of the proposed damage detection method is evaluated. Our analyses show that, by using this proposed approach, the location and severity of the structural damage can be successfully identified with much improved performance even in the presence of significant measurement noise.

The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T² analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.

This paper studies a design approach that yields robust vibratory MEMS gyroscopes. The design is based on multiple drive-mode resonators with incrementally spaced frequencies, distributed symmetrically around the center of a supporting frame. These resonators are structurally constrained in the tangential direction with respect to the supporting frame. In the presence of an angular rotation rate about the z-axis, a harmonic Coriolis force is induced on each proof mass. These force vectors lie in the tangential direction, generating a resultant torque on the supporting frame. The net Coriolis torque excites the supporting frame into torsional oscillation about the z-axis, which is capacitively detected to generate angular rate measurement. Two batches of prototypes have been fabricated using in-house single crystal silicon on insulator (SCS-SOI) bulk-micromachining and EFAB^TM process commercially available from Microfabrica. Wideband drive operation was demonstrated in SOI devices. EFAB process yielded 850 Hz devices with quality factor 250 in air (bandwidth 3 Hz) and 850 in vacuum. Increase of temperature from 25^o to 125^oC shifts the resonant frequency down by roughly bandwidth, while quality factor drops by 8%. Parasitics model associated with EFAB consists of only a lumped capacitor and is simpler than two-parametric parasitics circuit in SOI devices. Nonlinear parametric excitation of motion at resonant frequency by super-harmonic AC voltage was experimentally characterized. This actuation method provides high amplitude of motion and separates motion from parasitics in frequency domain. The actuation method can potentially further improve the bandwidth and gain characteristics of distributed mass gyroscope.

In this paper, a sensing methodology is developed and experimentally investigated to detect and characterize damages, which are essentially accompanied by changes in the micro/macroscopic condition of surface contact. The proposed technique is developed mainly for early detection of loose bolted joints, but also may be applicable to kissing bonds in adhesive joints and breathing cracks under the operational condition. The presented sensing system consists of PZT patches attached on the structural surface, one of which acts as a transmitter of high frequency harmonic wave. The incident harmonic wave is scattered by the contact surfaces which potentially involve damages, and received by the other patches. When the structure is subjected to the operational or ambient load at low frequencies, it vibrates, and the inherent damages may introduce a nonlinear effect to the vibro-acoustic dynamics that induces an interaction between the low frequency structural vibration and the high frequency transmitted wave. This nonlinearity is observed as the amplitude and phase modulation of the received wave due to the changes in the scattering characteristics synchronous with the structural vibration. By investigating the relationship between the modulations and the structural vibration, the nonlinear characteristics of the damages can be specified. Experiments using a beam with a bolted joint are conducted for illustrative purpose.

In this paper we present the design and control algorithms for novel electro-rheological fluid based torque generation elements that will be used to drive the joint of a new type of portable and controllable Active Knee Rehabilitation Orthotic Device (AKROD) for gait retraining in stroke patients. The AKROD is composed of straps and rigid components for attachment to the leg, with a central hinge mechanism where a gear system is connected. The key features of AKROD include: a compact, lightweight design with highly tunable torque capabilities through a variable damper component, full portability with on board power, control circuitry, and sensors (encoder and torque), and real-time capabilities for closed loop computer control for optimizing gait retraining. The variable damper component is achieved through an electro-rheological fluid (ERF) element that connects to the output of the gear system. Using the electrically controlled rheological properties of ERFs, compact brakes capable of supplying high resistive and controllable torques, are developed. A preliminary prototype for AKROD v.2 has been developed and tested in our laboratory. AKROD's v.2 ERF resistive actuator was tested in laboratory experiments using our custom made ERF Testing Apparatus (ETA). ETA provides a computer controlled environment to test ERF brakes and actuators in various conditions and scenarios including emulating the interaction between human muscles involved with the knee and AKROD's ERF actuators / brakes. In our preliminary results, AKROD's ERF resistive actuator was tested in closed loop torque control experiments. A hybrid (non-linear, adaptive) Proportional-Integral (PI) torque controller was implemented to achieve this goal.

