This PDF file contains the front matter associated with SPIE Proceedings Volume 11591, including the Title Page, Copyright information, and Table of Contents.

Over a hundred years ago, the pioneers of aviation took flight in no small part due to material innovations ranging from novel casting of the aluminum engine block to judicious selection of natural materials. Unquestionably, the future of aerospace will look as different from today as the Wright Flyer and Curtiss June Bug differ from UAVs and F35s. However, the role of materials will remain unchanged; they will be the crucial ingredient that enables these future machines to push the performance envelope and become active partners with the human operator. The presentation will share thoughts on how the materials enterprise could accelerate this future through convergence of innovations in biotechnology, nanotechnology, automation, artificial intelligence, and infomatics.

Military service members, whether in training or deployed, are exposed to extreme conditions that jeopardize their health, safety, and performance. New sensors that enable real-time monitoring of warfighter capabilities will not only enhance their performance but also prevent injuries. This presentation showcases how stimuli-responsive materials coupled with unique measurement strategies/algorithms can provide new data streams and insights on warfighter health and performance. The focus will be on recent developments in nanocomposite wearable sensors for physical and physiological monitoring. In addition, densely distributed motion and muscular engagement monitoring can be achieved when wearable sensors are coupled with tomographic methods.

An Earth-independent permanent extraterrestrial habitat system must function as intended under continuous disruptive conditions, and with significantly limited Earth support and extended uncrewed periods. Designing for the demands that extreme environments such as wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts (direct or indirect), vibrations, and solar particle events, will place on long-term deep space habitats represents one of the greatest challenges in this endeavor. This context necessitates that we establish the know-how and technologies to build habitat systems that are resilient. Resilience is not simply robustness or redundancy: it is a system property that accounts for both anticipated and unanticipated disruptions via the design choices and maintenance processes, and adapts to them in operation. We currently lack the frameworks and technologies needed to achieve a high level of resilience in a habitat system. The Resilient ExtraTerrestrial Habitats Institute (RETHi) has the mission of leveraging existing novel technologies to provide situational awareness and autonomy to enable the design of habitats that are able to adapt, absorb and rapidly recover from expected and unexpected disruptions. We are establishing both fully virtual and coupled physical-virtual simulation capabilities that will enable us to explore a wide range of potential deep space SmartHab configurations and operating modes.

Road quality is one of the most important factors influence the behaviors of vehicles, which may increase the safety of the autonomous vehicle with adaptive cruise control (ACC) model if take into consideration. This paper tends to investigate the impact of ride quality on the driving behavior of autonomous vehicles through the simulation tool, VISSIM. The road impact factor (RIF), a road quality index detected from the accelerometers on the smart mobile devices placed on regular vehicles, is used to describe the road quality. If the detected peak RIF is over a certain value, the communication to the autonomous vehicle will indicate a need to reduce the speed in the micro-simulator “Verkehr In Städten – SIMulationsmodell” (VISSIM) simulation. The difference between the driving behavior of the ACC vehicles with and without knowing the ride quality will be used to evaluate the impact of ride quality on driving behavior of autonomous vehicles through simulation.

Aging water transmission systems in the United States urgently require more efficient approaches to risk management to ensure that systems can operate without failure. Water utilities with spatially vast transmission systems collect extensive data related to the operation of the system, including internal fluid pressures. Combined with representative models that describe both the behavior and condition of the transmission system pipes, a reliability framework can be used to quantify the probability of failure of pipe segments. This work adopts transient pressure data to develop a statistical model of the maximum internal fluid pressures present in a transmission system. This data is combined with an analytical multi-layered ring model of the system pipes to estimate the system reliability. The study adopts the transmission system of the Great Lakes Water Authority (GLWA) to illustrate the use of the reliability framework to estimate the probability of failure of prestressed concrete cylinder pipe (PCCP) segments which have previously experienced failures in many systems across the US.

This paper investigates the use of non-contact laser vibrometers for continuous or periodic bridge monitoring and damage detection through the detection of changes in the bridge's natural frequencies. For this purpose, the vibrations of a full-depth precast-prestressed voided slab bridge and a steel girder bridge were measured under normal traffic using a laser vibrometer. Detailed finite element (FE) models were developed to study the bridge dynamic behavior for various damage scenarios, including longitudinal joint damages in a concrete bridge deck and a full-depth girder fracture in a steel girder bridge. In the concrete bridge deck, longitudinal reflective cracking on the deck surface and leakage had hinted at some level of damage at the joints. The bridge dynamic analysis showed that modal parameters in bridges with precast deck panels are sufficiently sensitive to changes in the state of the panel joints and, therefore, can be used for identifying joint damage. The results clearly confirmed the visible evidence of the damage. For the steel-girder bridge, the results of the dynamic analysis also indicated a noticeable frequency change in the bridge's natural frequencies after a fracture at a level that can be effectively detected with bridge frequency monitoring. Moreover, the proposed approach proved to be more cost-effective and less disruptive than standard monitoring approaches and, at the same time, capable of predicting damages from relatively small vibration amplitudes. This approach can be used to warn of potential damages between planned inspection intervals and to detect damages otherwise hidden from the eye during routine inspections.

