This PDF file contains the front matter associated with SPIE Proceedings Volume 10598, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

It is expected that bridge inspection using unmanned aerial vehicles (UAVs) equipped with imaging devices is able to improve public safety and structural reliability by providing the close detail of a bridge appearance. Thus, interests in bridge inspection using UAVs are increasing worldwide. However, at present, most of them simply use commercially available UAVs to acquire images of parts of the bridge that are difficult to access (e.g., upper parts of the pylons in long-span bridges). It cannot meet the final goal of bridge inspection that is to assess the condition of the bridge. Therefore, this approach is still considered to be at an early stage from a practical point of view and a more systematic and reliable approach is needed. In this paper, challenging issues of bridge inspection using UAVs are identified and their solutions are presented. To this end, a recently launched research project is introduced by describing the developing core technologies such as a new UAV-localization algorithm without GPS, noncontact inspection techniques based on data fusion of hybrid images and a bridge condition evaluation technique based on the processed data originally obtained from UAVs. Some interim results of field tests are also presented.

Civil infrastructure is important to ensure the ongoing functionality of human living environments. However, in North America, much of the infrastructure is aging and requires continuous monitoring and maintenance to ensure the safety of people. Traditionally, visual inspection has been carried out to monitor the health of such structures. However, assessments require trained inspectors, and monitoring methods are difficult due to the size and location of the infrastructure. Recently, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has been growing in popularity, and research has been conducted concerning the use of UAVs for the visual inspection of infrastructure. However, UAV inspection requires skilled pilots and the use of a global positioning system (GPS) for autonomous flight. Unfortunately, for some locations, a GPS signal cannot be reached for autonomous flight of the UAV. For example, the GPS signal on the inside of a building or underneath a bridge deck is unreliable, but these locations also require inspections to ensure structural health. In order to address this issue, autonomous UAV methods using ultrasonic beacons have been proposed. Beacons are able to provide positional data allowing UAVs to perform the autonomous mission. As an example of structural damage, we report the successful detection of concrete cracks using a deep convolutional neural network by processing the video data collected from an autonomous UAV.

Self-sensing nanocomposites hold immense potential for structural health monitoring (SHM) because their electrical conductivity is influenced by mechanical effects such as strain and damage. This property, known as piezoresistivity, has been leveraged by numerous researchers for damage detection. However, from a SHM perspective, it would be much more beneficial to know the stresses that precipitate failure so that mitigating actions can be taken. Herein, we propose a novel method of accomplishing this based on the concept of piezoresistive inversion. Using simulations, the conductivity of a deformed piezoresistive nanocomposite is first determined using electrical impedance tomography (EIT). Next, the piezoresistive inversion process is used to determine the displacement field that gives rise to the conductivity obtained via EIT. Strains are then determined from kinematic relations and stresses from constitutive relations. A suitable failure criterion is then used to predict the location and likelihood of failure. Using these simulations, we demonstrate that the proposed approach allows for the accurate localization and quantification of stress concentrations which may induce failure. Because of these damage prediction capabilities, this approach has the potential to enable unparalleled predictive SHM capabilities.

In recent years, interest has grown in the development of sensing skins for structural health monitoring (SHM) with electrical impedance tomography (EIT) used to image skin properties. The computational e ort associated with the inverse solver of EIT is very large and sometimes the final reconstructed strain map derived does not correspond to the true state of the system. To reduce the large computational effort associated with EIT reconstruction of large unpatterned thin films, this study fabricates a patterned spatial strain sensor made from single-walled carbon nanotube (SWCNT) nanocomposite films. The thin nanocomposite strain sensing films are fabricated on a flexible polyimide substrate by using a layer-by-layer (LbL) deposition process. Rather than using a plain/unpatterned film as previously proposed in EIT-based approaches, a grid of piezoresistive nanocomposite strip elements are patterned. The 2D grid arrangement of the nanocomposite sensing elements is achieved using optical lithography. Metal electrodes are deployed at the boundary nodes by physical vapor deposition (PVD) and are connected to wires for controlled current injection and electric potential measurements. In order to infer the strain distribution of a structure using the patterned strain sensing skin, there is a need to have a robust inverse solver. Ohm's and Kircho's laws are used to derived the Neumann-to-Dirichlet map of the rectangular resistor network. The Neumann-to-Dirichlet operator is represented by a matrix which is a function of the resistance distribution. The inverse solution obtains the resistance of each grid element by updating the Neumann-to-Dirichlet operator to drive convergence between the boundary potential measurement taken of the film and those predicted by the forward solution. The inverse solver has been validated by simulations and experiments of a square resistor network of 3 by 3 (node by node) and 4 by 4 resistor grids. Preliminary results show that the proposed inverse solver is accurate for 2D strain measurement using the patterned strain sensing grid.

Nanocomposites exhibit remarkable electromechanical properties and have potential applications in sensing and actuation. In this work, carbon nanotubes (CNTs) - epoxy nanocomposites are fabricated with the addition of graphite nanoplatelets (GNPs). An improvement in piezoresistivity is observed with the combination of CNTs and GNPs, compared to the use of only CNTs, which indicates the formation of an efficient hybrid conductive networks for strain and electrical transfer in the materials. We investigate the effect of static mechanical loading on the electrical sensing performance of the nanocomposites. The inter-particle distances between the fillers change in the event of applied loading, which leads to a modification of the CNT-GNP hybrid percolated network and hence results in a change of the electrical conductivity. This phenomenon is exploited to use the hybrid composites as strain sensors. Specifically, different matrix materials are tested to investigate their effects on the mechanical and sensing performance of the nanocomposites. In addition, numerical simulations are performed to model the strain sensing performance of the nanocomposites. The effect of the type of matrix on the sensing performance of the nanocomposites is predicted and compared with the experimental results.

Typical airliners operate in a range of conditions, hence airborne structural health monitoring (SHM) components, must withstand the relevant environmental conditions. Additional to the integrity of the components, the SHM performance (diagnosis and prognosis) must be robust and reliable under environmental and vibration profiles during operation. This work investigates the influence of the operational condition (including temperature, humidity and vibration loads) on the integrity of a piezoelectric based SHM system in terms of integrity of the system and robustness of the diagnosis for detecting barely visible impact damage (BVID) on a CFRP panel. Consequently, compensation techniques are proposed to remove the effect of the environmental loading on the decision-making algorithm. The validity of the proposed algorithm is demonstrated on a composite plate for the operational profile defined in MIL-STD 180G standard for airborne components of a regional aircraft.

Composite materials are commonly used in many branches of industry. One of the effective methods to join CFRP parts is the adhesive bonding. There is a search of effective methods for quality assurance of bonded parts. There is a need for pre- and post-bond inspection to ensure proper bonding and verify its quality. Research reported here focuses on post-bond inspection of bonded CFRP parts. In this paper we report investigations of samples that were modified with contamination that can be encountered during the manufacturing process of the CFRP parts. The contaminations were introduced before adhesive bonding, and the effect of the contamination on the quality bond is studied. First of the investigated cases was release agent contamination prepared by dip-coating of clean CFRP plates. The release agent is used during the production of composite elements and can contaminate the surface to be bonded. The second case was the moisture contamination. It was obtained by conditioning of the samples in humid conditions. Moisture contamination can be gained from water-coupled ultrasonics or during transportation of unprotected parts. The third type of contamination had more local character. It simulated fingerprints. Artificial sweat was used. The fingerprint contamination can be caused by improper handling of the parts. Apart from single contamination, also mixed contamination cases were studied, as well as curved samples. The samples were studied in non-destructive approach. It was shown that for some of the cases the detection is possible.

Composite materials are commonly used in many branches of industry. One of the effective methods to join CFRP parts is to use adhesives. There is a search of effective methods for quality assurance of bonded parts. There is a need for pre- and post-bond inspection to ensure proper bonding and verify its quality. Research reported here focuses on post-bond inspection of bonded CFRP parts. In this paper we report investigations of samples that were modified in order to simulate the conditions that can be encountered during the bonding repair processes. The modifications were made before adhesive bonding, and their effect on the quality of the bond is studied. The first case was the thermal treatment. It was made by exposure of samples to elevated temperatures. This case accounts for parts that may be exposed to external heat source or lightening impact. Second of the investigated cases was deicing fluid contamination prepared by dip-coating of clean CFRP plates. When cleaning the aircraft for a repair this fluid can be transported to bonding areas and weaken the joint. The third type of modification was faulty curing of the adhesive. It was prepared by local pre-curing of the adhesive. Pre-curing causes irregularities in the curing of the adhesive joint. Apart from single modifications, also mixed cases were studied, as well as scarf bonding. The samples were studied in non-destructive approach. It was shown that for some of the cases the detection is possible.

This paper explores the feasibility of using ultrasonic guided Lamb waves to characterise the type and through thickness severity of damage present in composite plate-like structures. Two cases were considered, the first compared isolated subsurface delaminations between plies whilst the second case looked at more complicated barely visible impact damages caused by a low velocity impactor. In this study, the ultrasonic guided Lamb waves were generated by a surface mounted piezoelectric transducer and were sensed by a Laser Doppler Vibrometer. This allowed full wavefield imaging of the Lamb wave interaction with damage without the need for a previously acquired damage free baseline signal. In order to save time and improve the signal to noise ratio, the narrowband toneburst signals are reconstructed from a singular chirp response and a post-processing algorithm. Both cases showed similar results in that the first symmetric mode, S₀, which is dominant at higher frequencies, caused mode conversions when interacting with the defects whilst the first anti-symmetric mode, A₀, dominant at lower frequencies, mainly caused a change in phase and amplitude across the defects. Both cases also showed that as the damaged area got more severe, the effects of the damage on both modes became more pronounced.

Extensive studies have been conducted to examine the use of piezoelectric transducers and other ultrasonic devices for structural health monitoring applications due to their high electroacoustic efficiency and simple operation principles. Most studies use guided Lamb wave inspection methods to detect, locate, and characterize damage in relatively simple plate-like structures. These studies have proven that this type of technique is useful for structural health monitoring in the simple structures, but little work has shown feasibility when using more realistic test articles. In our study, we present a sparse array technique using multiple piezoelectric transducers mounted to an external composite panel on a legacy F/A-18 wing section to investigate its capabilities for more realistic structural health monitoring applications. The panel is secured to the wing by a series of bolts surrounding the panel, and as the main method of simulating damage to the wing, these bolts are loosened by a specified torque in several different cases. The guided Lamb wave pitch-catch inspection method is used to identify such simulated damage by comparing baseline and damaged trials using differential analysis and a spatial mapping approach. This approach requires an estimate of the Lamb wave mode group velocities in the composite panel along with the geometry of the transducer array and the transducers location metrics. Despite the complexities of using a realistic test article, the sparse piezoelectric array combined with the Lamb wave inspection method shows promise in its ability to accurately detect, locate, and characterize the simulated damage to the airframe.

Jointed composite structures (JCSs) are often used in the marine, automotive and civil engineering industries. In JCS, thin carbon-fiber-reinforced composite laminates are bonded with epoxy adhesives. But, disbonds can occur at the bondinterphase due to variable environmental conditions, cyclic loading, aging, fatigue, amongst others, which may lead to a substantial reduction in load-bearing capacity of the structural assembly. Hence, it is essential to identify these hidden disbonds, and the identification becomes more challenging due to frequent change in ambient temperatures. It is found that the ultrasonic guided wave propagation based inspection technique is suitable for inspection of such complex multilayered structures. The aim of this paper is to investigate the disbond effects on the propagating wave modes in the JCS under variable ambient temperatures. Towards this, a series of finite element based numerical simulation of guided Lamb wave propagation in JCS under variable temperature is carried out in ABAQUS using piezoelectric actuator-sensor transducers. Laboratory experiments are then conducted to investigate the disbond effects and a good agreement is found between the simulation and experimental results.