The monitoring of adhesively-bonded joints through the use of ultrasonic guided waves is the general topic of this paper. Specifically, composite-to-composite joints representative of the wing skin-to-spar bonds of Unmanned Aerial Vehicles (UAVs) are examined. This research is the first step towards the development of an on-board structural health monitoring system for UAV wings based on integrated ultrasonic sensors. The study investigates two different lay-ups for the wing skin and two different types of bond defects, namely poorly-cured adhesive and disbonded interfaces. The guided wave propagation problem is studied numerically by a semi-analytical finite element method that accounts for viscoelastic damping, and experimentally by utilizing macro fiber composite (MFC) transducers which are inexpensive, flexible, highly robust, and viable candidates for application in on-board monitoring systems. Based upon change in energy transmission, the presence of damage is successfully identified through features extracted in both the time domain and discrete wavelet transform domain. A unique "passive" version of the diagnostic system is also demonstrated experimentally, whereby MFC sensors are utilized for detecting and locating simulated active damage in an aluminum plate. By exploiting the directivity behavior of MFC sensors, a damage location algorithm which is independent of wave speed is developed. Application of this approach in CFRP components may alleviate difficulties associated with damage location in highly anisotropic systems.

Lamb waves in plates and in cylindrical pipes have been the subject of extensive study, largely because they propagate great distances with little attenuation, and can therefore be used to detect flaws. In this paper we report finite element simulations and experimental studies of Lamb waves in steel bridge girder geometries. In our studies the Lamb waves are generated by PZT wafer-type transducers mounted on the girder web, driven by a windowed sinusoidal pulse; the pulse center frequency is chosen to yield a frequency-thickness product of roughly 1 MHz-mm, at which the group velocities of the S0 and A0 waves are well separated, and at which waves in higher modes are theoretically absent. Transient dynamic finite element simulations, both in 2D and in 3D, were performed using FEMLAB and ABAQUS. The simulations show that transmission at the web-flange joint creates guided waves in the flanges that travel at different velocities from the Lamb waves in the web, and that reflection at the web-flange joint creates a largely straight-crested wavefront for the Lamb waves in the web remote from the source. Simulation studies also illustrate the acoustic influence of plate girder transverse stiffeners, which is observed to be relatively small. A welded steel plate girder laboratory specimen was fabricated with proportions typical of highway bridge members, at approximately half-scale. The web height is 920 mm and thickness is 3.2 mm, for a representative height-thickness ratio of 288; the flange width is 100 mm and thickness is 6.4 mm, for a representative width-thickness ratio of 16. Small PZT transducers, roughly 6.4 x 6.4 x 0.6 mm, excited at less than 10 V, produce ample signals. We compare simulation results and experimental measurements for Lamb wave illumination of the plate girder segment. We also discuss the detection of cracks, simulated experimentally by saw cuts of varying dimensions in the laboratory girder specimen.

This paper investigates if cylindrical guided waves can be effectively used for pipe wall defect detection in soil-embedded pipes. For this purpose guided waves are propagated through pipes that are buried in the soil by placing transmitters on one end of the pipes and the receivers on the other end. Received signals for both defect-free and defective pipes are subjected to wavelet transforms. It is found that when a Continuous Wavelet Transform (CWT) based algorithm is applied to analyze the received signals then it is very easy to make distinction between damaged and undamaged pipes. To investigate whether embedding the pipe in the soil makes it more difficult to detect the pipe wall defects, the same set of defective and defect-free pipes are analyzed before and after burying them in the soil. In both cases the defects are easily detected after analyzing the wavelet transformed signals. Interestingly it can be detected more easily for the buried pipes because the difference between the received signal strengths from defect-free and defective pipes is found to be greater for the buried pipes. For soil embedded pipes the ultrasonic energy scattered by the defect is absorbed by the surrounding soil making the energy reaching the receiver significantly weaker than that for the defect-free soil embedded pipe.

Recent train accidents and associated direct and indirect costs including cost of repair of equipment and infrastructure as well as delay and death/injury costs, have reaffirmed the need for developing rail defect detection systems more effective than those used today. In fact, rail defect detection has been identified as a priority area in the U.S. Federal Railroad Administration 5-year R&D plan. This paper proposes an unsupervised learning algorithm for defect detection in rails. The algorithm is used in a non-contact inspection system that is targeted to the detection of transverse-type cracks in the rail head (including transverse fissures and detail fractures), notoriously the most dangerous flaws in rails. The system uses ultrasonic guided waves that are generated by a pulsed laser and are detected by air-coupled sensors positioned as far away as 76 mm (3") from the top of rail head. The inspection ranges is at least 500 mm (20") for surface head cracks as shallow as 1 mm. Fast data output is achieved by processing the ultrasonic defect signatures by Wavelet Transform algorithms. The features extracted after wavelet processing are analyzed by a learning algorithm based on novelty detection. This algorithm attempts to detect the presence of damage despite the normal variations in ultrasonic signal features that may be found in a field test.