Robust and adaptable sensor technology is essential for achieving meaningful structural health monitoring (SHM) and integrated nondestructive evaluation (NDE). Unfortunately, prevailing sensor technologies are most often pre-packaged and therefore lack much adaptability. In other words, sensors are rarely structure-specific or application-specific. Rather, an existing pre-packaged sensor must be retrofit to the component or structure under inspection. Multifunctional additive manufacturing (AM) has immense potential to overcome this limitation by permitting stimulus-responsive materials to be printed onto or directly embedded within structures for application-specific sensing. Herein, we explore this concept for strain sensors fabricated via multifunctional AM. Specifically, pelletized polylactic acid (PLA) is modified by the addition of carbon nanofibers (CNFs) at 7.5% by weight. This modification is done through a dry-mix process which is followed by multiple reclaiming and re-extrusion cycles through a single-screw filament extruder. Through this process, the CNFs form an electrically conductive network within the PLA structure. Because the electrical conductivity of the CNFmodified PLA is deformation-dependent (i.e. the material is piezoresistive), the sensors printed from CNF/PLA filament can be leveraged for strain sensing. In this work, we utilize a commercially available fused deposition modeling (FDM) printer to print the CNF-modified PLA into small and thin dog-bone shapes. These sensors then are adhered to a comparatively stiff substrate such that resistance changes across the sensor can be monitored as a function of strain as the substrate is deformed within a load frame. Our preliminary results show that AM-produced CNF-modified PLA strain gauges can indeed be used to track strains consistently. These successful preliminary results show that multifunctional AM has considerable potential for the development of highly adaptive, application-specific, and on-demand sensing technology.

This research evaluates the influence of phase fluctuation of a high-speed smart gear on the return loss of monitoring antenna in our developing “smart gear sensor system”. This smart health monitoring system of gear comprising of an antenna connected to a network analyer and another similar antenna integrating crack detection sensors directly printed on a gear. The principle of this system is based on magnetic coupling phenomenon between samples of antenna and crack detection sensors with antenna printed on polyacetal (POM) plates so that the characteristics of the gear side elements can be obtained wirelessly. In this paper, the effect of the relative phase fluctuation when the smart gear is operating at high speed on this coupling signal perhaps is considered. Practically, a special experimental rig consists of a motor with a speed control button, an antenna on a polyacetal (POM) plate with a radio frequency connector, and a smart gear with integrated sensors antenna was created. Thanks to this test rig, the smart gear is able to rotate with variant rotational velocities in range of 500 rpm to 5000 rpm while keeping pairing concentrically with the fixed monitoring antenna at a constant distance. Then, return losses received from this antenna via the network analyzer then are recorded at each specific speed. In comparison with the return losses gained in case of similar experimental setting but in stationary state, the shape form of the high-speed phase fluctuation return loss is indicated.

The development of a flexible surface acoustic wave (SAW) sensor as microelectromechanical systems (MEMS) device has been gradually emerged due to its unobtrusive size, passive, and wireless competencies. The concept behind this work is to additively develop a flexible surface acoustic wave (SAW) device with enhanced electromechanical properties capable of detecting mechanical strain occurring in aerospace applications. The nanocomposite substrate is made of polyvinylidene fluoride (PVDF) owing to its flexibility, piezoelectricity, long-term stability, and easy processing incorporated with carbon nanotubes (CNTs) as nanofillers. Adding CNTs to the polymer matrix for electromechanical properties enhancement is investigated through additive manufacturing (AM) process. Both the thin substrate and the interdigital transducer (IDT) are fabricated through direct digital manufacturing (DDM), exhibiting favorable piezoelectric and electrical properties. Various device characteristics of fabricated SAW sensor, including the generation and propagation of Rayleigh waves and the changes in wave characteristics, such as frequency, admittance, and impedance, are discussed in this paper. The effects of IDT dimensions and the resonant frequency response of the developed SAW device are also examined with numerical analysis.