Fiber-reinforced polymer (FRP) composites are widely used in the aerospace industry, but they are also susceptible to different types of damage. In particular, impact is a serious concern, since they can cause subsurface damage that can be hidden from visual inspection and jeopardize structural safety and performance. In addition, the complex geometries of FRP components can limit the use of current nondestructive inspection techniques. Thus, the objective of this study is to develop a portable, noncontact, surface scanning tool for technicians to rapidly detect, characterize, and localize subsurface structural features and damage in structural components. The technique is centered around the theory of electrical capacitance tomography (ECT). Unlike conventional ECT, an array of noncontact electrodes is arranged in a planar fashion. Electric field is propagated between electrodes to interrogate the sensing region, which is defined as the region of space underneath the plane of electrodes. Meanwhile, the corresponding mutual capacitance between excitation and other electrodes are measured. The recorded datasets are then used as inputs to solve the ECT inverse problem in which the electrical permittivity of the 3D sensing region can be reconstructed. In this work, numerical modeling was employed to demonstrate the sensing performance of the planar array capacitive imaging system. First, several composite components with different types of damage were simulated using finite element modeling. Damage was introduced by selectively changing the electrical permittivity at different regions. Second, the planar capacitive electrode array, which was placed near the composite component, was also modeled. Electric field was propagated between different electrodes while the corresponding mutual capacitance of other electrodes were determined. The simulated data was then used as inputs to solve the ECT inverse problem. The numerical simulation results showed that the planar array capacitive imaging system was able to detect, quantify, and locate damage-induced changes in electrical permittivity distributions in composite structures, thereby demonstrating its potential for use as a nondestructive inspection tool.

A distributed fiber optic strain sensor based on Rayleigh backscattering, embedded in a fiber-reinforced polymer composite, has been demonstrated. The optical frequency domain reflectometry (OFDR) technique was used to analyze the backscattered signal. The shift in the Rayleigh backscattered spectrum (RBS) was observed to be linear to the change in strain of the composite material. The sensor (standard single-mode fiber) was embedded between the layers of the composite laminate. A series of tensile loads were applied to the laminate using an Instron testing machine, and the corresponding strain distribution of the laminate was measured. The results show a linear response indicating a seamless integration of the optic fiber in the composite material and a good correlation with the electrical-resistance strain gauge results. In this study, distributed strain measurements in a composite laminate were successfully obtained using an embedded fiber optic sensor.

Regularly inspecting the components inside nuclear power plants is necessary to ensure their safe performance. Current inspection practices, however, are time consuming, tedious, and subjective that need technicians to watch videos and manually annotate cracks. While an autonomous inspection approach is desirable, state-of-the-art crack detection algorithms cannot perform well since the cracks on nuclear power plant reactors are typically very tiny with low contrast. The existence of scratches, welds, and grind marks on the surface makes the autonomous detection even more challenging. This study introduces a new framework that consists of a convolutional neural network (CNN) based on deep learning, a spatiotemporal registration process, and a Nave Bayes data fusion scheme based on statistics. The proposed framework is evaluated using several inspection videos and achieves 98.3% hit rate against 0.1 false positives per frame where the hit rate is much higher than other state-of-the-art algorithms.

This study introduces an innovative non-contact sensing technique for vision-based displacement measurement. Existing vision-based displacement measurement techniques utilizes physical target panels or physical features to compute relative displacement between the target and the observation point. Instead, the proposed method exploits the optical reference of a speckle pattern. A coherent light that is diffusely reflected on the surface of the target structure creates the speckle pattern. In this study, a camera records the changes in the speckle pattern in real time. Because the speckle pattern is sensitive to small changes of surface, the ambient vibration is enough to affect it. To estimate the displacement of the target from the raw speckle images, speckle contrast imaging (SCI), speckle flow imaging (SFI), and k-means clustering algorithm were used. After SCI and SFI quantifies the blurring effect in each image, the k-means clustering algorithm creates virtual sensing node from each image. The connection of virtual nodes from frame to frame highlights the displacements of the surface in time domain. Because the algorithms are time-consuming and computationally intensive, a GPU executes the entire post-processing operation in parallel and identifies the natural frequencies of the structure.

This paper presents an existing face detection method using cascade features updated for determining the cracks on concrete surfaces. The main goal of structural health monitoring (SHM) is to safeguard our existing structures from cracks, corrosion, delamination, and spalls due to incessant use of structures. Cracks are the foremost defect that will occur in the structures, and they require quick attention before they lead to structural failure; it is a laborious job to detect the cracks using personnel (visual inspection) practices, which produce highly unreliable results. The results of contact sensor-based crack detection techniques, however, mainly depend on parameters such as temperature, sensitivity, accessibility, etc. Recently there has been high expansion in computer vision (image processing) techniques that facilitate the detection of cracks. In this study, a modified cascade face detection technique based on the Viola-Jones algorithm is proposed to detect cracks in concrete walls. Cascade features calculated from the Viola-Jones algorithm are trained on positive and negative datasets of images with and without cracks. Once training is completed, the Viola-Jones algorithm spots the cracks on test images with bounding boxes drawn around the region of the cracks.

Considering the limitations of the current structure health monitoring techniques, a novel low-cost capacitive transducer (CT) using capacitance signals, which has the potential to provide accurate health assessment and damage prediction for reinforcement concrete structures, is firstly developed. Four major works including the development of the capacitive sensor, the optimization work of CT sensor design, the application of the CT in rebar size/depth/position and rebar corrosion tests using the developed CT are mainly introduced. Results have shown that the developed CT with innovative capacitance measuring method has great accuracy in predicting the rebar size and position inside a large structure, which is the foundation of the reinforcement corrosion detection. It can be concluded that the developed capacitive transducer is applicable in the condition tests of reinforced concrete structures and it paves the way for evaluation of the corrosion degree of the reinforced rebar.

With the development of wireless technology, active structural vibration control based on wireless sensor network has possibly tended to replace the traditional wired control method to an extent. Compared with wired control, wireless control has the characteristics of low cost, flexibility and easy implementation. In this paper, in order to further improve the flexibility and robustness of the wireless control system, the three-layer steel structure model is designed as the research object, the fuzzy PID control algorithm is developed, the high flexibility DSP is used for the controller, and the distributed wireless control experiment is done finally. The experimental results show that the fuzzy PID control algorithm can restrain the vibration of the structure well under the given initial state and earthquake excitation, and that the developed wireless distributed control experimental system is validated and has various potential applications in industrial control systems.

Distortion-induced fatigue cracks caused by differential deflections between adjacent girders are common issues for steel girder bridges built prior to the mid-1980s in the United States. Monitoring these fatigue cracks is essential to ensure bridge structural integrity. Despite various level of success of crack monitoring methods over the past decades, monitoring distortion-induced fatigue cracks is still challenging due to the complex structural joint layout and unpredictable crack propagation paths. Previously, the authors proposed soft elastomeric capacitor (SEC), a large-size flexible capacitive strain sensor, for monitoring in-plane fatigue cracks. The crack growth can be robustly identified by extracting the crack growth index (CGI) from the measured capacitance signals. In this study, the SECs are investigated for monitoring distortion-induced fatigue cracks. A dense array of SECs is proposed to monitor a large structural surface with fatigue-susceptible details. The effectiveness of this strategy has been verified through a fatigue test of a large-scale bridge girder to cross-frame connection model. By extracting CGIs from the SEC arrays, distortion-induced fatigue crack growth can be successfully monitored.

The current technology push to connect everyday objects via the “internet of things” has fueled significant advances in low-power processing and communication devices. One aspect of these connected products that still needs attention is the means by which they are powered. An attractive and feasible option is the use of energy harvesting from acoustic fields. Previous work developing a piezoelectric energy harvesting device has generated 2.6 mW of power from a hydraulic test rig. This paper presents an implementation of an energy harvesting device connected to a communications system that allows it to store energy and communicate sensor readings via Bluetooth Low Energy.

A capacitance based large-area electronics strain sensor, termed soft elastomeric capacitor (SEC) has shown various advantages in infrastructure sensing. The ability to cover large area enables to reflect mesoscale structural deformation, highly stretchable, easy to fabricate and low-cost feature allow full-scale field application for civil structure. As continuing efforts to realize full-scale civil infrastructure monitoring, in this study, new sensor board has been developed to implement the capacitive strain sensing capability into wireless sensor networks. The SEC has extremely low-level capacitance changes as responses to structural deformation; hence it requires high-gain and low-noise performance. For these requirements, AC (alternating current) based Wheatstone bridge circuit has been developed in combination a bridge balancer, two-step amplifiers, AM-demodulation, and series of filtering circuits to convert low-level capacitance changes to readable analog voltages. The new sensor board has been designed to work with the wireless platform that uses Illinois Structural Health Monitoring Project (ISHMP) wireless sensing software Toolsuite and allow 16bit lownoise data acquisition. The performances of new wireless capacitive strain sensor have been validated series of laboratory calibration tests. An example application for fatigue crack monitoring is also presented.

The primary method of structural health monitoring is human-based visual inspection, which—despite its limitations of consistency and accessibility—can warn about changes in a bridge’s condition. To improve the visual inspection of civil infrastructure and address these drawbacks of human-oriented inspection, computer vision-based techniques have been developed to detect structural damage in images. Most of these methods, however, detect only specific types of damage, such as cracks in concrete or steel. Another drawback is that the traditional convolutional neural network-based damage detection method is not able to provide the location of the detected damage. To provide quasi-realtime simultaneous detection and localization of multiple types of damage, a structural damage detection method based on Faster Regionbased Convolutional Neural Network (Faster R-CNN) is proposed. The original architecture of Faster R-CNN is modified, trained, validated, and tested for this study. The robustness of the trained Faster R-CNN is evaluated and demonstrated using seven new images taken of various structures.

Bolted steel joints are one of the most common types of connections in steel structures. Due to significant loads carried over long-term operation, bolted steel joints are prone to structural damage. Monitoring bolted steel joints is critical to ensure their functionality and structural safety. Among all factors related with joint damage, bolt loosening has been reported as a main cause of the damage of bolted joints. Detecting bolt loosening is therefore critical for the heath assessment of bolted steel joints. Recently, computer vision-based structural health monitoring (SHM) methods have been proposed in many research fields due to the benefits of being low-cost, easy-to-deploy, and contactless. In this study, we propose an image-based feature tracking approach to detect bolt loosening in steel connections. The method relies on a feature tracking algorithm, through which densely distributed feature points can be automatically detected and tracked from multiple images taken at different times. A novel algorithm is established to rapidly search feature points and track the movement of these feature points between images. If the bolt is loosened, feature points associated with the loosened bolt would exhibit a unique rotational movement pattern. By highlighting these feature points, the loosened bolt can be successfully localized. The effectiveness of the proposed approach was verified by a laboratory test of a steel joint using a consumer-grade digital camera.

Fatigue cracks developed in metallic materials are of critical safety concerns for mechanical, aerospace, and civil engineering structures. For fracture-critical structures, if not appropriately inspected, excessive growth of fatigue cracks can lead to catastrophic structural failures. Current crack detection technologies developed for nondestructive testing (NDT) or structural health monitoring (SHM) often require costly equipment, extensive human involvement, or complex signal processing algorithms. Recently, computer vision-based methods have shown great promise in damage detection for being contactless, low cost, and easy-to-deploy. In this paper, we propose a novel computer vision-based method for detecting fatigue cracks in a video stream. This method is based on tracking the surface motion of structural members under crack opening and closing, and identifying fatigue cracks by extracting discontinuities in the surface motion caused by cracking. The effectiveness of this method was validated through an experimental test of a steel compact, C(T), specimen. Results indicate that the proposed approach can robustly detect the fatigue crack under ambient lighting condition, despite the crack was surrounded by other crack-like edges, covered by complex surface textures, or invisible to human eyes under crack closure.