A new statistical method is proposed to quantify the significance of changes in mean frequencies of individual modal vibrations of measured structural response data. In this new method, called the modal distribution method, a power spectrum of measured structural response resulting from a Fourier transform is interpreted as being a series of independent modal responses. Each modal response is isolated over a frequency range and treated as a statistical distribution. The first two spectral moments are calculated directly from each of these distributions. A combined statistical comparison of the means of modal frequencies in separate data windows is used to produce a quantitative significance level of the observed differences between power spectra. Significant changes between these spectra indicate a change in the underlying process, such as damage detection in a structural health monitoring application. The method is general and may find a broad variety of applications, but it seems particularly well suited for structural health monitoring applications because the excitation is not required as input. An example is presented based on measured full-scale acceleration data from a drilling riser. To validate the new method, a power spectrum resulting from the field data is idealized to a target spectrum with known mean and variance of each mode. The idealized spectrum is subtly changed and new acceleration time-histories are simulated from these modified spectra to asses the effectiveness of the new method. The modal distribution method is found to be very effective at detecting subtle changes of mean modal frequencies.

Cable-stayed bridges are a recent development in bridge structural design in which the cables meet the bridge deck at an acute angle rather than perpendicularly. Some early cable-stayed bridges have exhibited large amplitude stay cable oscillations. One such bridge, the Fred Hartman Bridge across the Houston Ship Channel in Texas displayed two different modes of vibration: a local mode involving independent motion of individual cables and a global mode, in which the cables vibrated collectively under certain wind and rain conditions. This abrupt shift in mode as a function of a change in environmental parameters suggests chaotic behavior. Analysis of the probability density function of maximum accelerations of the cables typically showed a fractal power law distribution at lower values, but also some sharp changes in the tails. The Lorentz Map plots of the data also indicated two regimes: a dissipative one at lower acceleration values and chaotic behavior beyond a critical acceleration value. The plots also imply that the chaotic system is nearly one-dimensional. The working hypothesis is that steady winds impose additional stresses on the stay cables that push them over the boundary into the chaotic regime where random impulses from falling raindrops become amplified into cable oscillations.

The aging Civil Infrastructure System (CIS) in the United States has prompted the need for more effective structural health monitoring (SHM) techniques. Global Positioning Systems (GPS) have shown great promise for SHM, as they allow the total displacement of a structure to be measured, unlike other traditional sensors (i.e. accelerometers and strain gages). However, past research efforts have shown GPS to suffer from the effects of multipath interference, greatly reducing its accuracy in urban areas. In this study, a testing program was developed in which a controlled multipath source was introduced into a GPS network to allow for the characterization and removal of this phenomenon. In addition, the GPS performance was benchmarked against two more widely accepted sensor technologies: a terrestrial positioning system (TPS) and an accelerometer, to demonstrate its utility for monitoring CIS.

Many Structural Health Monitoring (SHM) methods have been proposed for the purposes of reducing maintenance costs and/or assuring performance of civil structures. The objective of this research is to propose a damage detection system that can obtain the detailed damage information by use of the minimum number of sensors. The proposed system minimizes the possibility of incorrect judgments. Modal frequencies of a structure are used for pattern recognition in the proposed method. Changes in multiple natural frequencies can be correlated to the spatial information of the location of damaged stories. Typically only two vibration sensors, one on the roof and the other on the ground, detecting a single input and a single output for the structure are needed to determine modal frequencies. Out of many pattern recognition tools, we propose to use the Support Vector Machine (SVM). This technique has been found effective. Our previous studies demonstrated that the proposed damage detection method worked well for simple models such as shear structures and bending structures. However, real buildings have various boundary conditions at their supports. In this study, the SVM technique was applied to damage detection of structures with various boundary conditions. The feature vectors for SVMs are generated based on the model of a structure. Then locations of structural damage are detected by inputting the measured structural vibration data into the SVMs. From simulation, it was found that the influence of the change in boundary conditions on the lower modes is larger. We performed experimental studies on damage detection of power distribution poles that had overhead wires. We proposed a method for determining the boundary conditions of the poles and verified this method based on measured vibration data. We demonstrated the effectiveness of the proposed method in detecting damage in the poles.