High-rate dynamic systems undergo events of amplitudes greater than 100 g_s in a span of less than 100 ms. The unique characteristics of high-rate dynamic systems include 1) large uncertainties in the external loads, 2) high levels of non-stationarity and heavy disturbances, and 3) unmolded dynamics generated from changes in the system configurations. This paper presents a deep learning algorithm consisting of an ensemble of long short-term memory (LSTM) cells used to conduct high-rate state estimation. The ensemble of LSTMs receives and transforms the signal into inputs of different time resolutions. Each input vector correlates to an LSTM cell which predicts the signal in real-time and produces feature vectors. The feature vectors are then processed through an attention layer and dense layer to predict the physical features of the system. Here, we study the temporal evolution of the attention layer weights to conduct state estimation, while the LSTM cells are attempting to conduct measurement predictions. We study the performance of the algorithm on experimental data generated by DROPBEAR, a dedicated testbed for high-rate structural health monitoring research. State estimation consists of estimating, in real-time, the location of a cart that moves along a beam. Results show that the attention layer weights can be used to estimate the cart location but that the beam requires impact excitations to accelerate the convergence of the algorithm.

Ultrasonic metal welding (UMW) is becoming a more broadly used technology for joining ductile materials, especially in the electric vehicle sector. The process, however, lacks monitoring capabilities that would improve confidence in the repeatability of welded joints. Often only destructive testing is used for quality evaluation. This method of inspection is insufficient for a production line and can lead to high scrap rates and failure to identify all poorly welded components. The work discussed in this paper aims to close the gap of weld quality evaluation for UMW through in-process monitoring. Multiple sensors were installed in line with a linear ultrasonic metal welder. Current, voltage, frequency, shear force, and displacement of the horn both laterally and vertically were monitored throughout welding trials. Parameters for expected overweld, underweld, and acceptable weld qualities were selected through screening trials and design of experiment (DOE) methods. Twenty welds from each of these three quality sets were made with all monitoring tools for several rounds of testing. Destructive analysis was used to confirm the weld quality for each experiment. This included peel testing by hand for qualitative results and mechanical peel testing for quantifiable results, as well as metallographic analysis of the cross section and weld interface. Signal analysis, performed for each set of sensor data, extracted unique features that may be correlated to the input weld parameters and resulting weld quality. Machine learning techniques were applied on these features to classify weld quality based on in-process monitoring data. Algorithms predicted weld quality with over 90% accuracy.

This paper demonstrates a diagnostic-prognostics framework to estimate probabilistic remaining useful life (RUL), in adhesively bonded joints subjected to fatigue loading, by calibrating the predictive model using the diagnostics data and quantifying uncertainty in the model parameters. The matching pursuit algorithm is used as the diagnostic method to measure the crack length and the rapid fatigue damage growth (FDG) simulator is used as the predictive model to estimate the remaining useful life (RUL). In the diagnostic method, Lamb waves are excited in the structure using piezo transducers, and the matching pursuit algorithm is used to quantify the damage from the reflected signal. The proposed diagnostic technique is verified using the signal obtained from finite element simulations and artificial noise is added to mimic the signal from a real structure. The diagnostic method is applied periodically to measure the crack length in the single lap joint (SLJ) subjected to fatigue loading and the crack length data is used to calibrate the parameters of the predictive model, which can estimate the RUL. However, the noise in the signal and assumptions in the diagnostic technique result in errors in the measured crack length. These errors in the crack length contribute to the parameter uncertainties during the predictive model calibration. To quantify the model parameter uncertainties, the Bayesian inference via the Markov chain Monte Carlo method is used, and to expedite the uncertainty quantification problem, the rapid FDG simulator is used as the predictive model. The approach is demonstrated using a fatigue damage growth simulation in the SLJ and promising results were achieved.

It is intended to identify the change of parameters of a structural model with passive control devices. Two methods were proposed to the parametric identification in this study. One is the recursive hybrid Genetic Algorithm and the other is the moving window hybrid Genetic Algorithm. In the development of these two algorithms, the time histories of the measurements were divided into a series of time intervals, and then the model of equivalent linear systems were employed to identify the modal parameters of the systems for each time interval. These two methods have different ways in dividing the time histories of the measurements. For the second one, the time histories of the sequential intervals are overlapped. Finally, these two methods are applied to the three-story structural models with added-damping-and-stiffness devices mounted on the shaking table. The ground motion records used for these models are time histories of El Centro earthquakes adjusted to different intensities. The results showed that the frequencies vary with the intensity of the ground motion and reflect the nonlinear behavior of the systems. The comparison of the results of the both methods showed that the change of the parameters for each interval are not smoother when compared to the results of the first method.