Structural health monitoring has become an outstanding tool to perform structural condition assessments, once performed solely by trained experts. In this study, a methodology utilizing an inexpensive depth sensor to detect and quantify volumetric damages within concrete surfaces is proposed. To allow automatic damage detection, a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based method is implemented. A database of 444 images with resolution of 853×1440 pixels annotated for concrete spalling is developed. The network is modified, trained and validated using the proposed database. Damage quantification is automatically performed using the depth data output by the sensor. The surface of the analyzed element is extracted by merging the bounding boxes output by the Faster R-CNN onto the depth map. A polystyrene test rig containing damage simulations of known volume was utilized to test the accuracy of volume calculation. In addition to that, a concrete beam was also used to test the entire system. The Faster R-CNN yielded an average precision (AP) of 77.97% for damage detection. Damage quantification routine presents error of 9.45% in volume quantification of samples located within 100 cm and 250 cm away from the sensor plane. On top of that, maximum depth measurements of damages show a mean precision error (MPE) of 3.24% considering the same distance range. The implemented method allows for damage segmentation and quantification regardless of the distance between the sensor and the analyzed element.

This study presents a novel methodology for structural health monitoring (SHM), using a self-powered sensing concept, within the context of machine learning (ML) and pattern recognition (PR). The proposed method is based on the interpretation of data provided by a self-powered discrete analog wireless sensor used to measure the structural response along with an energy-efficient pulse switching technology employed for data communication. A system using such an energy-aware sensing technology demands dealing with power budgets for sensing and communication of binary data, resulting in missing and incomplete data received at the SHM processor. Numerical studies were conducted on an aircraft wing stabilizer subjected to dynamic loading to evaluate and verify the performance of the proposed methodology. Damage was simulated on a finite element model by decreasing stiffness in a region of the stabilizer’s skin. Several features, i.e., patterns or images, were extracted from the strain response of the stabilizer. The obtained features were fed into a ML methodology incorporating low-rank matrix decomposition and PR for damage diagnosis of the wing. Different ML algorithms, including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within the learning methodology to assess the performance of the damage detection approach. Different levels of harvested energy were also considered to evaluate the robustness of the damage detection method with respect to such variations. Further, reliability of the proposed methodology was evaluated through an uncertainty analysis. Results demonstrate that the developed SHM methodology employing ML is efficient in detecting damage from a novel self-powered sensor network, even with noisy and incomplete binary data.

Microstrip patch antenna sensors have been demonstrated for strain, temperature, crack, moisture sensing etc. These antenna sensors are attractive because of their simple configuration, low profile, and conformability. Most of these studies, however, were carried out in lab settings using a specialized microwave instrument, which is expensive and has a low interrogation speed. This paper presents the development of a low cost interrogation system that can dynamically interrogate the antenna sensor and wirelessly transmit the acquired data to a smart device. The hardware implementation of the interrogation system as well as it characterization using a test apparatus imitate a prosthetic socket are presented. We demonstrated that the interrogation system can extract the resonant frequencies of the antenna sensors at real time.

Earthquakes have great impact on civil infrastructure, and the consequence of these events can be catastrophic. Not only are such damaging events rare, but they are unpredictable. Therefore, detection and early mitigation of damage is a critical issue to ensuring safety of civil infrastructure under earthquakes. One of the challenges to earthquake monitoring systems for structures is cost. Wireless sensors offer tremendous opportunity to reduce cost and realize the dream of pervasive sensing. However, earthquake monitoring of civil infrastructure using wireless smart sensors (WSS) is challenging. WSSs typically employ some form of duty cycling to reduce power consumption; hence, they will miss earthquakes if the sensor is in sleep mode when they occur. Demand-based WSSs meet the requirements of always-on earthquake monitoring within a minimal power budget by integrating an ultra-low power trigger accelerometer with a high-fidelity WSS platform and combining the beneficial aspects of both. In particular, the approach is able to rapidly turn on the WSS upon the occurrence of earthquakes and seamlessly transition to high-fidelity data acquisition. Afterwards, post-sensing data fusion is conducted to integrate the data obtained from the two sensors without losing any critical data from the triggering event. A laboratory test is presented to verify the efficacy of the proposed idea. The results show that the demand-based WSSs are able to capture the occurrence of earthquake and provide high-fidelity data for structural condition assessment in a timely manner.

Modern medicine is undergoing a revolution in the application of new sensor capabilities for aiding in diagnosis of specific conditions and monitoring a variety of informative vital signs. In the past, many of the measurements were limited by what could be accomplished externally. A shift toward in-vivo monitoring for both diagnostic and therapeutic sensing and actuation [1] has created a need for low power electronics, high energy density batteries and methods to successfully power devices embedded in the body. For a review of the field and sensing capabilities see [2]. Recent studies suggest that charging with ultrasound is more efficient at longer transmission distance (< 10cm) than inductive charging [3]. In this manuscript, we discuss the modeling and experimentation that we have accomplished and demonstrate in ultrasonic charging of sensors having the form and fit of in-vivo sensors. The task goal has been to use piezoelectric transducers for wireless communication and powering of sensors internal to the human body with a goal to transmit power levels of 100 μW a receiver with receiving area of 3x3 mm² over a distance of 16 cm equivalent to human body interior. The results suggest that we can transmit power levels that exceed this baseline requirement.

In this paper, we developed techniques to identify and quantify the damage (crack) to bridges based on images obtained by the unmanned aerial vehicle (UAV). The scope of the research includes image acquisition using UAV, the classification system of crack based on Deep-learning and algorithms of detection and quantification using improved Image Processing Techniques (IPTs). A conventional crack detection method using only IPTs can be applied marginally according to the image acquisition environment (lights, shadows, etc.), so we proposed the techniques based on Deep-learning to find the crack part in the region of interest (ROI) from the other types of damage or non-crack. After classifying the crack part in the ROI, improved IPTs are applied to the detected regions to quantify cracks at 300 micrometers. Performances of the technique were evaluated through preliminary test and field test. The non-contact bridge damage detection technology using UAV can be applied to the actual bridge inspection field It is expected to have more performance than existing bridge inspection methods.

This paper presents a deep learning-based concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared (IR) thermography images are able to improve crack detectability while minimizing false alarms. Large scale concrete-made infrastructures such as bridge, dam, and etc. can be effectively inspected by spatially scanning the hybrid imaging system including vision camera, IR camera and continuous-wave line laser. However, the decision-making for the crack identification often requires experts’ intervention. As a target concrete structure gets larger, automated decision-making becomes more necessary in the practical point of view. The proposed technique is able to achieve automated crack identification by modifying a well-trained convolutional neural network using a set of crack images as a training image set, while retaining the advantages of hybrid images. The proposed technique is experimentally validated using a lab-scale concrete specimen developed with various-size cracks. The test results reveal that macro- and micro-cracks are automatically detected with minimizing false-alarms.

This paper proposes a deep learning-based rapid inspection method for concrete structures. The proposed method is composed of three steps: (1) collection of a large volume of images containing damage information from internet, (2) development of a deep learning model (i.e., convolutional neural network (CNN)) using collected images, and (3) automatic selection of damage images using the trained deep learning model. In the first step, the internet-based search benefits in easy classification of collected images by varying searching word, and in collection of images taken under diverse environmental conditions. In the second step, a transfer learning approach has been introduced to save the time and cost for developing a deep learning model. In the third step, the probability map is introduced based on duplicated searching to make the searching process robust. The whole procedure of the proposed method has been applied to some figures taken in a real structure. This method is expected to facilitate the regular inspection and speed up the assessment of detailed damage distribution the without losing accuracy.

As compared to conventional physics-based techniques, advances in sensors and computing technologies have been promoting data-enabled structural diagnosis and conditional assessment using machine learning techniques in structural health monitoring (SHM). Machine learning helps civil engineers to extract valuable information from large amount of data to make time-sensitive decision. The application of different machine learning techniques to large-scale civil structures is, however, still impeded by challenges. In this study, we use representative supervised support vector machine (shallow learning) and deep Bayesian deep belief network (deep learning) to demonstrate their merits and limitations in structural diagnosis and conditional assessment. A benchmark in the literature is used for the demonstration. The results showed that the shallow learning highly relies on the hand-crafted features, while optimization of kernels is another challenge during learning process. The deep learning could promote the learning accuracy without kernel design. Although the noise could lead to difficulty in data mining, the comparison demonstrated that the deep learning has less sensitivity to the impacts of noise interference than those of shallow learning.

There is a high demand for high-performance and low-cost uncooled infrared (IR) detectors. In order to meet this need we are investigating the use of MEMS piezoelectric resonator technology using aluminum nitride (AlN) thin films. Recent research has shown that piezoelectric resonators have the potential to be used as a core element for highly sensitive, low-noise, and lowpower uncooled IR detectors. A novel design of an AlN IR sensor based on piezoelectric bending resonator is described and analyzed in this paper. The detector is constructed by using thermally mismatched materials which stress the resonator and shift the resonance frequency. The IR thermal input is sensed by monitoring the frequency shift induced by the in-plan thermal stress. These designs have the potential for very high sensitivity and are compatible with commercially viable CMOS fabrication technology.

CO2 concentration is considered as a very important index for the health of human respiratory system and industrial greenhouse gas emission so that CO2 sensing/monitoring becomes an interesting and challenging topic for both medical purpose as well as environmental engineering application. This paper proposes an innovative CO2 sensor by using the crystal resonators, including both Langasite and Quartz. The sensing principle is based on the frequency-mass effect, by which the adsorption of CO2 molecules on the crystal electrodes (typically Silver, Gold or Platinum) will cause the mass change and thereby determine the frequency shift according to the Sauerbrey equation. Lab experiments are carried out with both Langasite resonator and Quartz Crystal Microbalance. To evaluate their CO2 sensing performance, a mixed gas of CO2 and Nitrogen (reference gas) is applied to both Langasite CO2 sensor (with Platinum electrodes) and QCM CO2 sensor (with Gold electrodes), the concentration of CO2 is adjusted from 0% to 100% with a step of 25% by using a gas proportioner. Experiments results show that both Langasite resonator and QCM have a frequency shift with CO2 concentration change that is associate with the principles mentioned above. Moreover, Langasite resonator performs more accurately, stably and reliably, which is mainly due to the crystal and electrodes properties. The proposed CO2 sensor could be used as convenient breath monitoring for chronic respiratory disease, industrial greenhouse emission monitoring and chemical lab CO2 alarm.

An effective study and modeling of the dynamic structural status of buildings and large infrastructures (dams, bridges, sky-scrapes) requires the identification of their low frequency vibrations modes (mHz region) measured with suitable monitoring systems, based on high-sensitivity large-band linear and angular sensors. The UNISA Folded Pendulum class of sensors satisfies both the linear and angular requirements, as demonstrated by the new Extended Folded Pendulum Model (EFPM), describing their dynamical behavior for a generic orientation in space. In this paper, after a description of the EPFM model, we present monolithic implementations of pure angular sensors, together with some applications results.

Thick film resistors (TFRs) were screen-printed and fired on fluorophlogopite glass ceramic (FGC) and alumina ceramic substrate as smart aggregates. Performances of smart aggregates under compressive load were tested before and after embedded in mortar specimens. Compared to alumina ceramic substrate, the TFRs on FGC shows better compatibility and matching performance, the matching error between sensor and mortar decreased from 0.734 to 0.187, the output strain ratio between sensor and mortar increased from 0.0837 to 0.421, and the test results confirmed the theoretical calculation results. When embedded in mortar, the TFRs on FGC shows better repeatability than that on alumina ceramic under repeated compressive load.

With the advancing of reflectarray antenna technology, a rotational actuator is desired to enable the beam scanning capability. The anticipated characteristics of this actuator include small volume, low energy consumption, easy to control, fast response, high reliability, and etc. The actuator developed by this study is composed of bimorphs and microgears. It can rotate clockwise and counterclockwise alternately by varying the applied voltage. The most prominent advantage of this actuator is that it only requires feed forward control which is easy to implement. It doesn’t need feedback control, as other actuators do, with no accumulative errors for its positioning application. This study has optimized the geometry of this actuator using the multi-physics coupling finite element method (FEM) software. The components of this actuator have been fabricated by micro-electric-mechanical-system (MEMS) fabrication process and precise machining technology. Glass fiber reinforced composited (GFRC) and Lead Zirconium Titanate (PZT) materials are used to fabricate the bimorph. The copper and stainless steel are used to fabricate the housing and gear, respectively. Several actuators have been assembled and tested to investigate the characteristics of this actuator. A test set up has been developed to measure the relationship between the rotation angle and the applied voltage. A fast camera has been employed to experimentally assess the response time, repeatability, stability and etc. Finally, the experimental results are correlated with the analysis results.