This paper presents a method for structural health monitoring using acceleration measurements. In a previous study a method for detecting, locating, and quantifying structural damages has been developed by directly using the time domain structural vibration measurements. However, only displacement and velocity measurements were used in that study. In this paper, acceleration measurements are used as feedback. Because it is more practical to measure acceleration using accelerometers, it is preferable to use acceleration rather than displacement and velocity measurements for the purpose of structural damage detection and assessment. However, using acceleration measurements is more difficult since the effects of different damages can not be decoupled completely as in the cases of displacement and velocity measurements. One approach of circumventing this difficulty is presented and it involves increasing the order of time derivatives of the linear system. The effectiveness of the proposed method using acceleration feedback is evaluated with illustrative examples of a three and an eight-story model. Results obtained are found to be comparable with results from simulations using displacement measurements as feedback.

The response of a structure is usually modified when the structure is damaged, especially in the vicinity of the damaged zones. Such local perturbations are generally very small but they can be detected using wavelet transform techniques. To this end, a distributed two-dimensional Continuous Wavelet Transform (2D CWT) algorithm is proposed that can use data from discrete sets of nodes and provide spatially continuous variation in the structural response parameters that are used to monitor structural degradation. Combined with an embedded sensor network to provide nodal response signals, this algorithm has potential for Structural Health Monitoring (SHM). The advantageous features of this algorithm are its reliance on local data, its ability to yield spatially continuous information, and its limited communication and computation requirements.

To develop a promising hybrid structural health monitoring system, which enables to detect damage by the dynamic response of the entire structure and more accurately locate damage with denser sensor array, a combined use of mechanical vibration and electro-mechanical impedance is proposed. For the verification of the proposed healthmonitoring scheme, a series of damage scenarios are designed to simulate various situations at which the connection joints can experience during their service life. The obtained experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the connecting states and the target damage locations. From the analysis, it is shown that the proposed hybrid health monitoring system is successful for acquiring global and local damage information on the structural joints; hence, its effectiveness is verified.

In this paper, packaged fibre Bragg grating (PFBG) sensors were fabricated by embedding them in 70mm x 10mm x 0.3mm carbon-fibre composites which were then surface-bonded to an aluminium beam and a steel I-beam to investigate their strain monitoring capability. Initially, the response of these packaged sensors under tensile loading was compared to bare FBGs and electrical strain gauges located in the vicinity. The effective calibration constant/ coefficient of the PFBG sensor was also compared with the non-packaged version. These PFBG sensors were then attached to an I-section steel beam to monitor their response under flexural loading conditions. These realistic structures provide a platform to assess the potential and reliability of the PFBG sensors when used in harsh environment. The results obtained in this study gave clear experimental evidence of the difference in performance between the coated and uncoated PFBG fabricated for the study. In another experimental set-up, bare FBG and POF vibration sensors were surface-bonded to the side-surface of a CFRPwrapped reinforced concrete beam which was then subjected to cyclic loading to assess their long-term survivability. Plain plastic optical fibre (POF) sensors were also attached to the side of the 2-meter concrete beam to monitor the progression of cracks developed during the cyclic loading. The results showed excellent long-term survivability by the FBG and POF vibration sensors and provided evidence of the potential of the plain POF sensor to detect and monitor the propagation of the crack developed during the test.

Thanking to its distinguishing advantages including wavelength multiplexing capability, miniature size, high sensitivity, immunity from electro-magnetic interference and etc, the fiber Bragg grating (FBG) sensing technologies are regarded as a competent candidate for the bridge long-term health monitoring. According to the shifted Bragg wavelength of the light reflected by a fiber grating, the FBG sensors can accurately measure various physical properties such as strain, temperature, displacement, acceleration and corrosion. One special advantage of the FBG sensing technology is that only one demodulation device is required to acquire various physical properties simultaneously. Compared with the bridge health monitoring system using conventional sensors, this advantage makes the quasi-distributed sensing possible and data transmission more convenient because many FBG sensors can be connected in series by a single fiber. In this paper, an integrated FBG sensing system is presented for monitoring the physical state of a real bridge, the main-navigation channel cable-stayed bridge of the Donghai Bridge. The strain variation of two selected sections in the construction stage and during the load trial test are continuously monitored. The results of this study will supply a good guidance for the use of FBG sensors on the health monitoring of real bridges. Finally, the paper present the design and fabrication of an accelerometer based on the FBG technology for structure vibration monitoring.