This paper investigates the concept of an acoustic sensor network that can monitor a variety of geophysical processes occurring on other planetary bodies. In many cases sound is naturally omnidirectional and travels at known speeds which depend on the composition and density of the atmosphere. The differences in the time of flight of signals received by a distributed microphone network can be used to locate the source of the sound. We suggest this property is ideal for mobile planetary robots and can be used to expand the exploration envelope considerably by directing camera pointing or rover path planning thus extending beyond line-of-sight exploration. Acoustic signatures have been used in a variety of fields (eg. sonar, heavy machinery) to identify and catalog sounds associated with a specific vessels and malfunctioning machinery. Our ears have cataloged hundreds of sounds and we continuously use these sounds both consciously and subconsciously to extract information about our surroundings. This paper investigates the use acoustic measurements on other planetary bodies that could be used to characterize specific environmental parameters such as rain droplet size, wind speed, thunder, or dust devil vortex diameter. The paper identified other important sound sources that are thought to occur on other bodies in our solar system include; booming or singing dunes, waves, rivers, streams, fluidfalls, geysers, hurricanes, tornados, ice flow, volcanoes, planetary quakes, avalanche, rock slides and ice cracking. In addition, this paper focused on issues associated with the development of appropriate sensors for the network including the specification of the sensitivity, frequency response, and directional response of each of the microphones in the network in order to aid in localization of sound sources. We also presented our initial development of the transducers for potential mission targets including Mars and Titan and investigated the use of signal processing techniques including windowing, time frequency plots and correlation techniques to resolve phase differences between sensors in the network to aid in localization. We also identified additional benefits of these sensor networks in that they could used as engineering sensors to diagnose mechanical malfunctions on a rover or lander actuators or mechanisms. We also noted that they could also enhance public outreach by adding sound to videos.

A non-contact, full-field vision-based non-destructive inspection (V-NDI) system was developed with multiple damages detection capabilities in composite structures under thermal excitation. In contrast to point-based nondestructive inspection (P-NDI) systems employing laser Doppler vibrometer (LDV) with discrete wavefield captured by pointwise scanning, the V-NDI system captures higher spatial resolution wavefield by a CMOS camera for every time instance without repeating the experiment tens of thousand times to reassemble the wavefield like P-NDI. An Advanced Damage Processing Network (ADPNet) was proposed with laser speckle photometry (LSP) employed in a V-NDI system for hidden damages inspection. The LSP/ADPNet system relies on observing the variation of speckle clouds in time sequence without a baseline and is very insensitive to ambient noise with statistics-based image processing where traditional holography/ESPI suffers greatly. Other advances of LSP/ADPNet system are its robust tolerance of laser coherence, larger illumination area, flexible choice of correlation functions, and more advanced post-processing techniques such as Bayesian updating/inference or unsupervised image segmentation that can be readily applied. Thermal excitation can have very large power throughput in hundreds of watts compared to traditional PZT actuator, merely in a few watt ranges. Laser speckle itself is the result of self-interference scatter field reflected from a rough surface, and each speckle can be treated as a sensing point from a randomly distributed speckle cloud. By observing the variation of speckle cloud on the structure surface, displacement related quantities in higher dimensions (e.g. hypercomplex envelope, phase between real-valued signal and its quadrature, phase congruency, etc.) can be deduced by the Riesz bp transform and correlated in time sequence to highlight the location of hidden damages in a very effective way. Another novelty of this paper is to make a super compact real-time LSP system on LabVIEW FPGA by applying ADPNet comprised of the Riesz bp transform, non-linear filter bank and unsupervised image segmentation to quantify/characterize barely visible impact damages (BVID) on a C-17 Globemaster III composite aileron. To conclude, the images processed by LSP/ADPNet of a V-NDI system show a very good agreement with ultrasonic C-scan and pulse laser/LDV wavefield reconstruction results. It is also demonstrated to be more accurate and robust than Digital Image Correlation (DIC) for minute deformation (sub-nano to nano meter) measurement and large area (meter by meter) inspection under industrial environment.

This paper explores laser speckle photometry (LSP), a recent optical-based image analysis tool, as a method for detecting barely visible impact damage (BVID) in composite structures. This non-contact optical-based method provides the potential for large-scale scanning of aircrafts in real time to unearth BVID which would typically go unnoticed during routine inspections. In exploring LSP, various image correlation algorithms were tested to determine the most effective. The typical error-based correlation algorithm of mean squared error (MSE) was compared to two more advanced algorithms, normalized cross-correlation (NCC) and structural similarity (SSIM) index. Thermal LSP was conducted on a composite honeycomb panel with a surface dent of maximum depth 0.5 mm. The underlying damage was around 30 mm in diameter. When compared to the baseline experiments conducted using C-Scan and a laser Doppler vibrometer (LDV), it was found that only with limited cooling (around 2 seconds) did the algorithms produce consistently accurate results. Among the three imaging conditions, MSE and certain configurations of SSIM provided results that aligned the best with the baselines. Upon extended cooling, LSP produces too many false positives to be useful regardless of correlation algorithm used. Nevertheless, LSP shows great promise as a real-time non-destructive inspection tool not only in the aerospace industry but also in industries such as additive manufacturing where defects are prevalent.