Aptamers, with numerous advantages over antibodies, such as high stability, resistance to denaturation and degradation, and easy modification possibilities, have found widespread uses in biosensing, drug delivery, disease diagnosis and therapy. The specific binding ability of aptamers to cancer-related markers and cancer cells ensured high performance for early diagnosis of cancer. Carcinoembryonic antigen (CEA) usually is present only at very low levels in the blood of healthy adults, however, the levels can raise in certain kinds of cancers such as colon and rectal cancer, pancreas, breast, ovary or lung cancer, allowing CEA to be used as a tumor marker in clinical tests. Several research groups have further demonstrated the levels of CEA are significantly higher in saliva than in blood/serum. Here, we present an aptamer-based biosensor for CEA’s detection with a sensitivity of 0.399 µA/(ng/mL) in saliva. The binding affinities of the selected aptamers to CEA have been evaluated by Surface Plasmon Resonance imaging (SPRi) technology which is a very sensitive method to measure binding interactions of various biomolecules, including proteins, nucleic acids, and phospholipids. Furthermore, instead of using the time-consuming Systematic Evolution of Ligands by Exponential enrichment (SELEX) process, we proposed a molecular dynamics (MD) simulation method to automatically and effectively select target-specific aptamers. It systematically investigates the atomistic details of the binding mechanisms between aptamer and target. Complemented with experimental tests, SPRi validation and MD simulation, this electrochemical aptamer-based biosensing system demonstrates great sensitivity, clinically accuracy and reliability in realizing noninvasive disease detection and monitoring through saliva analysis.

This paper presents an experimental and numerical characterization of a piezoelectric d₃₆ shear-based torsion actuator made of xPb(Mg_1/3Nb_2/3)O₃-(1-x)PbTiO₃ (PMN-PT) single crystals embedded between Polydimethylsiloxane (PDMS) layers. The generated rate of twist value of the piezoelectric d₃₆-mode PMN-PT single crystal composite torsion actuator was obtained using a laser vibrometer from the maximum detected transverse deflection measurement. The quasi–static torsion actuation experiments were conducted on the piezoelectric d₃₆ torsion actuator by applying different AC voltages at 1 Hz. The experimental benchmark was further modelled by Finite Element (FE) code ABAQUS® using three dimensional (3D) piezoelectric finite elements. The experimental results and Finite Element computations showed good agreement. Findings reveal that more rate of twist is produced by PMN-PT single crystals in comparison to piezoceramic alternatives. This piezoelectric PMN-PT d₃₆-mode composite torsion actuator can be effectively used in torsional deformation control.

In a high temperature environment, it is challenging to perform structural health monitoring (SHM), which has become a required task for many important civil structures in harsh environments. A SHM system in high temperature environments requires a large number of sensors for different data resource measurements, for example, strain and temperature. The accuracy of the measurement is highly dependent on the trade-off between the number of sensors of each type and the associated cost of the system. This paper introduces a sensor optimization approach based on an evolutionary strategy for the multi-objective sensor placement of structural health monitoring in high temperature environments. A single-bay steel frame with localized high temperature environment validates the multi-objective function of the evolutionary strategy. The variance between the theoretical and the experimental analysis was within 5 %, indicating an effective sensor placement optimization using the developed genetic algorithm, which can be further applied to general sensor optimization for SHM system applications in high temperature environments.

Strain field distributions within a beam are frequently calculated using linear theory. However, linear beam theory does not provide solutions in the areas close to force application points, due to local strain perturbations caused by the force. Hence, interpretation of strain measurements taken by long-gauge sensors in proximity of force application point is difficult, as the measurements cannot be directly compared with analytical models. In addition, due to perturbation, sensors with different gauge lengths installed at that same location provide different values of measured strain. This paper explores and develops a potential analytical model for strain field perturbation in a beam’s regions close to force application points, and investigates the influence of sensor gauge length on strain measurement in these areas. An analytical model derived by Seewald is modified in order to fit the sensor measurements. Based on the model, evaluation of strain measurement is performed taking into account the gauge length of the sensor. Results are validated using data from a real structure, Streicker Bridge at Princeton University campus.

Basilar metamembrane (BM²) based frequency sensors have received growing interest in the engineering community for its ability to control the sensing/analyzing broadband frequency spectrum while mimicking the geometry and functionality of a basilar membrane (BM) in mammalian cochlea. Membrane stiffness plays the principal role in spatial selection of acoustic frequencies in BM². Likewise natural BM, conventional BM² possess homogeneous stiffness over the membrane length. However, broadband frequency selection ability of the BM²'s are considerably less due to geometric simplicity adopted in BM²'s (uniform thickness) compared to BM (tapered thickness). This work presents the possibility to manipulate (increase/decrease) the broadband frequency selection ability of the sensor, using functionally graded membrane stiffness. Three popular functions (logarithmic, exponential and linear) are presented as potential expression to grade membrane stiffness. A detailed insight is provided, how sensing parameters of a specific frequency band (e.g. sensing location of band start and end frequency, membrane segment width necessary to sense whole frequency band) can be influenced by coefficients of the functions.

Many convolutional neural networks (CNN) –based approaches were proposed and applied to detect damage in various civil structures in recent years. Usually, the training process of the classical CNN requires a large number of labeled data which is from the monitored structure in undamaged and various damaged scenarios. However, it is impractical to acquire sufficient data that can be exactly labeled with damaged from the infrastructures in service as training data. Thus, we propose a novel unsupervised CNN-based approach to automatically extract optimal feature representations from the unlabeled data in a single class. In the case study, a known dataset from an undamaged scenario is used to train CNN and a dataset from an unknown scenario is used to test the trained CNN. The proposed approach in unsupervised learning is capable of extracting feature representations from the raw acceleration signals that are sensitive to the presence of damage. Then, the extracted damage-sensitive features are fed into a one-class support vector machine (OC-SVM) for novelty detection. The feature set from the undamaged dataset is taken as training dataset to train the OC-SVM, and the extracted features from the unknown dataset are used for testing. In order to verify the effectiveness of the proposed approach in structural damage localization, a number of accelerometers are used to acquire sufficient raw acceleration data from a lab-scale steel bridge, and the preliminary experimental results show that the proposed novel CNN-based approach performs very well in damage localization.

In this paper we propose a novel fusion convolutional neural network to identify the local fatigue cracks in steel box girder of cable-stayed bridge. Unlike conventional CNN’s chain-like structure, the proposed network fully exploits multiscale and multilevel information of input images by combining all the meaningful convolutional features together. Raw images with high resolution of 3624×4928 are decomposed into three kinds of sub-image sets with lower resolution of 64×64, background, handwriting and crack, respectively. Multi-functional layers are stacked including convolution, ReLU, softmaxResults show that the test error drops to 4% after only 50 epochs and it is more effective compared with other deep learning networks when handling large image datasets.

In this study, an online monitoring technique for continuous fatigue crack quantification and remaining fatigue life estimation is developed for plate-like structures using nonlinear ultrasonic modulation and artificial neural network (ANN). First, multiple aluminum plates with different thicknesses were subjected to cyclic loading tests with a constant amplitude, and ultrasonic responses were obtained from three PZT transducers placed on each specimen. Second, an ANN is constructed by (1) defining the specimen thickness, the elapsed fatigue cycles, and two features extracted from the ultrasonic responses, named as cumulative increase and decrease of nonlinear modulation components, as inputs and (2) the crack length and the remaining fatigue life as outputs. The results of validation tests indicate that the proposed technique can estimated the crack length and the remaining fatigue life with a maximum error of 1.5 mm and 2 k cycles, respectively. The uniqueness of this technique lies on (1) fatigue crack quantification and remaining fatigue life estimation using nonlinear ultrasonic modulation, and (2) data-driven continuous crack quantification and prognosis.

The integration of sensing technology with structural health monitoring (SHM) has lead to advancements in how structures are monitored and investigated. One of the issues that has accompanied advancement in the industry is the time required to carry out testing on large-scale concrete reinforced structures using methods like impact-echo and ground penetrating radar (GPR). Back end processing and automation of testing systems are two means of addressing time consuming testing programs. This study proposes a semi-autonomous testing setup to carry out impact-echo testing on a lab specimen and a full-scale field structure. The testing method is coupled with artificial neural network (ANN) processing to decrease the need for user-interactions to produce results from the testing. The use of the semi-autonomous testing method and ANN processing is postulated to decrease the time needed for testing and improve the repeatability and accuracy of the impact-echo testing.

The objective of this work to develop a fast and effective in situ sensing and control method for flexible hydrodynamic lifting bodies in complex, multiphase flows. The sensing system is based on embedded strain and accelerometer measurements, which were used to reconstruct the real-time in situ 3-D deformations and off-line modal characteristics of a flexible surface-piercing hydrofoil in multiphase flow. An inverse fluid-structure interaction solver is used to determine the unknown operating conditions, hydrodynamic load distributions, cavitation and ventilation patterns. Good agreements are observed between predictions and measurements. Finally, preliminary experimental results are shown for a new ventilation control method that takes advantage of the measured in situ modal characteristics.

A modified magneto-rheological rubber (MRR) which resistance can be reduced to 1KΩ was fabricated by dispersed multi-walled carbon nanotubes (MWNTs) and carbonyl iron particles (CIPs) into polydimethylsiloxane (PDMS) matrix. Based on the excellent piezo-resistive properties of this modified MRR, a MR bearing with self-sensing characteristics was proposed and systematic researched. In order to study the self-sensing characteristics of the MR bearing based on modified MRR under load and magnetic fields, the structure and working conditions of the bearing were simulated and compared. The optimal structure size of the bearing was selected and used for the build-up of experiment test system. The results showed that, under the action of preload condition by bearing, due to piezo-resistance and magnetoresistance behavior of the modified MRR, the electrical resistance of it also can be changed over 28%. It suggests that, in the MR semi-active vibration isolation system, the modified MRBs are potentially capable of being used as an actuator and sensor in the same time.

This paper presents the preliminary study on the dynamic characteristics of the basilar membrane (BM) within the cochlea of inner ear. The BM is a vibrating element that varies in width and stiffness like a string on an instrument. While low frequency sounds vibrate near the apex (at the maximum length), high frequency sounds vibrate near the base of the cochlea (near the round and oval windows). Over the last decades, this frequency selectivity has been utilized for acoustic transducers by mimicking the cochlea tonotopy: passive frequency selectivity and transform from acoustic sound into frequency signal of hair cells in the organ of Corti. In previously reported studies, the frequency selectivity was simply achieved by physical parameters, such as length and thickness of beam array although the motion of the BM is generally described as a traveling wave. In this study, fluid-structure coupled acoustic analysis of vibrating BM within the cochlea of inner ear is performed to describe the actual motion of BM. The new approach different from the cantilever beam array –based approach will be then investigated for improved frequency selectivity.

In the light of the characteristics of pedestrian-induced vibration comfort evaluation of long-span footbridges, a real-time evaluation system of pedestrian-induced footbridge vibration comfort was developed based on Android platform for the first time. The system is composed of data acquisition subsystem, management center subsystem and Android mobile client. On the basis of collecting pedestrian-induced vibration signals by using the built-in high-precision acceleration sensor, space coordinate transformation algorithm was used to the dynamic signal acquisition process, transformation from smartphone’s coordinate system to the inertial coordinate system was achieved. Wavelet transformation was used to isolate gravity from the raw signals, the aim of this paper is to develop a real-time evaluation system. The system was tested on Guangzhou Gangding Footbridge, the field test results show that the comfort degree obtained from the system was identical with the truth, which demonstrates that the real-time vibration comfort evaluation system is scientific and effective, it makes up for the vacancy of real-time evaluation of pedestrian-induced footbridge vibration comfort.

