This PDF file contains the front matter associated with SPIE Proceedings Volume 9063, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

This paper discusses the development of an Acoustic Emission (AE) wireless node and its application for SHM (Structural Health Monitoring). The instrument development was planned for applications monitoring steel and concrete bridges components. The final product, now commercially available, is a sensor node which includes multiple sensing elements, on board signal processing and analysis capabilities, signal conditioning electronics, power management circuits, wireless data transmission element and energy harvesting unit. The sensing elements are capable of functioning in both passive and active modes, while the multiple parametric inputs are available for connecting various sensor types to measure external characteristics affecting the performance of the structure under monitoring. The output of all these sensors are combined and analyzed at the node in order to minimize the data transmission rate, which consumes significant amount of power. Power management circuits are used to reduce the data collection intervals through selective data acquisition strategies and minimize the sensor node power consumption. This instrument, known as the 1284, is an excellent platform to deploy SHM in the original bridge applications, but initial prototypes has shown significant potential in monitoring composite wind turbine blades and composites mockups of Unmanned Autonomous Vehicles (UAV) components; currently we are working to extend the use of this system to fields such as coal flow, power transformer, and off-shore platform monitoring.

Gearbox components of aerospace structures are typically made of brittle materials with high fracture toughness, but susceptible to fatigue failure due to continuous cyclic loading. Structural Health Monitoring (SHM) methods are used to monitor the crack growth in gearbox components. Damage detection methodologies developed in laboratory-scale experiments may not represent the actual gearbox structural configuration, and are usually not applicable to real application as the vibration and wave properties depend on the material, structural layers and thicknesses. Also, the sensor types and locations are key factors for frequency content of ultrasonic waves, which are essential features for pattern recognition algorithm development in noisy environments. Therefore, a deterministic damage detection methodology that considers all the variables influencing the waveform signature should be considered in the preliminary computation before any experimental test matrix. In order to achieve this goal, we developed two dimensional finite element models of a gearbox cross section from front view and shaft section. The cross section model consists of steel revolving teeth, a thin layer of oil, and retention plate. An ultrasonic wave up to 1 MHz frequency is generated, and waveform histories along the gearbox are recorded. The received waveforms under pristine and cracked conditions are compared in order to analyze the crack influence on the wave propagation in gearbox, which can be utilized by both active and passive SHM methods.

Advantages of two dimensional electronic ultrasonic beam focusing, steering and scanning with the matrix phased array (MPA) technology has been used to visualize the conditions of resistance spot welds in auto vehicle grade advanced high strength steel carbon steels nondestructively. Two of the commonly used joining techniques, resistance spot welding and resistance seam welding, for thin aerospace grade plates made of aluminum, titanium, and stainless steels have also been inspected with the same MPA NDE system. In this study, a detailed discussions of the current MPA based ultrasonic real time imaging methodology has been made followed by some of the NDT results obtained with various welded test coupons.

Titanium aircraft engine forgings are inspected at all material depths using the ultrasonic testing (UT) process called multizone inspection. This process creates a raster array of 1D A-Scan signals that image the material’s underlying characteristics, which in the case of forged titanium, result in highly variable noise. The 3D volume is analyzed as a stack of 2D depth slices that are gated into C-Scans. The intention is to accept or reject the part by examining for the presence of small anomalies in these images in terms of their signal to noise ratio (SNR). Ultrasonic testing inspectors determine that rejection decision by using specialized image software and a 2D assisted defect recognition (ADR) algorithm. This approach for calculating SNR greatly reduces inspector subjectivity compared to a manual approach, but in 2D, not all available information is used to determine a true detection. Enabling ADR to use the 3D volume for the forging inspection, an automated SNR calculation has been extended to address these issues. The 3D ADR algorithm’s life cycle from design to validation will be discussed. Validation testing results have shown that 3D ADR significantly increases probability of detection compared to 2D ADR while also reducing its false positive rate.

Ultrasonic guided waves are finding increased use in a variety of Nondestructive Evaluation and Structural Health Monitoring applications due to their efficiency in defect detection using a sensor at a single location to inspect a large area of a structure and an ability to inspect hidden and coated areas for example. With a thorough understanding of guided wave mechanics, researchers can predict which guided wave modes will have a high probability of success in a particular nondestructive evaluation application. For example, in a sample problem presented here to access bond integrity, researchers may choose to use a guided wave mode which has high in-plane displacement, stress, or other feature at the interface. However, since material properties used for modeling work may not be precise for the development of dispersion curves, in many cases guided wave mode and frequency selection should be adjusted for increased inspection efficiency in the field. In this work, a phased array comb transducer is used to sweep over phase velocity – frequency space to tune mode excitation for improved defect characterization performance. A thin polycarbonate layer bonded to a thick metal plate is considered with a contaminated surface prior to bonding. Physicallybased features are used to correlate wave signals with defect detection. Features assessed include arrival time and the frequency of maximum amplitude. A pseudo C-scan plot is presented which can be used to simplify data analysis. Excellent results are obtained.

Piezoelectric crystals are popular for passive sensors, such as accelerometers and acoustic emission sensors, due to their robustness and high sensitivity. These sensors are widespread in structural health monitoring among civil and industrial structures, but there is little application in high temperature environments (e.g. > 1000°C) due to the few materials that are capable of operating at elevated temperatures. Most piezoelectric materials suffer from a loss of electric properties above temperatures in the 500-700°C range, but rare earth oxyborate crystals, such as Yttrium calcium oxyborate (YCOB), retain their piezoelectric properties above 1000 °C. Our previous research demonstrated that YCOB can be used to detect transient lamb waves via Hsu-Nielsen tests, which replicate acoustic emission waves, up to 1000°C. In this paper, YCOB piezoelectric acoustic emission sensors were tested for their ability to detect crack progression at elevated temperatures. The sensor was fabricated using a YCOB single crystal and Inconel electrodes and wires. The sensor was mounted onto a stainless steel bar substrate, which was machined to include a pre-crack notch. A dynamic load was induced on the bar with a shaker in order to force the crack to advance along the thickness of the substrate. The obtained raw data was processed and analyzed in the frequency domain and compared to the Lamb wave modes that were evaluated in previous Hsu-Nielsen testing for the substrate.

The influence of mechanical noise in an Acoustic Emission (AE) testing still obscures its successful application in monitoring various structures and systems. While advances in pattern recognition algorithms are helpful to differentiate relevant data from captured noise, the algorithms fail if the characteristics of relevant data are unknown. A better scientific understanding of the characteristics of elastic waves due to damage mechanisms in a structure is needed for developing a quantitative measurement approach of damage (e.g. frequency content, type, orientation) based on the AE method. The results of small scale coupon tests cannot be directly used in large scale structures due to the influence of boundaries on propagating waves detected by the AE sensors. In this paper, a steel plate is modeled using an absorbing boundary condition and spectral elements in order to understand the direct wave release from damage without influenced by reflections. The selected absorbing layer is perfectly matched layer (PML), which is designed such a way that wave reflections from boundaries back into the solution domain regardless of frequency or angle of incidence are prevented through providing a stable solution and satisfying the Sommerfeld radiation condition. The displacement-based and timedomain equations of PML are utilized. The numerical results of the two-dimensional metal plate with absorbing boundary condition are validated with extended-geometry numerical models and experimental results.