The paper is aimed at developing a probabilistic framework for fatigue life prediction in adhesively bonded joints by calibrating the predictive model, governing adhesive fatigue behavior, using the set of experimental data, and quantifying uncertainty in the model parameters. A cohesive zone model (CZM) is employed to simulate the fatigue damage growth (FDG) along the adhesive bondline and Bayesian inference is used for uncertainty quantification (UQ). The fatigue behavior predicted by FEA modeling for high cycle fatigue, in particular, is computationally intractable, not to mention the inclusion of UQ. To enhance the computational efficiency and yet retain accuracy, a rapid FDG simulator is developed for adhesively bonded joints, by replacing the computationally intensive strain field calculations with the artificial neural networks (ANNs) based surrogate model. The developed rapid FDG simulator is integrated with Bayesian inference and the integrated framework is verified by quantifying uncertainty in fatigue model parameters using the experimental fatigue life data of a single lap joint (SLJ) configuration under constant amplitude fatigue loading. The quantified parameter uncertainties are then used to predict the probabilistic fatigue life in the laminated doublers in bending joint configuration, fabricated using similar adhesive material as SLJ, and successfully comparing it with the experimental data.

Planetary protection of returned Mars samples to Earth in a future NASA mission is a critical part of preventing uncontrolled biological materials being released from the samples. The planetary protection process requires addressing the potential risks and would involve “breaking the chain of contact (BTC)”, where any returned material reaching Earth for further analysis would have to be sealed inside a container with extremely high confidence. The sterilization process would require destroying any potential biological materials that may contaminate the external surface of the container. A novel process for containing the returning samples has been conceived and is in development at JPL. The process consists of using induction heated brazing to synchronously sterilize, separate, seam and seal the container.

The temperature distribution during a metal casting process is important because it influences structural properties and residual stresses of the casted part. In this paper, we report on multipoint temperature monitoring based on a regenerated fiber Bragg grating (RFBG) array during a copper casting process, with a maximum temperature exceeding 1100°C. A temperature calibration function up to 800°C was obtained experimentally and was linearly extended to 1200°C. According to the experimental results, the temperature information from an RFBG temperature sensor agreed well with that from a thermocouple that was located close to the respective RFBG sensor element, even at temperatures above 1100°C. With the RFBG temperature sensor array in a single fiber, the temperature distribution and its temporal development were obtained, which can be used to better understand the copper casting process and improve the casting mold design.

The growing interest in structural health monitoring (SHM) and the recent technological progress have encouraged the research community to study and develop innovative sensors and monitoring methods, like the acoustic emissions (AE) technique. The number of publications on this method has increased exponentially in the last decade. However, most of the experimental validations of AE techniques are based on tests carried out in laboratory conditions on specimens or individual structural elements, and the applications to full-scale bridges in operation are typically concerned with damage states that do not jeopardize their safety. In this paper, we analyze the results of AE monitoring of a full-size prestressed concrete highway bridge subjected to a load test up to its failure. The bridge was built in 1968 and regularly maintained over the years. It is representative, by type, age, and deterioration state, of similar bridges in operation on the Italian highway network. Based on these results, we discuss the effectiveness of AE monitoring of in-service structures under regular traffic and exceptional load transits. We aim to answer the following questions: (i) Can AE discriminate whether a viaduct has local damages, such as concrete cracks? (ii) Can AE identify damage initiation, for instance, during an exceptional load transit? (iii) Can AE provide qualitative and quantitative information on damage propagation? (iv) Is it worth to invest on AE monitoring rather than “traditional” monitoring, such as crack-opening sensors?

In this paper, we present structurally embedded gradient-index lenses integrated with a polymer (PVC) pipe for amplification of guided wave modes. The gradient-index lens is composed of equally spaced cylindrical metal inserts embedded into the pipe wall. All the inserts have the same height which is equal to the half-thickness of the pipe. The insert diameters are tailored in circumferential direction to realize hyperbolic secant distribution of refractive index around the circumference of pipe. We numerically demonstrate simultaneous amplification and focusing of the three commonly used pipe wave modes for ultrasonic inspection, namely L(0,2), L(0,1) and T(0,1), within a single lens design. Ultrasonic guided wave inspection of polymer pipes is challenging because of the high material damping which limits the sensor spacing and inspection range. To this end, we further investigate the damping properties of the PVC pipe through experiments. The structurally embedded design is promising in terms of long range transmission of ultrasonic signal which can remedy the attenuation problem in polymer pipes.