Online structural health monitoring of large-scale models of infrastructures under hazardous environmental loadings— like earthquakes—has been a vital research topic during recent years. A linear Kalman filter has been employed in many cases in which the desired parameters are extracted in a propagated state vector during a recursive regime. Also, many other kinds of nonlinear filters have been developed for nonlinear systems identification following the linear Kalman filter concept, such as the unscented Kalman filter and the cubature Kalman filter. The main contribution of these two Kalman filtering techniques relies on the propagation of a covariance matrix instead of nonlinear transition and measurement functions. Our extensive literature review shows that divergence of estimated states for large degree-offreedom (DoF) models is the main drawback of these techniques. To overcome this weakness, these two filters’ predefined points, sigma points, are combined—with some modifications—to have more predetermined points for the propagation of states and output of covariance matrices. The proposed technique was developed to be used for large DoF systems with a high level of noisy measured data, which indicates a robust identification system. To evaluate the proposed method, a numerical model (10 DoF linear system) with high levels of noise in the measured response data are employed to evaluate the robustness of the proposed method. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.

Monitoring a building’s structural performance is critical for the identification of incipient damages and the optimization of maintenance programs. The characteristics and spatial deployment of any sensing system plays an essential role in the reliability of the monitored data and, therefore, on the actual capability of the monitoring system to reveal early-stage structural damage. A promising strategy for enhancing the quality of a structural health monitoring system is the use of sensors fabricated using materials exhibiting similar mechanical properties and durability as those of the construction materials. Based on this philosophy, the authors have recently proposed the concept of "smart-bricks" that are nanocomposite clay bricks capable of transducing a change in volumetric strain into a change in a selected electrical property. Such brick-like sensors could be easily placed at critical locations within masonry walls, being an integral part of the structure itself. The sensing is enabled through the dispersion of fillers into the constitutive material. Examples of fillers include titania, carbon-based particles, and metallic microfibers. In this paper, experimental tests are conducted on bricks doped with different types of carbon-based fillers, tested both as standalone sensors and within small wall systems. Results show that mechanical properties as well as the smart brick’s strain sensitivity depend on the type of filler used. The capability of the bricks to work as strain monitoring sensors within small masonry specimens is also demonstrated.

Critical non-structural equipment, including high-precision equipment in the technology facilities, life-saving equipment in the hospitals, data storage equipment in the communication, computer, and data centers, etc., is vulnerable to vibration, and on top of that, the failure of these vibration-sensitive equipment will cause severe economic loss. In recent years, a lot of research has been conducted towards evaluation of semi-active control strategy for earthquake protection of vibration-sensitive equipment. Various innovative control algorithms have been studied to compensate the inertial loading, and these new or improved control strategies, such as the control algorithms based on the linear-quadratic regulator (LQR) and the sliding mode control (SMC), are also developed as a key element in smart structure technology. However, except to the advantage of simplicity and variability, both LQR and SMC are (centralized) full-state feedback controller and the state vector needs to be presented through a state estimator or compensator if it is not measured during earthquake excitation. On the other hand, considering decentralized control strategy, the controller is only evaluated using the response in the vicinity of control devices, thus minimizing the wiring and sensor communication requirements. However, the very limited information obtained and used by decentralized control strategy also restrains the control capability from reducing the response where without installing sensors.

The aim of this paper is to develop a decentralized control algorithm on the control of both structure and non-structural equipment simultaneously to overcome the limitations of decentralized control through combining the advantage of wavelet analysis. This wavelet-based decentralized control algorithm will be simulated based on a frame with an equipment isolation system and a magnetorheological (MR) dampers located on the top of first floor numerically. The performance and robustness of wavelet-based decentralized control algorithm as well as the response of primary structure are evaluated and discussed through simulation study to demonstrate the efficiency of proposed control algorithms.

To protect the China ancient cultural heritage and ancient buildings, this paper proposed an ultimate life prediction method that is suitable for the Chinese ancient wooden buildings. An ancient building installed a structural health monitoring (SHM) system was an example of practical engineering to validate the proposed method. In the practical engineering, the SHM system was briefly introduced firstly, afterwards a finite element model was developed and updated by the monitoring data, and the life of the building was analyzed and predicted. The results show that the proposed damage prognosis and life prediction method is applicable and efficient.

The objective of this study is to discuss three different methods on operational modal analysis: Covariance-driven stochastic subspace identification (SSI-COV), Refined frequency domain decomposition (rFDD) and Multivariate autoregressive (MVAR) model. First the SSI-COV method is employed. Through the proposed two steps of evaluation criteria on the discrimination of spurious modes from the stabilization diagram, identification on the correct models can be elaborated. Besides, discussion on the identification of harmonic component from stabilization diagram using the concept of singular value spectrum generated by refined frequency domain decomposition (rFDD) is also presented. Combine the SSI-COV, rFDD and the proposed criteria to remove the spurious modes, one can accurate estimate the structural modal dynamic properties. Comparison on the final stabilization diagram with respect to the model order spectrum using multivariate AR model is also presented. As an application, an ambient vibration test of 8-story steel frame from shaking table test is used to demonstrate the proposed algorithms. Discussion on the missing data to the influence on the result of identification is also presented. Compensation on the missing data to enhance the stabilization diagram.

Civil engineering structures can undergo serious damage due to impact forces. But accurate and rapid identification of impact force is quite challenging because its measurement is difficult and location is unpredictable. This study proposes a novel approach for the complete identification of impact force including its location and time history. The proposed method combines an augmented Kalman filter (AKF) and Genetic algorithm (GA) for accurate identification of impact force. In AKF unknow force is included in the state vector and estimated in conjunction with the states. First, the location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations, assumed co-variance values are used in AKF at this stage. These values are assumed based on a few analyses in which force location is assumed to be known. Then, GA is applied to optimize the error co-variances by minimizing the error between measured and estimated structural response. Once optimized co-variances are obtained, the exact time history of impact force can be constructed using AKF. Numerical example of a truss is considered to validate the efficacy of proposed approach. Strain and acceleration measurements are used as input for the AKF. Both modelling error and measurement noise are considered in the analysis to simulate the actual field conditions.

Modal parameters identification research based on using vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. Since time–frequency analysis for non-linear and non-stationary signals is extraordinarily challenging for structural response under extreme loading. To capture features in these signals, it is necessary for the analysis methods to be local, adaptive and stable. Classical approach to compute its instantaneous frequency is to consider the amplitude–frequency modulated formulation of its complex (or analytic) signal extension, via the Hilbert transform. The aim of this paper is therefore to present and discuss some non-parametric methods of time frequency analysis improved with respect to the classical implementation, and compare their performance under different conditions with respect to the signals to be examined. In particular, the following methods will be presented:

Modified Complex Morlet Wavelet with Variable Central Frequency (MCMV+VCF)

Enhanced Time-Frequency Analysis Through SVD-based WPT

Synchro-Squeeze wavelet transform method (SSWT)

Reassigned Smoothed Pseudo Wigner-Ville Distribution (RSPWVD)

To demonstrate the applicability of the proposed method responses from synthetic signals and instrumented responses are used to demonstrate the capability of feature extraction of the methods, analysis on the synthetic data as well as the response data.

This study explores the use of fractional partial differential equations to model the wave propagation through a one-dimensional complex and heterogeneous medium. In particular, this work discusses the use of fractional calculus to obtain closed form analytical solutions for the dispersion and propagation of elastic waves in a periodic, bi-material rod. From a mathematical standpoint, the approach allows converting a partial differential equation having spatially variable coefficients (i.e. the traditional wave equation in periodic media) to a space-fractional wave equation with constant coefficients. We show that the equivalent fractional equation exhibits a frequency-dependent and complex fractional order. Although this conversion might appear to increase the overall complexity of the model, in practice it enables obtaining closed form analytical solutions of wave propagation problems through inhomogeneous media. The analytical solution to the space-fractional equation is obtained for the steady state response under harmonic loading. The result is compared to the traditional finite element solution of the wave equation in periodic media showing that the new approach is able to provide a reliable and accurate analytical representation of the dynamic response of the medium.

This paper presents acoustic emission (AE) monitoring of a large-scale curved, post-tensioned concrete wall under monotonically increasing prestressing loads. This structural system, which is commonly used in water storage tanks, silos, bins, and nuclear containment structures, is subject to hidden delamination defeats that may develop during posttensioning and lead to a premature brittle failure. To detect the onset of such defects, this study uses a network of AE sensors mounted on the outer surface of the wall and identifies common patterns in AE signals. Specifically, AE signals are clustered using k-mean clustering, and their sequence is modeled with a hidden Markov model. The comparison of the results with accurate through-thickness expansion measurements of the wall shows that certain patterns in AE signals are correlated with the onset of delamination, and thus can be used to detect it. This early detection of such delamination defects provides decision makers with sufficient time to take remedial and preventive action.

Inland navigation infrastructure is critical to local and global economies, and unplanned or extended down time for maintenance and repairs can have significant social and economic consequences. The extensive collection of navigation related infrastructure maintained by the US Army Corps of Engineers (USACE) is in a state of degraded performance, with some components presently being used well beyond their design life. To provide data-driven decision-support for operation, maintenance, and repair/replacement of these components, the Engineer Research and Development Center (ERDC) is developing structural health monitoring (SHM) and damage prognosis (DP) tools and techniques. ERDC has deployed the Structural Monitoring and Analysis in Real-Time of lock Gates (SMART Gate) program at several sites for long-term monitoring of hydraulic steel navigation and flood control gates. When SMART Gate systems are deployed, a significant effort and percentage of the cost is spent installing conduit to protect wires that extend from the sensors, which are typically underwater during operation, to the data logger. To reduce installation time and cost, the ERDC developed a system for energy efficient sensor data transmission underwater. The system was successfully field tested, sending data the height of the lock chamber using low nominal power, with a relatively low data loss. This paper will describe the SHM framework developed by ERDC and the development and deployment of the wireless data transmission system.

The authors have developed a digital beam forming dynamic imaging radar system, ”VirA.” It is a cutting-edge non-contact measurement technology for space-continuous vibration monitoring of buildings. In this research, the authors have presented the vibration monitoring method by conducting vibration measurements of an actual tall building. Experiments were performed to compare the performances of two time-synchronized radars and a conventional vibrometer. Frequency analysis was performed and the dominant frequencies were compared with those determined from the conventional vibrometer.

Flood scour is one of the major causes of bridge failures in the United States. Flood fragility curves can be used as an objective tool to assess the risk of a bridge to exceed some limit-states for a given flood. Despite the method’s extensive use in seismic reliability analysis for civil infrastructures, the approach has rarely been utilized in evaluation of scour critical bridges. Flood fragility analysis of a instream bridge structure has been analyzed using first order reliability method. A MATLAB-based code has been developed to perform the analysis combining the first order reliability analysis tool of FERUM (Finite Element Reliability Analysis using MATLAB) and finite element structural analysis code ABAQUS. The developed code passes information back and forth between FERUM and ABAQUS in the iterative process to progress the analysis. Moreover, the code is capable of simulating scour depth, corresponding change in foundation parameter, variable limit-state for given inputs, which are realized by generating new finite element structural model each time FERUM calls ABAQUS. The proposed automated procedure has been demonstrated by deriving fragility curves, due to flood induced scour and stream water pressure, for a real bridge.

Estimating both state and ground input for earthquake-excited building structures using a limited number of absolute acceleration measurements is critical to post-disaster damage assessment and structural evaluation. Input estimation in this case is particularly challenging due to the lack of direct feedthrough term, which renders the system weakly observable for its input. Hence, input estimation in this scenario is sensitive to modeling error and measurement noise. In this paper, a two-step strategy is proposed to estimate both state (displacement and velocity) and ground input using a limited number of absolute acceleration measurements for building structures. First, the ground input is estimated by solving a least squares problem with Tikhonov regularization and Bayesian inference. In the second step, floor states are estimated using Kalman filter with input obtained from the first step, the least squares with Tikhonov regularization and Bayesian inference. The proposed strategy was numerically evaluated based on a sheartype building structure.