This paper represents a hybrid non-destructive testing (HNDT) approach based on infrared thermography (IRT), acoustic emission (AE) and ultrasonic (UT) techniques for effective damage quantification of partially grouted concrete masonry walls (CMW). This integrated approach has the potential to be implemented for the health monitoring of concrete masonry systems. The implementation of this hybrid approach assists the cross validation of in situ recorded information for structural damage assessment. In this context, NDT was performed on a set of partially grouted CMW subjected to cyclic loading. Acoustic emission (AE) signals and Infrared thermography (IRT) images were recorded during each cycle of loading while the ultrasonic (UT) tests were performed in between each loading cycle. Four accelerometers, bonded at the toe of the wall, were used for recording waveforms for both passive (AE) and active (UT) acoustics. For the active approach, high frequency stress waves were generated by an instrumented hammer and the corresponding waveforms were recorded by the accelerometers. The obtained AE, IRT, and UT results were correlated to visually confirm accumulated progressive damage throughout the loading history. Detailed post-processing of these results was performed to characterize the defects at the region of interest. The obtained experimental results demonstrated the potential of the methods to detect flaws on monitored specimens; further experimental investigations are planned towards the quantitative use of these NDT methods.

In this work an innovative methodology was employed for monitoring the fracture behavior in silicon carbide fiberreinforced ceramic matrix composites. This new methodology was based on the combined use of IR thermography and acoustic emission. Compact tension SiC/BMAS specimens were tested with unloading/reloading loops and the thermal dissipation due to crack propagation and other damage mechanisms was monitored by IR thermography. The accuracy of this technique was benchmarked by optical measurements of crack length. In addition, using acoustic emission descriptors, such as activity during the unloading part of the cycles, provided the critical level of damage accumulation in the material. Acoustic emission allowed to closely follow the actual crack growth monitored by IR thermography, enabling quantitative measurements.

In this work the flexural behavior of textile reinforced cement (TRC) laminate is examined using acoustic emission (AE). The TRC composite is a combination of inorganic phosphate cement (IPC) with randomly distributed glass fibres. IPC has been developed at the “Vrije Universiteit Brussel” and shows a neutral pH meaning that glass fibers are hardly attacked. During bending, stresses lead to the activation of damage mechanisms like matrix cracking, delaminations and fiber pull-out being in succession or overlapping in time. AE records the responses of the damage propagation events and allows the monitoring of the fracture behavior from the onset to the final stage. The effect of the span in three-point bending tests, which is varied to create different stress fields, is targeted. Parameters like duration and frequency reveal information about the mode of the damage sources in relation to the span. Results show that as the span decreases, the dominant damage mode shifts away from bending and acquires more shear characteristics by increasing the interlaminar shearing events.

The study focuses on the early detection of ice using controlled acoustic waves propagating in the wind turbine blades. An experimental set-up with a cold climate chamber, a composite test object used in turbine blades and equipment for glaze and rime ice production has been developed. Controlled acoustic waves are generated by magnetostrictive Terfenol-D based actuator. The propagation of three orthogonally polarized acoustic waves was studied by means of 6 accelerometers positioned, 3 each, in 2 holders on the 8 m long composite test object. The results show that for the considered composite test object the formation of ice, the ice mass, icing areas and the temperature have a significant influence on controlled acoustic waves propagation w.r.t. Fourier transform, amplitude attenuation and RMS values as indicators concluding that the proposed acoustic wave technique is a promising approach for ice detection.

According to the 2013 ASCE report card America’s infrastructure scores only a D+. There are more than four million miles of roads (grade D) in the U.S. requiring a broad range of maintenance activities. The nation faces a monumental problem of infrastructure management in the scheduling and implementation of maintenance and repair operations, and in the prioritization of expenditures within budgetary constraints. The efficient and effective performance of these operations however is crucial to ensuring roadway safety, preventing catastrophic failures, and promoting economic growth. There is a critical need for technology that can cost-effectively monitor the condition of a network-wide road system and provide accurate, up-to-date information for maintenance activity prioritization. The Versatile Onboard Traffic Embedded Roaming Sensors (VOTERS) project provides a framework and the sensing capability to complement periodical localized inspections to continuous network-wide health monitoring. Research focused on the development of a cost-effective, lightweight package of multi-modal sensor systems compatible with this framework. An innovative software infrastructure is created that collects, processes, and evaluates these large time-lapse multi-modal data streams. A GIS-based control center manages multiple inspection vehicles and the data for further analysis, visualization, and decision making. VOTERS’ technology can monitor road conditions at both the surface and sub-surface levels while the vehicle is navigating through daily traffic going about its normal business, thereby allowing for network-wide frequent assessment of roadways. This deterioration process monitoring at unprecedented time and spatial scales provides unique experimental data that can be used to improve life-cycle cost analysis models.

Our nation’s infrastructural systems are crumbling. The deteriorating process grows over time. The physical aging of these vital facilities and the remediation of their current critical state pose a key societal challenge to the United States. Current sensing technologies, while well developed in controlled laboratory environments, have not yet yielded tools for producing real-time, in-situ data that are adequately comprehensible for infrastructure decision-makers. The need for advanced sensing technologies is national because every municipality and state in the nation faces infrastructure management challenges. The need is critical because portions of infrastructure are reaching the end of their life-spans and there are few cost-effective means to monitor infrastructure integrity and to prioritize the renovation and replacement of infrastructure elements. New advanced sensing technologies that produce cost-effective inspection and real-time monitoring data, and that can also help or aid in meaningful interpretation of the acquired data, therefore will enhance the safety in regard to the public on structural integrity by issuing timely and accurate alert data for effective maintenance to avoid disasters happening. New advanced sensing technologies also allow more informed management of infrastructural investments by avoiding premature replacement of infrastructure and identifying those structures in need of immediate action to prevent from catastrophic failure. Infrastructure management requires that once a structural defect is detected, an economical and efficient repair be made. Advancing the technologies of repairing infrastructure elements in contact with water, road salt, and subjected to thermal changes requires innovative research to significantly extend the service life of repairs, lower the costs of repairs, and provide repair technologies that are suitable for a wide range of conditions. All these new technologies will provide increased lifetimes, security, and safety of elements of critical infrastructure for the Nation’s already deteriorating civil infrastructure. It is envisioned that the Nation should look far beyond: not only should we efficiently and effectively address current problems of the aging infrastructure, but we must also further develop next-generation construction materials and processes for new construction. To accomplish this ambitious goal, we must include process efficiency that will help select the most reliable and cost-effective materials in construction processes; performance and cost will be the prime consideration for selections construction materials based on life-cycle cost and materials performance; energy efficiency will drive reduced energy consumption from current levels by 50 % per unit of output; and environmental responsiveness will achieve net-zero waste from construction materials and its constituents. Should it be successfully implemented, we will transform the current 21^st century infrastructure systems to enable the vital functioning of society and improve competitiveness of the economy to ensure that our quality of life remains high.