3D-printed one-way valves were designed and fabricated to relieve the corrosion-induced internal pressure on concrete structures. These valves were post-installed onto concrete to increase corrosion resistance in the concrete structure and extended the service life. This study investigated an Internet-of-Things device to continuously monitor corrosion in steel-reinforced concrete in order to determine the effectiveness of the valves in preventing corrosion. The IoT device monitors acoustic emission to determine the corrosion stage of reinforced concrete. The ongoing results show that the current valve design is an effective one-way check valve that will allow the internal pressure of the concrete to be released. This type of valve will prevent reinforced concrete surface cracking and extend the life of concrete structures by only releasing internal pressure without allowing for external materials to further corrode the steel reinforcement in concrete.

Composite materials with nanofiller-modified polymer matrices have received considerable research interest for structural health monitoring (SHM) and nondestructive evaluation (NDE) because they are intrinsically self-sensing. The electrical conductivity of these materials is affected by mechanical stimuli such as strain and damage. Coupled with spatial imaging techniques such as electrical impedance tomography (EIT), this self-sensing property has been extensively leveraged for conductivity-based damage detection. While EIT can spatially resolve damage-induced conductivity changes, it provides little-to-no information about the precise damage shape or size. A major consequence of this is that delaminations, which are a critical failure mode in composite laminates, are difficult to precisely localize and discern from other failure modes. In light of this limitation, we herein present a new methodology for precisely determining the shape and size of delaminations in self-sensing composites. Our novel technique uses a genetic algorithm (GA) to inversely compute delamination shape and size using boundary voltage measurements, EIT-imaged conductivity changes, and a physicsbased delamination model. In the present study, we explore the feasibility of this approach using numerical simulations. Our preliminary results show that this technique can accurately reconstruct delamination shape and size. The proposed methodology can enable precise damage characterization and immensely benefit SHM in self-sensing composites in aerospace, mechanical, and civil applications.

The inverse problem of electrical impedance tomography (EIT) is often a highly nonlinear ill-posed problem of image reconstruction and the imaging feature prior information is limited. Since traditional iterative algorithms are not able to process the blurred EIT images, a deep learning algorithm is proposed in this study that can autonomously learning important imaging characteristics by some representative samples to improve the image reconstruction quality. First, a PSPNet network based on MobileNet are preliminary proposed to the EIT image post-processing, some simulation experiments based on the EIDORS platform were designed to illustrate the effectiveness and universality of this algorithms. Then we used the NOSER algorithm to reconstruct images of different shapes of targets, including circle, triangle, square, and any combination of two shape targets. For these resulting images, we used PSPNet for postprocessing. Furthermore, we designed a tank test with the EIT systems. Through detection of single target and two target positions in three different positions, the proposed deep learning PSPNet algorithm can improve the Size Error (SE) index by 9.02% on average, and reduced the Position Error(PE) index by 1.59% compared with the traditional NOSER algorithm. It can be concluded that the deep learning scheme PSPNet can effectively recognize the object positions and recover the sharp contour of targets very well.

Local scour is arguably the most pressing issue regarding the safety and longevity of overwater civil infrastructure. Many modern scour detection techniques do not provide continuous scour depth measurements, nor can they function under extreme flow conditions, which is when scour monitoring becomes most critical. Thus, the objective of this study was to develop scour depth monitoring sensors using ultrasonic time domain reflectometry (UTDR). The scour sensor was based on an aluminum strip with two piezoelectric macro fiber composites (MFCs) bonded at one end. The aluminum strip or rod-like sensor is intended to be driven and buried at the location where scour depth measurements are desired. The two MFCs were used to either generate or sense ultrasonic Lamb wave pulses propagating in the aluminum strip. During scour, as sediment is eroded from around the base of the strip, the distance (i.e., scour depth) between the MFCs and the soil interface would increase. The hypothesis was that increasing scour depth would change the mechanical impedance of the system to cause measurable and unique signatures in the residual Lamb wave signals. To test this hypothesis, different interfaces (i.e., metal-metal, polymer-metal, and soil-metal) were applied at different locations along the aluminum strip and MFC system. The MFC sensor-actuator pair was actuated to propagate and measure the corresponding Lamb waves during each test. The results showed clear changes in the residual signal, which were well-correlated to the changing locations of the artificial interface. In particular, the time-of-flight of the response pulse within the residual signature could be used to accurately determine the location of the soil interface or scour depth. Overall, this study demonstrated feasibility of an UTDR sensor for scour monitoring.