This research studies finite element (FE) model updating formulations utilizing the measured frequency-domain modal properties, i.e. resonance frequencies and mode shapes. The modal properties provided by an FE model are usually different from the ones experimentally measured from an as-built structure. To update the FE model parameters, optimization problems are formulated to minimize the difference between experimental and simulated modal properties. Two modal property difference formulations are presented in this research, one using MAC values and the other using direct differences between eigenvectors. To find the optimal solution of the formulated optimization problem, two optimization algorithms are studied for comparison, i.e. the Levenberg-Marquardt and the trust-region-reflective algorithms. Randomly generated starting values of optimization variables are adopted to increase the chance of finding global minimum. The model updating formulations with different optimization algorithms are studied with a space frame example.

Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, appropriate data analysis and feature extraction techniques are required to interpret the measured data and to identify the state of the structure and, if possible, to detect the damage. Among these techniques tracking modal parameters and estimating the structural current state from its seismic response measurement can provide useful information for structural safety assessment, therefore, on-line or recursive identification technique needs to be developed for structural seismic response monitoring. In this paper, the recursive subspace identification algorithms based on matrix inversion lemma algorithm (RSI-Inversion) with oblique projection technique was developed. Forgetting factor with enlarge window is introduced in the RSI-Inversion to emphasize the latest state of the time-varying system in this method. In addition to identifying the instantaneous dynamic characteristics of the structural system using RSI, a two-stage damage detection algorithm incorporated with the identified results from RSI will also be applied to localize and quantify the structural damage. Seismic responses of a base-isolated bridge are used to verify the proposed identification and the damage assessment algorithms, i.e. specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction.

The Department of Transportation classifies a vehicle depending on the number of axles and the space between them (wheelbase). Vehicle classification accuracy depends on the accuracy in estimating the speed and the wheelbase. In this study, a three-dimension Glass Fiber-Reinforced Polymer packaged Fiber Bragg Grating sensor (3D GFRP-FBG) is introduced for vehicle speed and wheelbase estimation. The experimental results show the ability of the sensor to estimate the speed and the wheelbase of a reference vehicle, with an accuracy of 95% or higher.

In this study, a method for the deployment of a distributed fiber optic sensor on a spherical object is proposed. The optical cable was wrapped around a 114.3 mm diameter acrylic ball in a spiral formation. Two different loops were placed and fixed to the surface with either superglue or epoxy. The ball was loaded axially in a load frame and the tangential strain was measured using pulse pre-pump Brillouin optical time domain analysis (PPP-BOTDA). This data was compared with a numerical model and the suitability of this deployment scheme is discussed. Attempt will be made to correlate their notable difference with the curvature and installation procedure. Overall, the spiral shape allows for a predictable location of the fiber and both the epoxy and superglue maintain the required bond strength for strain transfer to the fiber. Although this study focuses on the effects of axial load, this deployment methodology can be used in other structural health monitoring applications such as spherical pressure vessels or other curved objects.

In this paper, we introduce a novel extrinsic Fabry-Perot interferometer (EFPI)-based optical fiber inclinometer for tilt measurements with 0.015 μrad resolution. The reported inclinometer consists of an EFPI sensor, which is formed between endfaces of a suspended rectangular mass block and a fixed optical fiber, packaged inside a rectangular container box with an oscillation dampening device. Importantly, the two reflectors of the EFPI sensor remain parallel while the cavity length of EFPI sensor meters a change in tilt. The sensor design and the measurement principle are discussed. An experiment based on measuring the tilt angle of a simply-supported beam induced by a small load is presented to verify the resolution and accuracy of a prototype inclinometer.

In this research, distributed sensing based on Rayleigh scattering is used to measure temperature and strain in a composite panel during a high energy laser strike. The ultimate goal is to rapidly detect a laser strike by sensing the localized, rapid temperature rise caused when directed energy is incident on the surface of a composite structure. A secondary goal is to determine if the thermal response can be detected even in the presence of applied strain. Initial results will be discussed for composite structures comprised of carbon fiber/epoxy of various thicknesses using embedded distributed optical fiber sensors (DOFS) to rapidly detect temperature changes greater than 1000° on the surface or between plies of the composite. Measurements of the temporal and spatial response are taken at rates greater than 20Hz with sub-millimeter resolution. An infrared camera is used to validate the temperature measurements obtained using DOFS. In addition, since DOFS respond to strain as well as to temperature, any strain in the composite as a result of mechanical loading is coupled into the embedded fiber and is also detected by the sensor. Initial measurements are taken to demonstrate the simultaneous response to both temperature and strain and to characterize the typical strain that results. A DOFS-based sensing architecture can then be designed to mitigate the mechanical response of the sensor, allowing for isolation and rapid detection of the thermal response when high energy radiation is incident on the composite surface.

Temperature monitoring and regulation is a critical aspect of data center administration. Currently, conventional discrete transistor-based thermal sensing systems are widely used for this purpose, which requires a discrete device for each temperature measurement in the special domain. This leads to an increase in both complexity and cost as the data center grows in scale. This manuscript describes a real-time multiplexed optical fiber thermal sensing system for data center applications which simultaneously measures thousands of discrete points along the length of the fiber under test. This system allows for real-time thermal monitoring of several hundred servers with a spatial resolution of 1 cm, a temperature resolution of <1 °C, and a system update rate of 1 Hz. Temperature inside of individual servers and the ambient room temperature outside the racks can be simultaneously monitored in real time using a single optical fiber probe. To investigate this concept, a pilot experiment is presented which monitored the dynamic server temperature distribution using the proposed fiber sensing system. Temperature data recorded using built-in thermal sensors within the CPU of the server under test were simultaneously recorded and compared to measurements made. In order to induce a temperature change within the server, a computationally intensive task was undertaken during temperature testing. Both methods of temperature measurement demonstrated similar trends, indicating that the proposed multiplexed optical fiber-based system has substantial potential as a scalable method of distributed data center temperature monitoring.

A scour monitoring method for subsea pipelines is proposed using distributed Raman optical sensors based on active thermometry to achieve distributed monitoring of the scour and free span of a subsea pipeline. The method consists of three parts: a thermal cable, a data acquisition unit (DAU), and a data processing unit (DPU). In this paper, an industrial cable is used as the thermal cable, which can act as the heat source as well as to monitor the temperature. A DTS8000 thermodetector collects the temperature information over time. The surroundings of the subsea pipeline turn from sediment into seawater when scour and free span occur. The heat transfer properties are different between the two media, and the free span and length of the subsea pipeline can be detected from the different temperature change patterns during the heating and cooling process using a distributed Raman scattering technique. In this paper, we mainly focus on recognizing the interface of different media. Tests demonstrate good results in the identification of the interface using an industrial cable as a thermal cable.

Bottom-up crack may occur in concrete pavement with the increase of loads by traffic and environmental effect. Cracking in concrete pavements would produce serious damages in pavements since it induces water penetration in pavement structure and foundation. However, currently, most of the distress detection system available in market can only detect the surface cracks show up. Cracks hidden under the surface, especially, bottom-up cracks in pavements are very challenging to be detected. This paper introduces potentials to use in-pavement point sensors to detect the existence of bottom–up cracks and characterize them for future maintenance decisions. The stress intensity principal is used in this study to determine the locations and lengths of cracks. Upon validation, this study will significantly impact the current pavement detection practice to detect hidden bottom-up cracks.

Active and regular monitoring of structural components is a critical safety precaution, particularly as it concerns infrastructure, aerospace, industry, and many other applications. Solutions for effective structural health monitoring need to be able to probe surface and bulk properties using durable and low-power engineering. Towards these ends it is possible to build smarter, highly networked, and secure materials in order to enable remote real-time material sensing with a lesser-known form of 3D printing, ultrasonic consolidation (UC). This approach harnesses sound waves to weld metal layers in a low temperature process that does not damage sensors and electronics as they are embedded into a solid metal structure. This makes UC perfectly suited for designing solid metal parts with active embedded sensing components. However, the process parameters, material influences and mechanical factors that result in high-quality UC metal components are difficult to control or are loosely understood. The use of trial-and-error optimization during fabrication represents the chief hurdle between its current state of use and its potential to transform rapid prototyping and manufacturing of high-impact technologies (e.g., metal smart structures, wearable sensors, lab-on-a-chip, etc.). In this paper we will describe how embedded sensors may be used for in situ process monitoring and optimization. We will also discuss efforts towards standardizing UC welding for similar and dissimilar metal bonding and for embedding active sensors that can be used to create smart structures that will enable long-term structural health monitoring and other high impact applications.

Damage in civil, aerospace, and mechanical structures caused by crack growth and impact loading generate transient ultrasonic waves whose frequency and amplitude can reveal the underlying structural health condition. Hence, it is necessary to find a useful tool based on ultrasonic detection for structural health monitoring. Recently, smart sensors based on gratings such as fiber Bragg gratings (FBGs) have been shown to be suitable to detect such acoustic waves in structural health monitoring applications. However, the fiber-based gratings as the ultrasonic sensor has limited sensitivity to high frequency ultrasound detection due to a specific grating length and a finite spectrum width. To eliminate this limitation, one improvement has been made by using phase shift FBGs due to their special filtering characteristics. The phase shift FBGs can have a narrower spectral width, which will significantly improve the detection sensitivity. Another big improvement, for example Bragg grating waveguide (BGW) sensor, is to optimize the grating structure using different materials. In this work, we describe a 3D printed-polymer BGW sensor for ultrasound detection fabricated through a two-photon polymerization process. The design and fabrication have been optimized for high detection sensitivity. The results demonstrate the potential application of BGW devices for high-sensitivity ultrasound detection.

A magnetic and inertial measurement unit (MIMU) usually measures acceleration, rotation rate, and earth's magnetic field in order to determine a body's attitude. In order to find the orientation information using all sensors a fusion algorithm is used. This paper compares two approaches used for a Attitude and Heading Reference System (AHRS), namely Madgwick and Mahony with a basic fusion approach. Foot mounted MIMU data is used to estimate the Euler angles as well as the position. The results show that Madgwick obtains better heading orientation than Mahony and the basic AHRS approach in terms of the error (RMSE) of the Euler angles when compared to the ground truth. However, the execution time of Mahony is less than Madgwick with the basic AHRS taking the longest.

Distributed optical fiber sensors are increasingly utilized method of distributed strain and temperature sensing, and the swept laser source plays an significant role in these applications. However, there is dynamic frequency-noise as the laser sweeping. In this paper, we proposed and experimentally demonstrated a real-time in-situ phase noise detecting method in a field programmable gate array (FPGA) chip, which permits accurate and insightful investigation of laser stability. This method takes only 1 clock cycle to capture the phase noise.

Scavenging energy from human motion is a potential way to meet the increasing requirement of electrical power supply for portable electronics. However, since the conventional energy harvesters may collect both positive and negative work, the users have to pay extra effort. This work aims at developing a smart energy harvester to identify and capture the negative work of human ankle motion. During normal walking, only dorsiflexion of ankle joint at stance phase performs negative work. Thus, one-way clutch is employed to filter ankle plantarflexion and mechanical contact switch array is used to disconnect resistance load, avoiding capturing positive work when ankle dorsiflexion is at swing phase. With the oneway clutch and mechanical contact switch array, the energy harvester can effectively target negative work as energy scavenged without consuming electrical energy. The energy harvester is designed based on proposed principles. It is also modeled to predict the output power, efficiency and assistive torque. The simulation results show that the energy harvester can provide enough power for portable electronics and it is helpful in reducing ankle moment.

Bimorph piezoelectric elements are often used to harvest energy for low-power structural health monitoring systems. When these piezoelectric elements are deployed for extended periods of time and operate under near-resonant conditions, the resulting high amplitude cycling can lead to degradation of the piezoelectric element, resulting in a shift in the design fundamental frequency. For scenarios in which the piezoelectric harvester is subject to slowly-varying time-dependent frequency inputs, the natural frequency shift due to degradation may cause the piezoelectric harvester to detune from resonance, subsequently affecting the harvester’s power output. The current study seeks to understand how the accumulation of damage shifts the optimal tip mass and resistive load in a bimorph piezoelectric energy harvester. A cantilever piezoelectric element is modeled utilizing coupled electromechanical equations in a distributed system. The piezoelectric is subject to ground accelerations; the resulting power output is recorded for a range of tip masses and resistive loads. A rainflow analysis is then performed to calculate the piezoelectric element’s tip displacement amplitude and the corresponding cycle count. A damage accumulation model based on a weighted form of Miner’s rule is then used to degrade the harvester’s flexural rigidity, piezoelectric capacitance, and piezoelectric strain constant. The piezoelectric is again loaded and the process repeated. The resulting power output contours reveal how the optimal realization of tip mass and resistive load changes as damage accumulates in the piezoelectric element. Apparent trends in the power output contours are explained. Approved for publication, LA-UR-18-20075.