In order to best prioritize road maintenance, the level of deterioration must be known for all roads in a city’s network. Pavement Condition Index (PCI) and International Roughness Index (IRI) are two standard methods for obtaining this information. However, IRI is substantially easier to measure. Significant time and money could be saved if a method were developed to estimate PCI from IRI. This research introduces a new method to estimate IRI and correlate IRI with PCI. A vehicle-mounted dynamic tire pressure sensor (DTPS) system is used. The DTPS measures the signals generated from the tire/road interaction while driving. The tire/road interaction excites surface waves that travel through the road. DTPS, which is mounted on the tire’s valve stem, measures tire/road interaction by analyzing the pressure change inside the tire due to the road vibration, road geometry and tire wall vibration. The road conditions are sensible to sensors in a similar way to human beings in a car. When driving on a smooth road, tire pressure stays almost constant and there are minimal changes in the DTPS data. When driving on a rough road, DTPS data changes drastically. IRI is estimated from the reconstructed road profile using DTPS data. In order to correlate IRI with PCI, field tests were conducted on roads with known PCI values in the city of Brockton, MA. Results show a high correlation between the estimated IRI values and the known PCI values, which suggests that DTPS-based IRI can provide accurate predictions of PCI.

An investigation on the prediction of macrotexture Mean Texture Depth (MTD) of pavement from a moving vehicle is conducted. The MTD was predicted by using the tire/road noise measured from a microphone mounted underneath a moving vehicle. Principal Component Analysis (PCA) is used to filter noise from microphone data prioer to estimating its energy over an optimally selected bandwidth. Energy obtained using this method is named PCA energy, hence the developed method for MTD estimation is termed as PCA Energy Method. The acoustic energy is assumed to have positive linear correlation with MTD of pavement. Moreover, PCA was used to differentiate important information about the road surface from noisy data while vehicle is moving, yielding a set of principal component vectors representing the conditions of each road section. This principal component vector was used to compute the PCA energy that is to be used for MTD prediction. The frequency band most relative to pavement macrotexture was determined to be 140 to 700 Hz through theoretical and statistical research. Then, a MTD prediction model was built based on a Taylor series expansion with two variables, PCA energy and the driving speed of the vehicle. The model parameters were obtained from an engineered track (interstate highway) with known MTD, and then applied to urban roads for the feasibility test. The accuracy of the model is 83.61% for the engineered track, which is 10% higher than the previous energy-based methods without PCA treatment. Moreover, applicability of the model is increased by the extended MTD prediction range between 0.2 and 3 mm compared to that of the engineered track having 0.4 to 1.5 mm. In addition, the MTD could be predicted every 7.8 meters and with good repeatability in the urban road test, which proves the feasibility of the proposed approach. Therefore, the PCA Energy Method is a reliable, efficient, and cost effective way to predict MTD for engineering applications as an important index for pavement friction prediction and pavement segregation identification.

The main objective of this study is to develop a prototype air-coupled ultrasonic system (ACUS) for simultaneous data collection of impact-echo tests and surface wave measurements in concrete bridge decks. The ACUS includes two hexagonal air-coupled sensor arrays, each of which includes a solenoid-driven impact source at the center and six air-coupled sensors (ACSs) with parabolic acoustic reflectors (PARs) at vertices of a hexagon. The developed ACUS will be used as a part of an automated nondestructive evaluation and rehabilitation system (ANDERS) for concrete bridge deck inspection. First, a prototype hexagonal ACS array with PARs was developed in laboratory, and the prototype ACUS was built by combining the two hexagonal ACSs in a row. Second, an advanced data interpretation and visualization algorithm for the ACUS was developed for presenting the resulting data from individual test method, and data fusion of the two methods. Third, acoustic scanning was conducted using the developed ACUS over a simulated concrete bridge deck having various artificial defects (delaminations, surface-breaking cracks, segregated aggregates, partially grouted tendon ducts, and accelerated corrosion test regions). The results were visualized as several defect maps (i.e., frequency map and energy map from the IE testing, and velocity and transmission map from the surface wave measurements). In addition, the images from the two different test methods were combined by using a proposed fusion algorithm. It will be demonstrated that the ACUS is very effective for improving speed of data collection, and that the innovative fusion algorithm enables more accurate data interpretation.

The information presented in this report provides a detailed assessment of the condition of the Arlington Memorial Bridge (AMB) deck. The field-data collection was obtained by both the RABIT™ Bridge Inspection Tool and a number of semi-automated non-destructive evaluation (NDE) tools. The deployment of the semi-automated NDE tools was performed to inspect the AMB deck condition and also to validate data obtained by the RABIT™ Bridge Inspection Tool. Data mining and analysis were accomplished through enhanced data interpretation and visualization capabilities using advanced data integration, fusion, and 2D rendering. One of the major challenges that the research team had to overcome in assessing the condition of the AMB deck was the presence of an asphalt overlay on the entire bridge deck.

This study was to assess the porosity of Porous Asphalt Concrete (PAC) in conjunction with a medical X-ray computed tomography (CT) facility. The PAC was designed as the surface course to achieve the target porosity 18%. There were graded aggregates, soils blended with 50% of coarse sand, and crushed gravel wrapped with geotextile compacted and served as the base, subbase, and infiltration layers underneath the PAC. The test site constructed in 2004 is located in Northern of Taiwan in which the daily traffic has been light and limited. The porosity of the test track was investigated. The permeability coefficient of PAC was found severely degraded from 2.2×10^-1 to 1.2×10^-3 -cm/sec, after nine-year service, while the permeability below the surface course remained intact. Several field PAC cores were drilled and brought to evaluate the distribution of air voids by a medical X-ray CT nondestructively. The helical mode was set to administrate the X-ray CT scan and two cross-sectional virtual slices were exported in seconds for analyzing air voids distribution. It shows that the clogging of voids occurred merely 20mm below the surface and the porosity can reduce as much about 3%. It was also found that the roller compaction can decrease the porosity by 4%. The permeability reduction in this test site can attribute to the voids of PAC that were compacted by roller during the construction and filled by the dusts on the surface during the service.

The objective of the present study was the repair monitoring of an extensively cracked concrete floor using the Impulse – Response method. The study included the evaluation of the condition of the concrete floor that suffered from extensive cracking on its surface, through systematic tests. The purpose of the study was to investigate the causes that led to extensive cracking on the floor surface in order to plan the repair strategy. The investigation included a thorough visual inspection and recording of cracks, estimation of the crack depth using ultrasonic pulse velocity measurements, investigation for voids between the concrete floor and the underlying aggregate layer using the Impulse – Response method, concrete floor thickness estimation using the Impact – Echo method and concrete quality estimation using cores cutting. The repair method that was chosen was based on grout injections in order to fill the voids located between the concrete and the underlying aggregate layer. The area, where the injections took place, was inspected using the Impulse – Response method before and after the injections for monitoring purposes and a secondary grid was designed after considering the results. The area was inspected for a third time, after injecting in the secondary grid, in order to confirm the successful filling of the voids.

Structural Health monitoring refers to monitoring the health of plate-like walls of large reactors, pipelines and other structures in terms of corrosion detection and thickness estimation. The objective of this work is modeling the ultrasonic guided waves generated in a plate. The piezoelectric is excited by an input pulse to generate ultrasonic guided lamb waves in the plate that are received by another piezoelectric transducer. In contrast with existing methods, we develop a mathematical model of the direct component of the signal (DCS) recorded at the terminals of the piezoelectric transducer. The DCS model uses maximum likelihood technique to estimate the different parameters, namely the time delay of the signal due to the transducer delay and amplitude scaling of all the lamb wave modes due to attenuation, while taking into account the received signal spreading in time due to dispersion. The maximum likelihood estimate minimizes the energy difference between the experimental and the DCS model-generated signal. We demonstrate that the DCS model matches closely with experimentally recorded signals and show it can be used to estimate thickness of the plate. The main idea of the thickness estimation algorithm is to generate a bank of DCS model-generated signals, each corresponding to a different thickness of the plate and then find the closest match among these signals to the received signal, resulting in an estimate of the thickness of the plate. Therefore our approach provides a complementary suite of analytics to the existing thickness monitoring approaches.