Moisture content inside concrete affects the properties of Portland cement concrete structures. It also indicates the likelihood of structural damages (e.g., freeze-thaw, steel corrosion) in reinforced and prestressed concrete structures. While several laboratory techniques (intrusive/destructive) are available for moisture determination, it is a challenging task to quantify the moisture content of concrete in real structures without using intrusive methods/techniques. In this paper, an SAR (synthetic aperture radar) image analysis approach using critical contour area for subsurface moisture sensing inside concrete specimens is proposed. Concrete specimens cast and conditioned in laboratory environments were air-dried for three months and continuously monitored for their moisture variation by SAR imaging. Dimensions of concrete specimens were 0.3 m-by-0.3 m-by-0.05 m. A 10.5-GHz center frequency radar system with 1.5 GHz bandwidth was used to generate SAR images of the concretes at various moisture contents from 0% to 3.85% (by mass). All SAR images of concrete specimens in this paper were collected inside an anechoic chamber. To determine the critical contour area (A_c) of an SAR image for subsurface moisture sensing inside concrete, a selection criterion is proposed and applied to SAR images of concrete specimens at different moisture contents (ψ). A critical SAR amplitude is chosen at the highest coefficient of determination (R-square, R²) for all values of moisture content. From our SAR imaging result, it was found that critical contour area increases nonlinearly with a linear increase of moisture content of concrete specimens.

Recently, numerous studies have been conducted on the UAV-based inspection of infrastructure including bridges, mainly in the United States, Europe and Asia. According to preliminary research and pilot projects, UAV-based bridge inspection has advantages such as safety, cost-time efficiency and access to hazardous areas. Also, above all, it enables objective assessment of the condition of the structure from captured images. However, there are several limitations to the practical application of UAV-based bridge inspection. In particular, damage present in areas where images have not been captured can lead to inappropriate condition assessment. Therefore, it is necessary to check whether the UAV has captured all images for the inspection area. In addition, even if damage is identified in the captured image, it is necessary to map where the image is located on the bridge. In this study, a missing area detection and damage mapping methodology based on the estimated field-of-view using sensor data from UAV systems is proposed. The framework of the proposed methodology consists of a total of 4 phases. First, phase 1 is aimed at converting GPS data to the location of the camera during a UAV flight using IMU data. In phase 2, the coordinates of the center point of the image are determined from the gimbal IMU data and working distance. And phase 3 is the process of calculating the field-of-view through the camera's focal length and working distance. In the preceding phases, the coordinates of each image captured by the UAV are determined. Based on these, missing area detection and damage mapping are performed in phase 4. The proposed missing area detection and damage mapping methodology is experimentally validated for concrete shear wall with artificial damages and the actual target bridge. As a result of experimental validation, the proposed methodology detected areas for which image capture was missing and provided a result of mapping the identified damage within adequate accuracy.

Advances in computer prerformance, optical devices and digital imaging technology have allowed digital image correlation methods to measure the shape and deformation of the vibrating structures. This paper presents a novel vision-based damage detection framework which can eliminate the sinusoidal shaker step along with a magnified, precise modal shape data especially for higher modes that typical DIC algorithm can hardly detect. For this, an aluminum plate has been selected to make a comparative study on the performance of serval vibration-based damage detection techniques, namely, curvature mode shapes , Gapped smoothing techniques Generalized Fractal Dimension, Modal Strain Energy and Wavelet Transform.

Interest in blue energy harvesting systems is rapidly growing and becoming widespread given its promise as a renewable and clean energy source. Blue energy harvesting from raindrop impact would allow low-power systems to operate in remote areas without the need for battery replacement and related maintenance. Insufficient power output is the most critical limitation that makes the conventional types of rain energy harvester (REH) typically unusable or/and infeasible. To overcome this limitation, a bio-inspired metasurface skin is proposed in this paper to serve as the membrane for a piezoelectric type of REHs. The proposed metasurface membrane is comprised of a system of biaxial-cuts inspired by snake scale. The power enhancement of the bio-inspired harvester with a metamembrane was studied and compared to its equivalent conventional harvester with a plain membrane when the substrate was under raindrop pressure. The Finite Element Model (FEM) results showed that the metamembrane could transfer more stress deformation to the piezo-element layer, thus enhancing power output. This is attributable to the metasurface membrane polarizing the PVDF better than a conventional plain membrane because of its higher ability to stretch the PVDF. The proposed bio-inspired harvester could be used for different public facilities such as tents, umbrellas, awnings, temporary roofs, coverings, and tarps to provide power for sensing, lighting, signage, digital displays, etc., especially in heavy-rain regions.