In this article, the concept of simultaneous noise filtering and energy harvesting are fused to propose metamaterial (MetaWall) bricks made of rubber-metal-concrete composite, as an industrial building material. The MetaWall bricks are capable of filtering acoustics noises more effectively than conventional barriers while harvesting the electrical energy from the trapped acoustic pressure generated by the sound and vibration. MetaWall bricks are made of Acousto-Elastic Metamaterial (AEMM) unit cells as energy harvesting component in the wall. A unit AEMM cell of the brick consists of the concrete exterior, soft rubber inclusions and hard metallic resonators. To exploit the local resonance of the resonator and recover the trapped strain energy in the soft constituents of AEMM, piezoelectric wafers are placed inside each AEMM unit cell. The primary objective of the work is to examine the performance of the MetaWall bricks. A prototype of the MetaWall brick is simulated to verify the concept. The results show that the model could generate about 1.73mW power under 10KΩ resistive load.

This paper shows input-output analysis of a neural oscillator swarm stimulated by earthquake-induced acceleration responses of a structure. We have proposed a new active mass damper system consisting of a neural oscillator and a position controller. However, the proposed system has not adapted successfully to parameter changes of the structure. Recent studies in biology have demonstrated that multiple oscillators have hierarchical network structures to ensure adaptation to environmental changes. To improve the robust performance of the proposed system by constructing of hierarchical network of neural oscillators, there is a need for a better understanding of nature of different neural oscillators. This research addressed this need by visualizing output of swarm of neural oscillators, whose natural frequencies and input gains are different. The numerical information of output is visualized by grayscale, and the relation of output of different neural oscillators is considered when input is the same. As a result, the research provides new information that predicts the instant center frequency of a structure excited by earthquakes.

Smart cements offer a unique opportunity to unify our approach to the remote monitoring and repair of concrete assets. Here, we present our latest progress in manufacturing and testing smart cement sensor-repairs based on fly ash geopolymers - a novel class of cement-like binders that cure to a strong, chemically resistant, electrically conductive shell. Since chloride and moisture are two of the leading causes of degradation of reinforced concrete, we are proposing a technology that is able to monitor chloride ingress into concretes at different levels of moisture. The main task of the work was to manufacture geopolymer binders for concrete specimens, and to cure them at ambient temperatures. We have studied how practical considerations, such as the concrete substrate’s maturity, can affect how or whether smart cements can be applied, thus understanding the main limitations of the technology. By using electrical impedance measurements, we aim to demonstrate that geopolymer skin layers can provide high resolution monitoring of chloride contamination at different levels of moisture. Here we present results which show that smart cements are sensitive to changes in humidity of the surrounding environment. Our goal is to develop a robust and field-worthy technology which unifies civil monitoring and maintenance. This goal is of key national importance to the US and many countries within Europe, who now face an ageing population of reinforced concrete bridges, tunnels and support structures.

An important challenge of exploring the solar system is the ability to penetrate at great depths the subsurface of planetary bodies for sample collection. The requirements of the drilling system are minimal mass, volume and energy consumption. To address this challenge, a deep drill, called the Auto-Gopher II, is currently being developed as a joint effort between JPL’s NDEAA laboratory and Honeybee Robotics Corp. The Auto-Gopher II is a wireline rotaryhammer drill that combines breaking formations by hammering using a piezoelectric actuator and removing the cuttings by rotating a fluted bit. The hammering is produced by the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that has been developed by the JPL team as an adaptable tool for many drilling and coring applications. The USDC uses an intermediate free-flying mass to convert high frequency vibrations of a piezoelectric transducer horn tip into sonic hammering of the drill bit. The USDC concept was used in a previous task to develop an Ultrasonic/Sonic Ice Gopher and then integrated into a rotary hammer device to develop the Auto-Gopher-I. The lessons learned from these developments are being integrated into the development of the Auto-Gopher-II, an autonomous deep wireline drill with integrated cuttings and sample management and drive electronics. In this paper the latest development will be reviewed including the piezoelectric actuator, cuttings removal and retention flutes and drive electronics.

Structural Health Monitoring (SHM) based on the vibration of structures has been very attractive subject for researchers in different fields such as: civil, aeronautical and mechanical engineering. System Identification (SI) and Vibration based Damage Identification (VBDI) are two main parts of SHM. A full-scale seven-story reinforced concrete (RC) wall building has been tested during October 2005 and January 2006 by the University of California at San Diego (UCSD). The building was excited through four historical California ground motions. The RC wall experienced different levels of damage, progressively under increasing intensity of ground motions. At different levels of damage, the building was subjected to ambient vibration tests and low-amplitude White Gaussian Noise (WGN) base excitation. In this study, the response of the structure to ambient vibration tests is used to identify damage using VBDD method. The frequency domain decomposition method (FDD) is used to identify the modal parameters of the building. Damage changes the modal properties (frequency, mode shape and damping) by reducing the stiffness. Therefore, changes in the vibration characteristics of the structure can be used to identify location and severity of damage. A mode shape curvature-based method is used to detect and localize damage. Also a data-driven technique based on Neural Networks has been developed to identify the damage in the structure. The results show a close correlation with the structural damage observed in the experimental study.

Source location technology has been broadly used for structure monitoring. Source location (damage detection) of the structure is a challenging task due to the complex underground conditions. The basic assumption in many currently available techniques is that the velocity of wave, which is generated by structural damage and propagates in the structure, is considered in the constant value to estimate the source location. If the wave velocity is assumed to be a fixed velocity in the target structure, significant error can occur for detecting the damage location. In this study, laboratory tests were performed to analyze the accuracy of the source location according to the wave velocities. This study proposes a velocity model with the receiver distance, with which we verify the accuracy of the source location. Our results from the proposed velocity model produce an erorr rate of only 4%, which is significantly lower than the error rate of the original source location with a varied velocity. This velocity model achieves a high degree of reliability in damage monitoring.

We investigate the nonlinear wave propagation through micro-cracks that are compressed by external forces by means of nonlinear ultrasonic modulation technique. The nonlinear modulated waves are generated by the truncation of the waves passing through cracks due to the opening and closing of the cracks, and the nonlinear ultrasonic modulation technique has been known to be effective in detecting finer cracks in comparison with other linear ultrasonic methods since the technique utilizes the breathing of the cracks rather than wave reflections or refractions. However, if the cracks are strongly compressed, the crack opening is hindered due to the excessive initial stress and the nonlinearity does not show up.

In this study, the improvement of the nonlinear modulation wave technique for the detection of micro-cracks under compression is devised. By analyzing photomicrographs of the cracks with crack width measuring algorithm, a realistic crack model is generated, and a chirp signal is applied to find the resonant frequencies which are used as the excitation frequencies. Experimental tests are conducted to verify the numerical results. The aluminum plate is compressed in the direction normal to the cracks’ lateral surfaces and is excited using piezoelectric patches attached on the surface aluminum plate. The experimental and numerical results show good agreement for various excitation frequencies and different compressions.

This article introduces an origami-inspired passive morphing wing concept that is designed, analyzed and fabricated via a single analysis-oriented computational framework ensuring kinematic feasibility and path-uniqueness of a targeted motion. Supposing a notional in-plane wing morphing problem to provide perimeter boundary conditions, a fractal origami pattern that offers large in-plane strain while providing high out-of-plane stiffness is proposed as a supporting mechanism. To enable computational design and analysis of a complex origami pattern, a script-based multidisciplinary design and analysis computational tool, Computational Aircraft Prototype Synthesis (CAPS) is employed. A mathematical description of a fractal origami pattern is formulated to create the highly-symmetric, initial geometry. Then, a nonlinear structural analysis with a truss model is carried out to understand the evolving origami structure that promotes the desired wing shape change. CAPS is then employed to increase the geometric fidelity of the resultant shape by algorithmically converting the reconfigured origami structure from an infinitely-thin plate representation into a composite made of finitethickness plates and compliant hinges with multiple material assignments. A prototype of the final deformed design is fabricated in its maximally-compressed configuration using a multiple-material additive manufacturing technique, guaranteeing tensile loading over the operating domain, and thereby desired path uniqueness. The design, analysis and fabrication carried out in this work demonstrate the potential of using origami for morphing wings. More generally, the workflow developed for this study is demonstrated as a viable approach for multidisciplinary design and analysis of complex aircraft components.

The random shape and distribution of the aggregates add large inhomogeneity into the mixtures. Large material composition variability will increase the risk of damages, such as cracking and rutting. This paper introduced a digital image processing based HMA mixture material composition variability analysis method as a new material performance evaluation supplementary tool. The low temperature performances of six types of asphalt pavement materials have been evaluated through beam bending tests. Through comparing with the beam bending lab test data, it has shown that material composition variability has direct influence on the variability of the strength of the HMA beam bending test specimens, and proved the effectiveness of the proposed variability analysis method.

Electrical impedance tomography (EIT) is recently demonstrated to be viable for damage localization over a spatial area. The algorithm reconstructs the spatial conductivity distribution within a defined boundary via boundary voltage measurements. To solve this inverse problem, a finite element model (FEM) conforming to the interrogated geometry is required. Previous studies on identifying a center crack’s propagation suggests that an FEM-updating strategy may help identify both the existence of a crack and the plastic zones formed around the crack’s tips. In this paper a data-driven algorithm is applied to automatically update the FEM. The selforganizing map algorithm is adopted to categorize the reconstructed conductivity data, tracing the boundary of the crack to be updated as material-absence. The EIT results from the updated FEM model are able to identify damage location and damage severity with desired accuracy.

Ultrasonic corrosion detection has been developed and widely applied in non-invasive tests in civil engineering. This paper demonstrates real time fiber optic ultrasonic corrosion detection on reinforcing rebar based on photoacoustic (PA) principle in non-invasive tests in civil engineering. The optical acoustic sensors are fabricated to monitoring the corrosion of rebar in concrete. This paper explores an approach to make an assessment for the level of rebar corrosion as well. From the experimental results, the trend of central frequency had a shift to lower based on the development of corrosion. Since the sensor can measure the rebar corrosion timely, it will have a significant step on structural health monitoring.

Soil nailing systems are a common way to stabilize slopes and construction pits. Their design is usually based on the mechanical equilibrium of a rigid body motion and therefore, only tensile stresses are considered and accompanying forces like bending (shear stresses) in the soil nails are neglected. Continuous strain measurements along nails could verify this assumption, but may not be performed using conventional sensing technologies. This paper reports about monitoring of a soil nailed slope stabilization using distributed fiber optic sensing. Soil nails in different anchoring horizons were instrumented and autonomously monitored over several weeks, in which the construction pit was excavated continuously. After the excavation, the final load bearing capacity of one selected nail was determined within a classical geotechnical load test. In addition to the field measurements, the bending behavior of the instrumented nail system was analyzed under laboratory conditions. The presented studies demonstrate the high potential of distributed fiber optic sensing systems and their capability to extend traditional measurement methods in foundation engineering applications.