Various monitoring sensors have been used for the monitoring and damage prediction of structures. Piezoelectric and optical fiber sensor that are required housing for the field applications are used widely. The voltage change of piezoelectric for the steel girder is used for damage prediction. The inspection and monitoring for safety of crane is not easy because it is located in high level and the operation should be stop for the inspection. The constant input load by moving the crane girder with constant speed was used instead of ambient vibration. In this test, wireless monitoring system using LAN is tried for the long distance measurement. The objective of this paper is to present the dynamic measurement results to identify the potential damage of steel beam using piezoelectric sensor. Cantilever beams, a simply supported beam with bolted splice, and actual crane girder have been chosen for the test. FFT method was used for the damage identification. This output-only dynamic test is likely applied to the top crane to monitor the damage.

Critical civil infrastructure systems such as bridges, high rises, dams, nuclear power plants and pipelines present a major investment and the health of the United States' economy and the lifestyle of its citizens both depend on their safety and security. The challenge for engineers is to maintain the safety and security of these large structures in the face of terrorism threats, natural disasters and long-term deterioration, as well as to meet the demands of emergency response times. With the significant negative impact that these threats can have on the structural environment, health monitoring of civil infrastructure holds promise as a way to provide information for near real-time condition assessment of the structure's safety and security. This information can be used to assess the integrity of the structure for post-earthquake and terrorist attacks rescue and recovery, and to safely and rapidly remove the debris and to temporary shore specific structural elements. This information can also be used for identification of incipient damage in structures experiencing long-term deterioration. However, one of the major obstacles preventing sensor-based monitoring is the lack of reliable, easy-to-install, cost-effective and harsh environment resistant sensors that can be densely embedded into large-scale civil infrastructure systems. Nanotechnology and MEMS-based systems which have matured in recent years represent an innovative solution to current damage detection systems, leading to wireless, inexpensive, durable, compact, and high-density information collection. In this paper, ongoing research activities at Alabama A&M University (AAMU) Center for Transportation Infrastructure Safety and Security on the application of nanotechnology and MEMS to Civil Infrastructure for health monitoring will presented. To date, research showed that nanotechnology and MEMS-based systems can be used to wirelessly detect and monitor different damage mechanisms in concrete structures as well as monitor critical structures' stability during floods and barge impact. However, some technical issues that needs to be addressed before full implementation of these new systems and will also be discussed in this paper.

Cement-based composites filled with carbon black of 120nm size were prepared, the composites contained 15% amount of CB was in the percolation threshold, and it showed good strain sensing ability, the resistivity of composites changed linearly with applied strain, suggesting that this kind of composites was a promising candidate for strain sensor used in concrete structures. Water ratio has obviously influences on the initial resistivity of the composites. Epoxy encapsulated was found a practicable way to insulate the composites from water, and make the resistance measurement practicable. Strain senser was maken with the composite contained 15% amount of CB, and applied in concrete column for strain monitoring, the results showed that the strain of the concrete column could be monitored by this kind of sensor.

Nondestructive damage sensing and load transfer mechanisms of carbon nanotube (CNT), nanofiber (CNF), and Ni nanowire strands/epoxy composites were investigated using electro-micromechanical technique. Electrospun PVDF nanofiber was also prepared as a piezoelectric sensor. High volume% CNT/epoxy composites showed significantly higher tensile properties than neat and low volume% CNT/epoxy composites. CNF /epoxy composites with smaller aspect ratio showed higher apparent modulus due to high volume content in case of shorter aspect ratio. Using Ni nanowire strands/silicone composites with different content, load sensing response of electrical contact resistivity was investigated under tensile and compression condition. The mechanical properties of Ni nanowire strands with different type and content/epoxy composites were indirectly measured apparent modulus using uniformed cyclic loading and electro-pullout test. CNT or Ni nanowire strands/epoxy composites showed humidity and temperature sensing within limited ranges, 20 vol% reinforcement. Thermal treated electrospun PVDF nanofiber showed higher mechanical properties than the untreated case due to increased crystallization, whereas load sensing decreased in heat treated case. Electrospun PVDF nanofiber web also responded the sensing effect on humidity and temperature. Nanocomposites using CNT, CNF, Ni nanowire strands, and electrospun PVDF nanofiber web can be applicable practically for multifunctional applications nondestructively.