As externally bonded fiber-reinforced polymer (FRP) is a critical load-bearing component of strengthened or retrofitted civil infrastructures, the betterment of structural health monitoring (SHM) methodology for such composites is imperative. Henceforth the vibrational characteristics of near surface interfacial defects involving delamination and trapped air pockets at the FRP-concrete interface are investigated in this study using a finite element approach. Intuitively, due to its lower interfacial stiffness compared with an intact interface, a damaged region is expected to have a set of resonance frequencies different from an intact region when excited by acoustic waves. It has been observed that, when excited acoustically, both the vibrational amplitudes and frequency peaks in the response spectrum of the defects demonstrate a significant deviation from an intact FRP-bonded region. For a thin sheet of FRP bonded to concrete with sizable interfacial defects, the fundamental mode under free vibration is shown to be relatively low, in the order of kHz. Due to the low resonance frequencies of the defects, the use of low-cost equipment for interfacial defect detection via response spectrum analysis is highly feasible.

Most of NDT system by magnetic field sensing has employed coil or semiconductor as sensor which has one axis sensitivity. Recent development of semiconductor technology can makes a chip that enable us to measure 3 axis magnetic field in a 1mm square. This vector information is expected to show a new insight in NDT testing. This presentation will show a basic experimental feasibility study for application of magnetic sensor to flux leakage and eddy current testing method by using a sensing system with the chip.

In a typical vibration test of tensioned cables, tension forces are mostly estimated from theory of a vibrating string with the first natural frequency. To obtain slightly better estimations, formulas based on an axially loaded beam can be employed. However, uncertainty on both flexural rigidity and effective length of the vibrating cable raise difficulty in reliably determining the possible range of the tension value. From the previous work of the authors, an alternative approach for the calculation of tension forces without the need of rigidity data had been proposed, in which frequencies of high modes are instead required in recovering accurate results. This paper extends the previous work to also consider the discrepancy between the design length and effective length so as to further improve the results. Feasibility of the proposed methodology with enhanced equations was verified by actual cable forces measured in an extradosed bridge. Current study aims to apply the proposed approach to the dynamic monitoring of the in-situ stay cables so as to improve the traditional assessment results without increasing the testing costs.

Fractal dimension based damage detection method is studied for a composite structure with random material properties. A composite plate with localized matrix crack is considered. Matrix cracks are often seen as the initial damage mechanism in composites. Fractal dimension based method is applied to the static deformation curve of the structure to detect localized damage. Static deflection of a cantilevered composite plate under uniform loading is calculated using the finite element method. Composite material shows spatially varying random material properties because of complex manufacturing processes. Spatial variation of material property is represented as a two dimensional homogeneous Gaussian random field. Karhunen-Loeve (KL) expansion is used to generate a random field. The robustness of fractal dimension based damage detection methods is studied considering the composite plate with spatial variation in material properties.

A worthy goal for the structural health monitoring field is the creation of a scalable monitoring system architecture that abstracts many of the system details (e.g., sensors, data) from the structure owner with the aim of providing “actionable” information that aids in their decision making process. While a broad array of sensor technologies have emerged, the ability for sensing systems to generate large amounts of data have far outpaced advances in data management and processing. To reverse this trend, this study explores the creation of a cyber-enabled wireless SHM system for highway bridges. The system is designed from the top down by considering the damage mechanisms of concern to bridge owners and then tailoring the sensing and decision support system around those concerns. The enabling element of the proposed system is a powerful data repository system termed SenStore. SenStore is designed to combine sensor data with bridge meta-data (e.g., geometric configuration, material properties, maintenance history, sensor locations, sensor types, inspection history). A wireless sensor network deployed to a bridge autonomously streams its measurement data to SenStore via a 3G cellular connection for storage. SenStore securely exposes the bridge meta- and sensor data to software clients that can process the data to extract information relevant to the decision making process of the bridge owner. To validate the proposed cyber-enable SHM system, the system is implemented on the Telegraph Road Bridge (Monroe, MI). The Telegraph Road Bridge is a traditional steel girder-concrete deck composite bridge located along a heavily travelled corridor in the Detroit metropolitan area. A permanent wireless sensor network has been installed to measure bridge accelerations, strains and temperatures. System identification and damage detection algorithms are created to automatically mine bridge response data stored in SenStore over an 18-month period. Tools like Gaussian Process (GP) regression are used to predict changes in the bridge behavior as a function of environmental parameters. Based on these analyses, pertinent behavioral information relevant to bridge management is autonomously extracted.

This study introduces an efficient procedure to estimate the structural response of a suspension bridge in real-time based on a limited set of measured data. Unlike conventional techniques, the proposed procedure does not employ mode shapes and frequencies. In this study, the proposed technique is used to estimate the response of a suspension bridge structure based on a set of strain gauge measurements. Finite element analysis is performed only once to set up the structural parameters, namely computed flexibility matrix, and computed hanger forces matrix. The response of the bridge was estimated without any additional finite element analysis using the computed structural parameters and the measured hanger strains. The Alfred Zampa Memorial Bridge, on Interstate 80 in California, was selected for this study. A high fidelity finite element model of the bridge was developed using the general purpose computer program ADINA. The proposed method has been proven to have the capability to estimate any type of structural response in real time based on the measured hanger strains, and provides an important part of an integrated Structure Health Monitoring (SHM) system for major bridges.

This study investigated a number of different damage detection algorithms for structural health monitoring of a typical suspension bridge. The Alfred Zampa Memorial Bridge, a part of the Interstate 80 in California, was selected for this study. The focus was to implement and validate simple damage detection algorithms for structural health monitoring of complex bridges. Accordingly, the numerical analysis involved development of a high fidelity finite element model of the bridge in order to simulate various structural damage scenarios. The finite element model of the bridge was validated based on the experimental modal properties. A number of damage scenarios were simulated by changing the stiffness of different bridge components including suspenders, main cable, bulkheads and deck. Several vibration-based damage detection methods namely the change in the stiffness, change in the flexibility, change in the uniform load surface and change in the uniform load surface curvature were employed to locate the simulated damages. The investigation here provides the relative merits and shortcomings of these methods when applied to long span suspension bridges. It also shows the applicability of these methods to locate the decay in the structure.

An active sensing diagnostic system for reinforced concrete SHM has been under investigation. Test results show that the system can detect the damage of the structure. To fundamentally understand the damage algorithm and therefore to establish a robust diagnostic method, accurate Finite Element Analysis (FEA) for the system becomes essential. For the system, a rebar with surface bonded PZT under a transient wave load was simulated and analyzed using commercial FEA software. A detailed 2D axi-symmetric model for a rebar attaching PZT was first established. The model simulates the rebar with wedges, an epoxy adhesive layer, as well as a PZT layer. PZT material parameter transformation with high order tensors was discussed due to the format differences between IEEE Standard and ANSYS. The selection of material properties such as Raleigh damping coefficients was discussed. The direct coupled-field analysis type was selected during simulation. The results from simulation matched well with the experimental data. Further simulation for debonding damage detection for concrete beam with the PZT rebar has been performed. And the numerical results have been validated with test results too. The good consistency between two proves that the numerical models were reasonably accurate. Further system optimization has been performed based on these models. By changing PZT layout and size, the output signals could be increased with magnitudes. And the damage detection signals have been found to be increased exponentially with the debonding size of the rebar.