A corrosion sensor utilizing fiber optics and the magnetic attraction force is proposed. The sensor aims to detect the internal corrosion of pipelines that are made of ferromagnetic materials. Its components include a beam made of a non-magnetic material, a strong permanent magnet, and a Fiber Bragg Grating (FBG) sensor that is very sensitive to strain changes. The sensor is based on the assumption that the magnetic attraction force generated between a magnet and a ferromagnetic material decreases if the thickness of the ferromagnetic material is decreased. To generate this force between the sensor and the pipe, the beam is positioned in a way that the magnet is only few millimeters away from the pipe. The internal corrosion causes a reduction in the thickness of the interior pipe wall, which according to the assumption should reduce the attraction force. As a result, the strain measured by the optical fiber will be affected as it is directly related to the variations in force. We present an initial numerical investigation of the feasibility of the proposed working principle utilizing a Finite Element Analysis (FEA) simulation tool. Simulation results show that the attraction force first increases then saturates with the increase in wall thickness. The change in force becomes significant once the thickness reduces to a threshold value. We also investigate the effect of changing the magnet size, magnetic permeability of pipe material, separation distance between pipe and magnet, and the magnetic flux density of the magnet.

Long time lasted micro vibration will easily cause the looseness of bolts. It is critical for the health and safety monitoring of bolt looseness. Although many remarkable bolt looseness detection approaches already been developed in recent years, it is still difficult to the quantitatively evaluate the bolt degradation without do any harm to the bolt. In this article, a FBG (Fiber Bragg Grating) embedded smart washer was proposed to monitor the looseness of bolt connection and the theory analysis were induced to verify the linear tendency of smart washer annular strain. Two experiments were carried out to verify the analysis: first of all, the relationship between applied torque and bolt tension were investigated. A smart bolt which made by embedding FBG sensor into the bolt was used in the specimen and the experiment result indicates that bolt axial tension is linearly with applied torques which also consistent with previous researches. Then, the experiment which fabricated with smart washer and smart bolt was implement. The linear relationship between pre-load change and the wavelength increment was obtained. The analytical and experimental results both demonstrate that the proposed novel approach in this study is very sensitive to the bolt pre-load degradation and it is robust in monitoring the blot looseness.

For laboratory demonstrations, Lamb wave detection using fiber Bragg grating (FBG) sensors is typically performed with only the grating location spot bonded and with the fiber axis aligned with the ultrasonic propagation direction. However, in reality, the entire length of fiber is often bonded to protect the fiber from any environmental damage, referred to here as continuous bonding. Theoretically, the Lamb wave signal can couple to the guided traveling wave in the optical fiber at any adhered location, which could potentially produce output signal distortion. In this paper, we investigate the impact of continuously bonding a long length of optical fiber on the measured Lamb wave signal detected by an FBG. Therefore, an experiment is performed to measure the Lamb wave signals excited from a PZT actuator using a surface bonded FBG with varying optical fiber bond length, indicating that the output FBG response remains constant with changing length. The second experiment investigates the FBG angular response to the traveling wave in the optical fiber, and compares to the conventional case where FBG directly measures the Lamb waves with varying angle. Specifically, the optical fiber is bonded to the plate at a distance away from the FBG. The Lamb wave is launched to the bond location with varying angles, which is coupled to traveling wave, then measured with FBG. The results indicate that the mechanism of the Lamb wave transfer to the directly bonded FBG is through displacement matching, whereas that of the traveling wave is through a forced excitation.

Welded I-section steel members are widely used in engineering, and it is prone to fatigue damage at the web/flange junction under cyclic load. Due to the sudden damage and the heavy losses, it is necessary to detect the damage in time. Based on the study of stress wave propagation mechanism in steel beam, a broadband impact wave is generated by a hammer striking on the surface of a steel I-beam, and the fiber interferometer is attached to the surface of steel beam to receive the stress wave signal. By analyzing the impact echo signal spectrum with FFT, the existence and location of the damage is determined. Experimental results show that it is feasible to detect the damage at the flange/web junction of the steel I-beam by impact-optic method.

We report a low-cost and compact extrinsic Fabry-Perot interferometer-(EFPI) based optical fiber sensor for measuring three-dimensional (3D) displacements, including interfacial sliding and debonding during a delamination process. The idea is to use three spatially-arranged EFPIs as the triaxial displacement sensing elements. The sensor consists of an optical fiber component and a mirror component. The fiber component includes three optical fibers, and their corresponding mirrors form the mirror component. Two coincident roof-like metallic structures are used to support the two components, making sure that the endfaces of the fibers and corresponding mirrors maintain a parallel relationship during measurements. As a result, three EFPIs are formed by the endfaces of the optical fibers and their corresponding mirrors. The prototype sensor was first calibrated, and then an experiment monitoring the interfacial sliding and debonding between a long square brick of mortar and its steel base plate support during the drying/curing process was conducted to demonstrate the practicability of the sensor. The experimental results show that our sensor can function continuously for a long period of time. The details obtained from the measured data were also discussed. The robust and low-cost three-dimensional sliding and debonding sensor has a high potential in various applications, especially in structural health monitoring.

Recent advances in the fields of nanocomposite technologies have enabled the development of highly scalable, low-cost sensing solution for civil infrastructures. This includes two sensing technologies, recently proposed by the authors, engineered for their high scalability, low-cost and mechanical simplicity. The first sensor consists of a smart-cementitious material doped with multi-wall carbon nanotubes, which has been demonstrated to be suitable for monitoring its own deformations (strain) and damage state (cracks). Integrated to a structure, this smart cementitious material can be used for detecting damage or strain through the monitoring of its electrical properties. The second sensing technology consists of a sensing skin developed from a flexible capacitor that is mounted externally onto the structure. When deployed in a dense sensor network configuration, these large area sensors are capable of covering large surfaces at low cost and can monitor both strain- and crack-induced damages. This work first presents a comparison of the capabilities of both technologies for crack detection in a concrete plate, followed by a fusion of sensor data for increased damage detection performance. Experimental results are conducted on a 50 50 5 cm³ plate fabricated with smart concrete and equipped with a dense sensor network of 20 large area sensors. Results show that both novel technologies are capable of increased damage localization when used concurrently.

Many cities seek utilities monitoring with centrally managed Internet of Things (IoT) systems. This requires the development of many reliable low-cost wireless sensors, such as water temperature and flow meters, that can transmit information from subterranean pipes to surface-mounted receivers. Traditional radio communication systems are either unable to penetrate through multiple feet of earthen and manmade material, or have impractically large energy requirements which necessitate either frequent replacement of batteries, or a complex (and expensive) built-in energy harvesting system. Magnetic signaling systems do not suffer from this drawback: low-frequency electromagnetic waves are shown to penetrate well through several feet of earth and water. In the past, these signals were too weak for practical use; however, this has changed with the recent proliferation of high-sensitivity magnetometers and compact antennas using mechanically actuated rare-earth magnets. A permanent magnet can be either rotated or vibrated to create an oscillating magnetic field. Utilizing this phenomenon, two flow meter designs are proposed: one which uses a propeller to directly rotate a diametrically magnetized neodymium magnet; and, another which uses an oscillating tail to move a permanent magnet back-and-forth across a novel soft-magnet Y-stator, which projects a switching magnetic field. These oscillating magnetic fields are used to send water flow rate information to an above ground LoRa-capable Arduino receiver equipped with a magnetometer. Simulation software is used to model the oscillating electromagnetic fields. Complete system performance with remote datalogging is tested, with the aim of integrating many sensors and surface receivers into a single LoRa wireless IoT network.

A smart city integrates diverse sets of information and communication technologies to monitor asset condition, security, safety, service quality, and operational efficiencies, often in real time, which requires significant information from infrastructure road sensors. In this study, an infrastructure embedded sensor network will be developed to provide real-time traffic and road condition information such as traffic volume (e.g. ADT, peak-hour traffic), traffic composition (vehicle classification), vehicle speed, dynamic weight via weigh-in-motion (WIM), and other data. Robust fiber optics-based infrastructure sensors will be used as a candidate for the embedded sensor network. Detail sensor network design, data analysis for traffic information abstraction will be performed followed by field testing for validation of the network. The developed infrastructure sensor network can be used to support the application of smart cities with future efforts in fields.

Distributed optical fiber sensors are an increasingly utilized method of gathering distributed strain and temperature data. However, the large amount of data they generate present a challenge that limits their use in real-time, in-situ applications. This letter describes a parallel and pipelined computing architecture that accelerates the signal-processing speed of sub-terahertz fiber sensor (sub-THz-fs) arrays, maintaining high spatial resolution while allowing for expanded use for real-time sensing and control applications. The computing architecture described was successfully implemented in a field programmable gate array (FPGA) chip. The signal processing for the entire array takes only 12 system clock cycles. In addition, this design removes the necessity of storing any raw or intermediate data.

Condition assessment of civil infrastructures is difficult due to technical and economic constraints associated with the scaling of sensing solutions. When scaled appropriately, a large sensor network will collect a vast amount of rich data that is difficult to directly link to the existing condition of the structure along with its remaining useful life. This paper presents a methodology to construct a surrogate model enabling diagnostic of structural components equipped with a dense sensor network collecting strain data. The surrogate model, developed as a matrix of discrete stiffness elements, is used to fuse spatial strain data into useful model parameters. Here, strain data is collected from a sensor network that consists of a novel sensing skin fabricated from large area electronics. The surrogate model is constructed by updating the stiffness matrix to minimize the difference between the model’s response and measured data, yielding a 2D map of stiffness reduction parameters. The proposed method is numerically validated on a plate equipped with 40 large area strain sensors. Results demonstrate the suitability of the proposed surrogate model for the condition assessment of structures using a dense sensor network.

Structural health monitoring (SHM) enables wide-area, in situ, and continuous evaluation of the health of mechanical, civil, and aerospace structures to detect the existence, location, and severity of damage. In this paper, we introduce a sparse and scalable sensor network, driven by custom ultra-low power and highly integrated CMOS transceivers, that is suitable for a variety of active SHM applications using ultrasonic guided waves. The transceivers both generate electrical actuation signals for piezoelectric transducers and provide broadband lownoise reception of returned signals. Specifically, the transmitter can generate narrow-band Hanning-windowed sinusoids (5 cycles long) up to 12.7 Vpp with a center frequency in the 100 kHz to ∼2.8 MHz range. The waveform is synthesized using filtered pulse-width modulation (PWM), which is integrated with a programmable phasedlocked loop (PLL) to achieve low distortion and high repeatability. The fully-differential low-noise receiver is capable of performing a Hilbert transform on-chip to extract both amplitude and phase information from the received signal. For long term monitoring, we propose a two-step SHM strategy, in which damage is first detected using environmentally compensated data and is then localized and characterized. Furthermore, we discuss two commonly-used SHM damage localization algorithms, namely RAPID and delay-and-sum, in terms of computation, memory, and power consumption for the proposed sparse SHM networks. The proposed approach has been effectively demonstrated both in simulations and in experiments on an SHM test bed.

This work describes modeling of non-linear damping behavior in hybrid beam structures with SMA embedded layers. The dependency of the intrinsic damping capacity of SMAs on the strain is simulated through modeling of material’s hysteresis cycle. The hysteresis cycles and the corresponding loss-factors have been investigated through uniaxial tensile test and have been reproduced and validated by numerical simulation. To do so, a phenomenological hysteresis model has been developed and implemented in ABAQUS through UMAT interface. Finally, the method was used to simulate the damping of hybrid beam structures with SMA embedded layers and the results were compared with experimental data.

A low-cost and robust extrinsic Fabry-Perot interferometer (EFPI) based optical fiber displacement sensor with a wide measurement range, up to cm level, is reported in this paper. A gold-coated reflective mirror fixed inside the metal shell of the sensor, together with the endface of a single mode fiber form a Fabry-Perot cavity. The optical fiber is supported and oriented by a fiber ceramic ferrule, and the sensor is packaged and protected by a metal shell. A triangle geometry based displacement transfer mechanism is employed in the constructed sensor which makes the sensor capable of measuring wide-range displacement, and this triangle geometry based structure allows the measurement range of the sensor to be flexibly adjusted. When the measurement handgrip of the sensor experiences a displacement, the cavity length will change due to the triangle geometry design. By tracking the change in the reflection spectrum, the displacement can be determined. The experimental results show that a displacement measurement sensitivity of 42.68 nm/μm (change in EFPI cavity length/displacement magnitude) over a measurement range of 2.0 cm was achieved by the constructed prototype sensor. The present wide-range displacement sensor with low cost, high robustness has a great potential in the chemical-oil industry, the construction industry.