In this study, measurements from low-impact velocity experiments and surface mounted optical fiber Bragg grating (FBG) sensors were used to obtain detailed information pertaining to damage progression in two-dimensional laminate woven composites. The woven composites were subjected to multiple strikes at 2m/s until perforation occurred, and the impactor position and acceleration were monitored throughout each event. From these measurements, we obtained dissipated energies and contact forces. The FBG sensors were surface mounted at different critical locations near penetration-induced damaged regions. These FBG sensors were used to obtain initial residual strains and axial and transverse strains that correspond to matrix cracking and delamination. The transmission and the reflection spectra were continuously monitored throughout the loading cycles. They were used, in combination with the peak contact forces, to delineate repeatable sensor responses corresponding to material failure. From the FBG spectra, fiber and matrix damage were separated by an analysis based on the behavior of individual Bragg peaks as a function of evolving and repeated impact loads. This provided an independent feedback on the integrity of the Bragg gratings. Thus, potential sources of error such as sensor debonding were eliminated from the strain data throughout the measurements. A comparison by number of impact strikes and dissipated energies corresponding to material perforation indicates that these measurements can provide accurate failure strains.

Color marker employed in conjunction with space robots for on-orbit assembly has been developed. The marker consists of three printed discs with different colors. When used for on-orbit assembly, markers will be attached at the proximity of the connection mechanisms of assembly segment for large space structures. The distance and attitude of the segments can be measured by the positional relationship between the color discs of the marker. From the evaluation test, it has been verified that the performance of the measuring system with the color markers can meet the requirement of the space robot arms.

Recently, a damage detection method for nonlinear systems using model updating has been developed by the authors. The method uses an augmented linear model of the system, which is determined from the functional form of the nonlinearities and a nonlinear discrete model of the system. The modal properties of the augmented system after the onset of damage are extracted from the system using a modal analysis technique that uses known but not prescribed forcing. Minimum Rank Perturbation Theory was generalized so that damage location and extent could be determined using the augmented modal properties. The method was demonstrated previously for cubic springs and Coulomb friction nonlinearities. In this work, the methodology is extended to handle large systems where only the first few of the augmented eigenvectors are known. The methodology capitalizes on the ability to create multiple augmentations for a single nonlinear system. Cubic spring nonlinearities are explored within a nonlinear 3-bay truss structure for various damage scenarios simulated numerically.

Crack detection with piezoelectric wafer active sensors (PWAS) is emerging as an effective and powerful technique in structural health monitoring (SHM). Modeling and simulation of PWAS and host structure play an important role in the SHM applications with PWAS. For decades finite element method has been extensively applied in the analysis of piezoelectric materials and structures. The advantage of finite element analysis over analytical solutions is that stress and electrical field measurements of complex geometries, and their variations throughout the device, are more readily calculated. FEM allows calculation of the stress and electric field distributions under static loads and under any applied electrical frequency, and so the effect of device geometry can be assessed and optimized without the need to manufacture and test numerous devices. Coupled field analysis taking both mechanical motions and electrical characteristics into account should all be employed to provide a systemic overview of the piezoelectric sensors/actuators (even arrays of them) and the host structures. This use of PWAS for SHM has followed two main paths: (a). Wave propagation (b). Electromechanical impedance; Previous research has shown that PWAS can detect damage using wave reflections, changes in wave signature, or changes in the electromechanical (E/M) impedance spectrum. The primary goal of this paper is to investigate the use of finite element method (FEM) to simulate various SHM methods with PWAS. For the simulation of Electro-mechanical (E/M) impedance technique, simple models, like free PWAS of different shapes and 1-dimmension beam with PWAS are investigated and the simulated structural E/M impedance was presented. For the wave propagation SHM technique, a long beam with several PWAS installed was studied. One PWAS is excited by tone burst signals and mechanical wave will propagate along the beam. The existence of a crack will affect the structure integrity and the echo reflected by crack can be observed through the simulations.

As data densities in computer hard disk drives increase, airflow-induced vibration of the disk drive suspension becomes a major barrier to positioning the read-write head with sufficient precision. One component in reducing these vibrations is a dedicated sensor system for detecting vibration on the sensor arm directly, which enables high-frequency sampling and modal selectivity. In this paper, an efficient method for identifying optimal position and shape of piezoelectric strain gages on a flexible structure is presented, and applied to the steel suspension of a hard disk drive. Zinc oxide deposition processes are adapted to steel substrates, and used to fabricate miniature zinc oxide strain gages at the optimal strain gage location. Substrates with sensors installed were assembled into full disk drive suspensions and tested in a commercial disk drive.