Recently, novel methods to estimate the strength of concrete have been reported based on numerous NDT methods. Especially, electro-mechanical impedance technique using piezoelectric sensors are studied to estimate the strength of concrete. However, the previous research works could not provide the general information about the early-age strength important to manage the quality of concrete and/or the construction process. In order to estimate the early-age strength of concrete, the electro-mechanical impedance method and the artificial neural network(ANN) is utilized in this study. The electro-mechanical impedance varies with the mechanical properties of host structures. Because the strength development is most influential factor among the change of mechanical properties at early-age of curing, it is possible to estimate the strength of concrete by analyzing the change of E/M impedance. The strength of concrete is a complex function of several factors like mix proportion, temperature, elasticity, etc. Because of this, it is hard to mathematically derive equations about strength of concrete. The ANN can provide the solution about early-age strength of concrete without mathematical equations. To verify the proposed approach, a series of experimental studies are conducted. The impedance signals are measured using embedded piezoelectric sensors during curing process and the resonant frequency of impedance is extracted as a strength feature. The strength of concrete is calculated by regression of strength development curve obtained by destructive test. Then ANN model is established by trained using experimental results. Finally the ANN model is verified using impedance data of other sensors.

This paper focuses on new signal processing algorithms customized for an air coupled Ultra-Wideband (UWB) Ground Penetrating Radar (GPR) system targeting highway pavements and bridge deck inspections. The GPR hardware consists of a high-voltage pulse generator, a high speed 8 GSps real time data acquisition unit, and a customized field-programmable gate array (FPGA) control element. In comparison to most existing GPR system with low survey speeds, this system can survey at normal highway speed (60 mph) with a high horizontal resolution of up to 10 scans per centimeter. Due to the complexity and uncertainty of subsurface media, the GPR signal processing is important but challenging. In this GPR system, an adaptive GPR signal processing algorithm using Curvelet Transform, 2D high pass filtering and exponential scaling is proposed to alleviate noise and clutter while the subsurface features are preserved and enhanced. First, Curvelet Transform is used to remove the environmental and systematic noises while maintain the range resolution of the B-Scan image. Then, mathematical models for cylinder-shaped object and clutter are built. A two-dimension (2D) filter based on these models removes clutter and enhances the hyperbola feature in a B-Scan image. Finally, an exponential scaling method is applied to compensate the signal attenuation in subsurface materials and to improve the desired signal feature. For performance test and validation, rebar detection experiments and subsurface feature inspection in laboratory and field configurations are performed.

Nondestructive evaluation (NDE) method reliability can be determined by a statistical flaw detection study called probability of detection (POD) study. In many instances, the NDE flaw detectability is given as a flaw size such as crack length. The flaw is either a crack or behaving like a crack in terms of affecting the structural integrity of the material. An alternate approach is to use a more complex flaw size parameter. The X-ray flaw size parameter, given here, takes into account many setup and geometric factors. The flaw size parameter relates to X-ray image contrast and is intended to have a monotonic correlation with the POD. Some factors such as set-up parameters, including X-ray energy, exposure, detector sensitivity, and material type that are not accounted for in the flaw size parameter may be accounted for in the technique calibration and controlled to meet certain quality requirements. The proposed flaw size parameter and the computer application described here give an alternate approach to conduct the POD studies. Results of the POD study can be applied to reliably detect small flaws through better assessment of effect of interaction between various geometric parameters on the flaw detectability. Moreover, a contrast simulation algorithm for a simple part-source-detector geometry using calibration data is also provided for the POD estimation.

This paper deals with modeling in electromagnetism in the field of eddy current for Non Destructive Evaluation. Several techniques could be used to diagnose structural damages. In eddy current application, a magnetic field generates by an excitation coil (or primary coil), interacts with a conductive target and generates eddy current. Variations in the phase and the magnitude of these eddy currents can be monitored using a second “receiver” coil. Variations in the physical properties (electrical conductivity, magnetic permeability,..) or the presence of any flaw in the target will cause a change in eddy current and a corresponding change in the phase and amplitude of measured signal. The interpretation of the signals requires a good understanding of the interaction between eddy current and structure. Therefore, researchers need analytical or numerical techniques to obtain a clear understanding of wave propagation behaviors. However, modeling of wave scattering phenomenon by conventional numerical techniques such as finite elements requires very fine mesh and heavy computational power. To go further, an innovative implementation of a semi-analytical modeling method, called the Distributed Points Source Method (DPSM), has been developed and used. The DPSM has already shown great potentialities for the versatile and computationally efficient modeling of complex electrostatic, electromagnetic or ultrasounic problems. In this paper, we report on a new implementation of the DPSM, called differential DPSM, which shows interesting prospects for the modeling of complex eddy current problems. In parallel, an Eddy Current Imager (ECI) has been recently developed in our laboratory in the aim of imaging cracks in metallic structures. In this paper, a simplified modeling of the ECI is presented using DPSM technique, the basics of DPSM formalism being firstly developed. A comparison between experimental and computed data obtained for a millimetric surface defect is presented in the form of complex magnetic cartographies. The obtained results show good agreement. Then, imaging in the case of a buried object in a metallic target is discussed. The effect of 2 parameters (the conductivity and the depth of the buried object) on the magnetic field which is computed at the surface of the material through our DPSM modeling is presented. The objective is to predict the sensor behavior for different values of these parameters, and to plot some arrays of curves, which can be used as calibration curves for the sensor’s user.

The Automated Nondestructive Evaluation and Rehabilitation System (ANDERS) aims to provide a uniquely comprehensive tool that will transform the manner in which bridge decks are assessed and rehabilitated. It is going to be achieved through: 1) much higher evaluation detail and comprehensiveness of detection at an early stage deterioration, 2) comprehensive condition and structural assessment at all stages of deterioration, and 3) integrated assessment and rehabilitation that will be minimally invasive, rapid and cost effective. ANDERS is composed of three physical systems that merge novel NDE technologies together with novel intervention approaches to arrest the deterioration processes. These technologies are incorporated within a series of human-operated and robotic vehicles to allow rapid, comprehensive application across large populations of bridges. To perform assessments, ANDERS is equipped with two complimentary nondestructive approaches. The first, Multi-Modal Nondestructive Evaluation (MM-NDE) System aims to identify and characterize localized deterioration with a high degree of resolution. The second, global structural assessment system named STAR aims to capture global structural characteristics and identify any appreciable effects of deterioration on a bridge structure. Output from these two approaches will be merged through a novel Automated Structural Identification (Auto St-Id) approach that constructs, calibrates, and utilizes simulation models to assess the overall structural vulnerability and capacity. These two systems comprise the assessment suite of ANDERS and directly inform the Nondestructive Rehabilitation (NDR) System. The NDR System leverages robotics for the precision and rapid delivery of novel materials capable of halting the early-stage deterioration identified. The paper covers the details of the three ANDERS components developed through the support from National Institute of Standards and Technology-Technology Innovation Program (NIST-TIP). Specifically, the paper will provide a description of the components, their principle of operation and applications, and describe how the components work together.

A multitude of concrete-based structures are typically part of a light water reactor (LWR) plant to provide foundation, support, shielding, and containment functions. Concrete has been used in the construction of nuclear power plants (NPPs) because of three primary properties, its inexpensiveness, its structural strength, and its ability to shield radiation. Examples of concrete structures important to the safety of LWR plants include containment building, spent fuel pool, and cooling towers. Comparative testing of the various NDE concrete measurement techniques requires concrete samples with known material properties, voids, internal microstructure flaws, and reinforcement locations. These samples can be artificially created under laboratory conditions where the various properties can be controlled. Other than NPPs, there are not many applications where critical concrete structures are as thick and reinforced. Therefore, there are not many industries other than the nuclear power plant or power plant industry that are interested in performing NDE on thick and reinforced concrete structures. This leads to the lack of readily available samples of thick and heavily reinforced concrete for performing NDE evaluations, research, and training. The industry that typically performs the most NDE on concrete structures is the bridge and roadway industry. While bridge and roadway structures are thinner and less reinforced, they have a good base of NDE research to support their field NDE programs to detect, identify, and repair concrete failures. This paper will summarize the initial comparative testing of two concrete samples with an emphasis on how these techniques could perform on NPP concrete structures.

This paper describes a computing backend for a water pipe monitoring system. Today, most such systems are divided into event-triggered and continuous monitoring, but they all lack systematic handling of data. Many systems simply store data in files with specific naming conventions and ad hoc formats, making them difficult to retrieve, maintain, disseminate, and analyze. To address these problems, our backend supports data management and dissemination. Unlike previous systems that store data in files or conventional databases before analysis, our modular architecture not only saves data in efficiently searchable ways by indexing as a baseline dataset but also detected events in discrete time manner and other processed data. To facilitate analysis, we design a plug-in structure to allow processing modules to perform inline processing and shorten detection time. For data dissemination, our architecture can compose multiple visualizations including geographical maps to create powerful tools to yield new insight into massive datasets. The backend system enables Internet web service for visualization, data management, and remote sensor control for better integration. Our system is applicable to not only water pipelines but also bridges and civil structures in general. Our proposed backend system has been implemented and validated through field deployment. One such system has been running for over 1.5 years and has collected millions of records to date. A Google Map integrated visualization service has been developed to demonstrate lively collected records in real-time. This is expected to be more helpful for better understanding of civil structures’ behavior in the long term.

A wireless health monitoring system has been developed for determining the height of water condensation in steam pipes. The data acquisition in this system is done remotely using a wireless network system. The developed system is designed to operate in the harsh manhole environment and the pipe temperature of over 200 °C. The test method is an ultrasonic pulse-echo and the hardware that includes a pulser, receiver, a data processor and wireless modem for communication. Data acquisition and signal processing software were developed to determine the water height using adaptive signal processing and data communication that can be controlled while the hardware is installed in a manhole. A statistical decision-making tool is being developed based on the field test data to determine the height of the condensed water height under high noise conditions and other environmental factors.

Wind turbine towers are in need of condition monitoring so as to lower the cost of unexpected maintenance. Wind loading from turbulence and gusts can cause damage in horizontal axis wind turbines even the supporting towers. Monitoring of wind turbines in service using embedded data sensor arrays usually is not targeted at the turbine-tower interaction from the perspective of structural dynamics. In this study the remote monitoring of the tower supporting a horizontal-axis wind turbine was attempted using a microwave interferometer. The dominant frequency of one tower was found to be decreased by more than 20% in 16 months. Numerical modeling using spectral finite elements is in progress and should provide further information regarding frequency shift due to stiffness variation and added mass. Expected outcome will contribute to remote monitoring procedures and nondestructive evaluation techniques for local wind turbine structures during operation.

1. Detection of visible crack, delamination etc. in composite structures can be fulfilled by several techniques. However, the problem is of greater complexity in the case of nonvisible defects such as barely visible impact damage and microcracks. The objective of this research work was to create and validate a low cost smart-sensor for NDT and structural health monitoring (SHM) to be used for complex geometries. The smart-sensor presents a dual function, i.e. it determines the presence of delamination and cracks within the cross-section but it also provides information on surface damages due to fatigue or impacts. In the latter case the damage could induce the breakage of the sensor that could still work with a different resonant frequency. The sensor utilizes a passive wireless resonant telemetry scheme based on an inductor capacitor (LC). The use of a passive system eliminates the need for onboard power and exposed interconnects, increasing the life of the device and the reliability due to the continuous operation even in case of damage results from the sensor. The sensor design, the signal processing and the experimental setup that validate the remote interrogation of the antenna sensor are presented. Two different designs were investigated, one for conductive surface and one for nonconductive surface (fiberglass-composite).

With the upcoming implementation of ceramic matrix composites (CMCs) within aerospace systems (e.g., aviation turbine engines), an in-depth understanding of the failure process due to mechanical loads is required. This includes developing a basic understanding of the complex, multi-mechanism failure process as well as the associated nondestructive evaluation (NDE) techniques that are capable of recognizing and quantifying the damage. Various NDE techniques have been successfully utilized for assessing the damage state of woven CMCs, in particular, consisting of silicon carbide fibers and silicon carbide matrices (SiC/SiC). The multiple NDE techniques, studied by the authors of this paper, included acousto-ultrasonics, modal acoustic emissions, electrical resistance, impedance based structural health monitoring, pulsed thermography as well as thermoelastic stress analysis. The observed damage within the composites was introduced using multiple experimental tactics including uniaxial tensile tests, creep tests, and most recently, ballistic impact. This paper offers a brief review and summary of results for each of the applied NDE tools.

Based on low coherence interferometry, a robust optical coherence tomography (OCT) system has been built. The system was used to monitor the growth of a delamination between the middle layers of a glass fiber composite under a static loading. Firstly specimens of the material used for the spar webs in wind turbines were prepared with an interlaminar crack from free edges. Then they were statically loaded by a customized tensile test stage to extend the delamination length and simultaneously scanned by the OCT system. To process the acquired data, an optimized signal processing algorithm was developed. The cross-sectional images clearly show the microstructure and the crack within the specimen. The 3D crack profiles show the application of OCT to determine the evolution of the crack structure inside the composite material during the propagation of the delamination, for the first time to the best of our knowledge.

Toward the goal of delamination detection and quantification in laminated composites, this paper examines guided wave propagation and wave interaction with delamination damage in laminated carbon fiber reinforced polymer (CFRP) composites using frequency-wavenumber (f-k) analysis. Three-dimensional elastodynamic finite integration technique (EFIT) is used to acquire simulated time-space wavefields for a CFRP composite. The time-space wavefields show trapped waves in the delamination region. To unveil the wave propagation physics, the time-space wavefields are further analyzed by using two-dimensional (2D) Fourier transforms (FT). In the analysis results, new f-k components are observed when the incident guided waves interact with the delamination damage. These new f-k components in the simulations are experimentally verified through data obtained from scanning laser Doppler vibrometer (SLDV) tests. By filtering the new f-k components, delamination damage is detected and quantified.

In this paper, we studied the microstrip patch antenna for the purpose of temperature sensing. The relationship between the antenna resonant frequency shift and temperature variation is first derived based on the transmission line model. A substrate material was selected to achieve a linear sensor response. Temperature chamber tests on patch antenna sensors bonded to three different test samples were carried out. Preliminary experimental results indicated a linear relation between the normalized antenna resonant frequency changes and temperature variations. However, a large discrepancy between the measured and predicted sensitivities was observed, which indicated that the thermal strain might have a significant influence on the dielectric constant of the substrate. To account for this effect, we introduced a strain coefficient of dielectric constant to quantify the effect of strain on the dielectric constant. With the modified theoretical predictions, the errors between the measurements and predictions were within the systematic error of the reference thermocouple, which validates the feasibility of using a microstrip patch antenna for temperature sensing.