The thermal protection system (TPS) represents the greatest risk factor after propulsion for any transatmospheric mission. Any damage to the TPS leaves the space vehicle vulnerable and could result in the loss of human life as what happened in the Columbia accident. Aboard the current Space Shuttle no system exists to notify the astronauts or ground control if the thermal protection system has been damaged. The goal of this project is to add self-diagnostic capability to future spacecraft through the use of a fiber-optic network embedded in the TPS. This system of sensors would allow for the detection of region fracture, optical temperature measurement at different depths within the region, communication with neighboring regions, and detection of communication loss. The hardware that would be added to each region consists of a radiation-hardened microcontroller, fiber-optic sensors and power. Each region would have the ability of reporting its own damage as well as reporting a loss of communication with any of its neighboring regions. Such a network would provide continuous health monitoring of the TPS in real-time. The developed intelligent region technologies are readily adaptable to ablative thermal protective systems.

Sonic IR Imaging is a novel NDE technique, which combines a short ultrasonic pulse excitation and infrared imaging to detect defects in materials and structures. The ultrasound pulse, typically a fraction of a second long, causes heating in the defects, which results in the change of IR radiation from the target. This change can be detected by infrared sensors, and thus, defects can be identified. One key objective in developing this technology is to maximize the IR signal so that the probability of detection (POD) of defects is optimized. From our work, we learned that the ultrasonic frequency, coupling medium between the ultrasonic transducer and the target, the pressure from the transducer on the target, the characteristics of the target itself, etc. are all factors that affect the IR signals. In addition, different IR sensors have different responses for the same IR radiation. To develop Sonic IR Imaging technology, it is important to study the relationship between the IR signal and the input acoustic energy for different system configurations. In this paper, we'll describe the thermal energy computing tools developed for analyzing data from different sets of parameters in Sonic IR Imaging.

Pavement maintenance is vital for travel safety; detecting road weather conditions using a wireless sensing network poses many challenges due to the harsh environment. This paper presents some preliminary results of an ongoing effort of applying "Smart Dust" sensor network for monitoring pavement temperature and moisture condition to detect icy road condition. Careful considerations yield effective solutions to various hardware and software development issues including the selection of sensors and antenna, design of casing, interfacing motes with alien sensors and programming of motes. A series of experiments is carried out to study traffic interference to packet delivery performance of a small-scale sensor network in a pseudo-field environment. In addition, several overnight tests are conducted to study the performance of motes operated under a power efficient condition. The results are analyzed and challenges are identified in this smart sensing application. The aforementioned research activities would benefit robust real-world implementations of off-the-shelf sensor network products.

There are many areas in Taiwan which does not have solid rocks. Suffering from the debris flow induced by the earthquakes and many flood disasters in recent years, the geology even shows the flimsiness. It is very frequent to find lots of falling stones on the highway; sometimes disasters take place. Because the optical fiber has the features of low loss and wide bandwidth; it has replaced the coaxial cable as the mainstream of the communication system in recent years. Because of its high sensitivity characteristic, the interferometer is usually applied to long distance, weak signal detection. In general, if the vibration is located at far away places, the weak signal makes it uneasy to monitor. The configuration of our sensing system is made of an interferometer and fiber Bragg Gratings. A demodulation circuit was used as the signal processor to measure the phase difference caused by vibration. Compare to that of a traditional accelerometer, the fiber optic sensor is more easier to implement in field applications. The later is an all fiber system with good accuracy for low frequency vibration measurement. According to our preliminary test, the dynamic range of the system is 45 dB and the noise level is 7.3 × 10^-3 rad. Hence, the object of this paper is to study the feasibility of a monitoring system, which is compatible with the existed optical fiber communication and a high sensitivity interferometer as a falling stone monitoring system.

This paper presents intrinsic polymer fiber (POF) sensors for high-strain applications such as the performance-based assessment and health monitoring of civil infrastructure systems subjected to earthquake loading or morphing aircraft. POFs provide a potential maximum strain range of 6-12%, are more flexible that silica optical fibers, and are more durable in harsh chemical or environmental conditions. Recent advances in the fabrication of singlemode POFs have made it possible to extend POFs to interferometric sensor capabilities. Furthermore, the interferometric nature of intrinsic sensors permits high accuracy for such measurements. A formulation for the sensor response is presented, including the finite deformation of the POF cross-section at high strain values and nonlinear strain optic effects in the polymer. In addition, the design of a time-of-flight interferometer for phase measurements over the large strain range required is outlined. Afterwards, initial measurements of the mechanical response of the sensor at various strain rates are presented. Finally the bond strength of specimens with the POF embedded in various structural materials is investigated.