Horizontal-axis wind turbines (HAWTs) are growing in size and popularity for the generation of renewable energy to meet the world’s ever increasing demand. Long-term safety and stability are major concerns related to the construction and use-phase of these structures. Braking and active pitch control are important tools to help maintain safe and stable operation, however variable cross-section control represents another possible tool as well. To properly evaluate the usefulness of this approach, modeling tools capable of representing the dynamic behavior of blades with conformable cross sections are necessary. In this study, a modeling method for representing turbine blades as a series of interconnected spinning finite elements (SPEs) is presented where the aerodynamic properties of individual elements may be altered to represent changes in the cross section due to conformability (e.g., use of a mechanical flap or a “smart” conformable surface). Such a model is expected to be highly valuable in design of control rules for HAWT blades with conformable elements. Sensitivity and stability of the modeling approach are explored.

To investigate a variety of nondestructive inspection technologies and assess impact damage characteristics in carbon fiber aircraft structure, the FAA Airworthiness Assurance Center, operated by Sandia National Labs, fabricated and impact tested two full-scale composite fuselage sections. The panels are representative of structure seen on advanced composite transport category aircraft and measured approximately 56”x76”. The structural components consisted of a 16 ply skin, co-cured hat-section stringers, fastened shear ties and frames. The material used to fabricate the panels was T800 unidirectional pre-preg (BMS 8-276) and was processed in an autoclave. Simulated hail impact testing was conducted on the panels using a high velocity gas gun with 2.4” diameter ice balls in collaboration with the University of California San Diego (UCSD). Damage was mapped onto the surface of the panels using conventional, hand deployed ultrasonic inspection techniques, as well as more advanced ultrasonic and resonance scanning techniques. In addition to the simulated hail impact testing performed on the panels, 2” diameter steel tip impacts were used to produce representative impact damage which can occur during ground maintenance operations. The extent of impact damage ranges from less than 1 in2 to 55 in2 of interply delamination in the 16 ply skin. Substructure damage on the panels includes shear tie cracking and stringer flange disbonding. It was demonstrated that the fiber optic distributed strain sensing system is capable of detecting impact damage when bonded to the backside of the fuselage.

Currently, the deviation between the model and an actual structure is generally identified through a so-called model updating process, in which a set of experimental measurement of structural dynamic response is used in combination with the model prediction to facilitate an inverse analysis that is usually deterministic. In reality, however, structural properties, such as mass and stiffness, are inevitably subject to variation/uncertainties. As such, the identification of property variations in a probabilistic manner can truly reveal the underlying physical characteristics of the structure involved. In this research, we adopt the Bayesian probabilistic framework to conduct stochastic model updating using measured vibratory response. Furthermore, this paper proposes an efficient scheme to facilitate such procedures by incorporating the Gaussian process and Markov Chain Monte Carlo (MCMC) into the Bayesian framework. The feasibility of this presented methodology is validated by case studies.

Much effort is devoted nowadays to derive accurate finite element (FE) models to be used for structural health monitoring, damage detection and assessment. However, formation of a FE model representative of the original structure is a difficult task. Model updating is a branch of optimization which calibrates the FE model by comparing the modal properties of the actual structure with these of the FE predictions. As the number of experimental measurements is usually much smaller than the number of uncertain parameters, and, consequently, not all uncertain parameters are selected for model updating, different local minima may exist in the solution space. Experimental noise further exacerbates the problem. The attainment of a global solution in a multi-dimensional search space is a challenging problem. Global optimization algorithms (GOAs) have received interest in the previous decade to solve this problem, but no GOA can ensure the detection of the global minimum either. To counter this problem, a combination of GOA with sequential niche technique (SNT) has been proposed in this research which systematically searches the whole solution space. A dynamically tested full scale pedestrian bridge is taken as a case study. Two different GOAs, namely particle swarm optimization (PSO) and genetic algorithm (GA), are investigated in combination with SNT. The results of these GOA are compared in terms of their efficiency in detecting global minima. The systematic search enables to find different solutions in the search space, thus increasing the confidence of finding the global minimum.

This research studies a substructure model updating approach. Requiring modal testing data from only part of a large structure (i.e. a substructure), finite element model parameters for the substructure can be updated. Prior to updating, Craig-Bampton transform is adopted to condense the entire structural model into the substructure (currently being instrumented and to be updated) and the residual structure. Finite element model of the substructure remains at high resolution, while dynamic behavior of the residual structure is approximated using only a limited number of dominant mode shapes. To update the condensed structural model, physical parameters in the substructure and modal parameters of the residual structure are chosen as optimization variables; minimization of the modal dynamic residual is chosen as the optimization objective. An iterative linearization procedure is adopted for efficiently solving the optimization problem. The proposed substructure model updating approach is validated through numerical simulation with two plane structures

The objective of this study was to evaluate the in-situ serviceability of cellulose fiber reinforced concrete (CFRC) pipes using Acoustic Emission (AE). Three-edge-bearing test was conducted on CFRC pipes in the laboratory in accordance with ASTM C497-05. Pipes were saturated in water for various periods of time to simulate the practical field situations of CFRC pipes with different service life exposed to moisture effects. AE sensors and LVDT were installed on pipes detect the damage phases during loading. Both monotonic and cyclic loading modes were conducted on the CFRC pipes. Results showed that the CFRC pipes generally exhibited the nonlinear load-displacement behavior before failure, yet the level of ductility was reduced with longer exposure to moisture condition. AE data were evaluated through two feature signals, that is, intensity analysis and calm ratio analysis. Results showed that AE can detect the onset of damage and the ultimate failure of CFRC pipes. Additionally, the analysis on CFRC pipes subjected to various moisture effects showed different features of AE results. It is found that intensity analysis can distinguish different periods of exposure to moisture and levels of ductility, which make it feasible to monitor the health of CFRC pipes under moisture effects.

Fluorescence method was used to detect the micro-damage caused by fatigue in a plain-woven carbon fiber reinforced polymer (CFRP). Fluorescence measurement is a method which estimates micro-damage by measuring fluorescent intensity change inside materials. The principle is, larger micro-damage means larger plastic strain, thus more space in that damaged spot which allows more fluorescent dyes coming in the material. By detecting fluorescent intensity in CFRP layer by layer using confocal laser microscopy, micro-damage can be estimated. Results show that there’s a good relationship between micro-damage and fluorescent intensity gradient.

This paper has been withdrawn by the publisher because it was not presented at the conference.

We present a methodology to assess slender beams by means of highly nonlinear solitary waves. This is accomplished by understanding the coupling mechanism between highly nonlinear solitary waves propagating along a granular system and a beam in contact with the granular medium. Nonlinear solitary waves are compact non-dispersive waves that can form and travel in nonlinear systems such as one-dimensional chains of particles. In the study presented in this paper, the waves are generated by the mechanical impact of a striker and are detected by means of sensor beads located along the chain. We investigated numerically and experimentally the effect on the solitary waves of slender beams of different modulus, length, boundary condition, and axial stress. We found that the geometric and mechanical properties of the beam or thermal stress applied to the beam alter certain features of the solitary waves. In the future, these findings may be used to develop a novel sensing system for the Nondestructive Evaluation of beams.