Optical fiber sensors have received increasing attention in the fields of civil engineering due to their advantages such as explosion proof, immunity to electromagnetic interference and high accuracy, especially fitting for measurement applications in harsh environment. In this paper, a novel FBG (fiber Bragg grating) strain sensor, which was packaged in a 1.2mm stainless steel tube by epoxy resin, was developed. Strain transferring characteristics was conducted in the calibration experiment on the plain concrete beam using universal testing machine. Three tube-packaged strain FBG sensors were applied in the vibration experiment of roller compacted artificial concrete dam model. The strain analysis was done with different work conditions by three dynamic loads of noise, sine wave and random wave. The different parts of roller compacted artificial concrete dams were monitored successfully in elastic strain and split strain by action of dynamic load. The results show that possible fatigue and breakage damages can be monitored conveniently by embedded FBG sensors, and information can be well provided for structure health diagnoses under the action of dynamic load.

Measuring the displacement of flexible bridges directly is difficult particularly on monumental suspension bridges. Since these bridges cross over sea channels or large rivers, installation of conventional devices for displacement measurement is technically not easy and costly, if not impossible. In this study, real-time displacement measurement of bridges was carried out by means of digital image processing techniques. This is innovative, highly cost-effective and easy to implement, and yet maintains the advantages of dynamic measurement and high resolution. First, the measurement point is marked on the bridge with a target panel of known geometry. A commercially available digital video camcorder is installed on a fixed point some distance from the bridge (e.g. on the coast) or on a pier (abutment) of the bridge which can be regarded as a fixed point. The camcorder with a telescopic device installed takes a motion picture of the target marked. Meanwhile, the displacement of the target is calculated using an image processing technique, which requires a target recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and the number of pixels moved. To measure the displacement at multiple locations on the bridge, an effective synchronized vision-based system was developed using master/slave system and wireless data communication. For the purpose of verification, the measured displacement by synchronized vision-based system was compared with the data measured by a contact-type sensor, a linear variable differential transformer (LVDT) from laboratory tests. The displacement measured by the proposed method showed a good agreement with the data from the conventional sensors. A field test on a pedestrian suspension bridge was also carried out to check the feasibility of the proposed system.

In order to provide a true distributed sensor and control system for civil structures, we have developed a Structural Nervous System that mimics key attributes of a human nervous system. This nervous system is made up of building blocks that are designed based on mechanoreceptors as a fundamentally new approach for the development of a structural health monitoring and diagnostic system that utilizes the recently discovered plant-protein forisomes, a novel non-living biological material capable of sensing and actuation. In particular, our research has been focused on producing a sensory nervous system for civil structures by using forisomes as the mechanoreceptors, nerve fibers, neuronal pools, and spinocervical tract to the nodal and central processing units. This paper will present up to date results of our research, including the design and analysis of the structural nervous system.

The authors have been studying the strain sensitive materials which are based on conductivity change resulting from structural change in percolation system. In this study, we have developed a maximum strain memory sensor, which enables to detect damage to structures easily even after a large earthquake. To confirm the performance as the sensor, tensile tests embedded into concrete specimen have been conducted. As a result, it is discovered that this sensor is sufficiently effective to diagnose cracks in the concrete structure.

In this study, fiber reinforced polymer (FRP) confined smart concrete/mortar sensors were invented and validated for significantly improved measurement range. Several trial mixes were made using cement mortar and micron-phase graphite powders at different mix proportions. Compressive loading tests were conducted on smart mortar cylinder specimens with or without FRP confinement. Two-probe method was used to detect the electrical resistance of the smart cement mortar specimens. Strong correlation was recognized between the stress and electric resistance of the smart mortar. The test results indicated that the FRP wrapping could significantly enlarge the range of such self-sensing property as a consequence of confinement.

This paper presents the results of a study to determine the effect of temperature on modal variability on a curved post-tensioned box girder bridge with three continuous spans. The bridge has been monitored during the past 6 years using 16 accelerometers, 12 thermocouples and 6 tilt meters. Monitoring is based on normal vehicle loading. There is concern that changes due to temperature variations may mask changes due to structural damage. A thorough understanding of this uncertainty is necessary so that changes in vibration response resulting from damage can be discriminated from changes that are due to temperature variability. This paper presents the results of a study to evaluate the ambient vibration information over a full year period in which data was collected continuously. The effects of temperature change on the bridge's modal frequencies are analyzed and interpreted. This correlation between the natural frequencies and temperature is essential to establishing a structural health monitoring approach that can provide for indications that damage has occurred.