With continual improvement in computing power and software codes that simulate multiple physical effects, complex analyses can be performed that allow for more accurate modeling of real world systems. Here, a general purpose finite element (FE) code was utilized to conduct a rotordynamic assessment of a rotor system containing a flexible disk. Typically, specialized rotordynamic software packages make numerous assumptions to simplify the various types of rotor response calculations. Disks, for example, are commonly assumed rigid and are represented by lumped masses or discrete beam elements. Such idealizations may cause inaccuracies when calculating critical speeds for rotor systems that involve a relatively flexible disk. By utilizing a general purpose FE approach, where multiple rotational effects are considered, a more accurate model can be developed that includes the dynamic contributions of a flexible disk. This paper illustrates the rotordynamic analysis of a generic, yet realistic, compressor with a shrouded impeller model, without extensive geometric simplification. Furthermore, through the utilization of the fully featured geometry, several dynamic effects are demonstrated to have a significant influence on the rotor system’s Campbell diagram. The dynamic effects investigated include disk flexibility, stress stiffening, and spin softening. It is shown that neglecting any of these may cause significant errors regarding the rotordynamic analysis predictions.

This paper presents a novel algorithm for detection of multiple flaws in structures as an inverse process, where the forward problem is based on eXtended Finite Element Method (XFEM). The proposed algorithm can be applied to quantify any flaw with arbitrary shape and size (e.g., cracks, voids, or their combination) whose number is unknown beforehand, and is shown to be significantly more efficient than other methods proposed in the literature. The basic concept is to employ a two-scale optimization framework, where first a coarse flaw region is detected and then fine scale convergence is used to zoom in on the flaw. Both optimization steps rely on a forward problem in which an XFEM model with both circular and elliptical enrichments is used. The advantage of XFEM is in the alleviation of costly remeshing techniques when candidate flaws keep updating with the optimization process. The proposed hierarchical optimizers include both heuristic and gradient-based algorithms, such as the discrete artificial bee colony (DABC) algorithms and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. In details, the first step employs a DABC optimization as a coarse-scale search where the optimizer is limited to specific solutions that correspond to locations and shapes of flaws, thus converting a continuous optimization problem into a discrete optimization with a small number of choices. The results of the first step provide local subdomains and rough identified flaw parameters, which can be considered as search space reduction and initial guess for a fine tuning optimization step. This solution zooming is carried out by the BFGS method and leads to a fast converging method as illustrated on two benchmark detection examples.

In this paper we present theoretical results on an acoustic metamaterial beam and a bar that exhibit negative effective mass and negative effective stiffness. A one-dimensional acoustic metamaterial with an array of spring-mass subsystems was fabricated. The frequency of the acoustic one dimensional metamaterial structure has the same form as that of the permittivity in metals due to the plasma oscillation. We also provide a theory to explain the simulation results. And we use the concept of conventional mechanical vibration absorbers to reveal the actual working mechanism of the acoustic metamaterials. We explain the two vibrate modes which are optical mode and acoustic mode in detail. When the incoming elastic wave in the acoustic metamaterials to resonate the integrated spring-mass-damper absorbers to vibrate in their optical mode at frequencies close to but above their local resonance frequencies to create shear forces and bending moments to straighten the beam and stop the wave propagation. Moreover, we explain the negative parameter in acoustic metamaterials.

Paper was removed post-meeting since it was not presented at the conference in accordance with our onsite/post-meeting book process

The verification of kissing bond defects with conventional nondestructive testing techniques is generally unsuccessful because kissing bonds behave very similar to an intact adhesive. Therefore, one has to apply a suitable method such as loading the adhesive joint using laser-generated shock waves in order to make this kind of defect detectable. The present work describes the results of experimental studies on nondestructive inspection of bond flaws in bonded joints of metal materials. While it is not the purpose of this work to evaluate or characterize shock waves, we demonstrate that kissing bond flaws can be made visible nondestructively using laser generated shock waves.

The purpose of this research is to estimate of the cover depth of steel fiber reinforced concrete using the impact-echo testing. In order to evaluate the security of the construction, usually need to estimate the cover depth of the reinforced concrete. At present, the examination technique of the cover depth of the reinforced concrete without the steel fiber is mainly applied in the magnetic and electrical methods, its rapid detection and good results. But the research of the reactive powder concrete be gradually progress, with the steel fiber concrete structure will be increased, if should still operate the examination with the magnetic and electrical methods, theoretically the steel fiber will have the interference to its electromagnetism field. Therefore, this research designs four kinds of reinforced concrete plate that include different steel fiber contents, to evaluate test results of estimate of the cover depth of the reinforcing bar. The results showed that: estimate of the cover depth of steel fiber reinforced concrete reinforcing bar using the impact-echo testing, the variety of the steel fiber content does not have much influence, the test measurement error within ± 10%, and the most important source of uncertainty is the velocity of concrete.

The past decade has seen new, emerging innovation of Ultrasonic Testing (UT). Specifically, multiple manufacturers have produced Phased Array Ultrasonic Testing (PAUT) systems. The PAUT systems embed a matrix of multiple (some up to 128) single transducers into one probe used for scanning elastic materials. Simultaneously exciting multiple transducers offers distinct advantages; depending on the sequencing of transducer excitation, the ultrasonic beam could be steered within the material and multiple beams help develop extra dimensional data to assist with visualization of possible flaws including the discontinuity size, shape and location. Unfortunately, there has not been broad acceptance of PAUT in the bridge fabrication industry because it is currently not a recognized inspection technology in AWS D1.5. One situation in which the technology would excel would be inspection of Complete Joint Penetration (CJP) butt welds. Currently, AWS D1.5 required CJP welds subjected to tensile or reversal stresses only be inspected by Radiographic Testing (RT). However, discontinuities normally seen by RT can also be seen with PAUT. Until specification language is adopted into D1.5, there will continue to be hesitancy to use PAUT for the inspection of CJP butt welds. Developmental work must first be performed to develop the acceptance criteria and the specification language. The developmental work from the inspections carried out on butt-weld specimens and transition butt-weld specimens are presented in this paper. Specific scan plans were developed using the ES-Beam simulation software for each of the test specimens. In depth analysis of PAUT data obtained to determine exact location and sizing information of the defects was performed. The results also present the comparison of results from PAUT to those obtained using conventional UT and radiography.

Moisture ingression creates huge concerns on fiber reinforced composites because of the reduction in mechanical strengths with the moisture absorption and additional weight gains on the aircraft. If the appropriate techniques are used to predict the moisture ingression in the laminate composite structures, it will be of an immense advantage to detect moisture contents and their effects on the properties of the composites. Mechanisms of moisture influence in the composite structures also need to be thoroughly understood for a better design of new structural composites. This research study aims at investigating the effectiveness of different non-destructive inspection (NDI) techniques to detect moisture absorption. Carbon and glass fiber reinforced composite panels were prepared and used during the moisture ingression tests. Different NDI techniques, including ultrasonic through transmission and pulse echo, phase array, bondmaster and digital tap hammer were chosen to determine the moisture ingression levels. Throughout the tests, initial and final weights of the composite panels were determined using the high accuracy balances. NDI scanning parameters were kept constant for the tests. The effectiveness of each technique was determined and their results were discussed in detail.