Utilizing human motion as a source of energy is a potential method to extend the battery life of portable electronics. A number of energy harvesters have been developed in recent years to harvest energy over the knee joint. However, the overall efficiency of these devices is often low due to the noticeable increase in user effort. This increase is the result of the extra effort required to carry the device, as well as the inability of the harvester to match its resultant torque with the users’ natural knee torque. Here, a lightweight cable driven energy harvester over the knee with variable transmission ratio is introduced to harvest energy with minimal user effort. The variable transmission ratio is achieved with a variable radius drum (VRD) cable mechanism utilized to match the user’s and harvester’s effective knee torque over the gait. Power output and gait analysis testing are conducted with VRDs producing 20% to 100% of the natural knee torque, as well as without VRD. The results show a linear relationship between the 20-80% VRDs and power output, with the 80% VRD producing the highest power output at 0.35 W. Without a VRD, power output is around 0.22 W. Gait analysis shows the 20% VRD slightly reduces the impact of the harvester with a RMSE of 5.53° compared to 5.86° without VRD.

This paper investigates the feasibility of an electromagnetism energy harvester (EMEH) for scavenging electric energy from transportation infrastructures and powering of conventional sensors used for their structural health monitoring. The proposed EMEH consists of two stationary layers of three cuboidal permanent magnets (PMs), a rectangular thick aircore copper coil (COIL) attached to the free end of a flexible cantilever beam whose fixed end is firmly attached to the highway bridge oscillating in the vertical motion due to passing traffic. The proposed EMEH utilizes the concept of creating an alternating array of permanent magnets to achieve strong and focused magnetic field in a particular orientation. When the COIL is attached to the cantilever beam and is placed close to the PMs, ambient and traffic induced vibration of the cantilever beam induces eddy current in the COIL. The tip mass and stiffness of the cantilever beam are adjusted such that a low-frequency vibration due to the passing traffic can effectively induce the vibration of the cantilever beam. This vibration is further amplified by tuning the frequency of the cantilever beam and its tip mass to resonance frequency of the highway bridge. The numerical results show that the proposed EMEH is capable of producing an average electrical power more than 1 W at the resonance frequency 4 Hz over a time period of 1 second that alone is more than enough to power conventional wireless sensors.

Magnetostrictive materials show a dimensional change in response to changes in their magnetization. Terfenol-D (Tb_xDy_1−xFe₂ x ~ 0.3) is one of well-known magnetostrictive material which shows giant magnetostriction (>1000ppm), and the high magnetostriction is beneficial for actuator applications. In order to implement a magnetostrictive actuator in a real application, the maximum stroke and maximum force output of the actuator should be defined. One way to define actuator performance is to measure the load line performance, allowing the user to determine the suitability of the actuator for a particular application. Actuator load line is defined in terms of two key parameters, blocked force and free displacement. However, magnetostrictive materials have a highly nonlinear strain response, which is a function of both applied magnetic field and applied stress. Within a certain stress region, the strain response of Terfenol-D at a constant bias magnetic field may actually increase with increasing applied compressive stress, which is not the normal behavior of a linear actuator. In this study, the empirical dynamic load line performance of a Terfenol-D actuator with various driving conditions is evaluated. The load line performances are measured up to 204 kg of additional mass and the actuation frequency range of 0.5 Hz to 20 Hz. The trend of the load line is compared with simulation results based on static characterization of a separate Terfenol-D rod.

Masonry is a widely used architectural style for its decent characteristics such as high thermal resistivity and durability. However, its poor performance under horizontal load, for example, earthquake, has aroused public concerns for seismic hazard. We implement shaking table tests for two types of model, pristine and cracked, to investigate the seismic behavior of masonry structure as well as the effect of crack. Each model is equally designed to contain one main wall and two side walls. Only for the cracked model, however, an artificial line crack is created on each side wall during the fabrication. Biaxial horizontal seismic excitations are imposed, by gradually increasing the load until each model collapsed. The effects of the existing cracks and propagations are investigated, and specific crack patterns that impair the seismic resistance are identified.

An accurate numerical model that is able to represent real structural behaviors and reproduce structural responses with high fidelity is critical to numerous engineering applications such as damage detection, diagnosis, and prognosis, and data assimilation. While a wide variety of methods have been developed in the past decades for finite element model updating, a widely adopted concept is to solve a constrained optimization problem to minimize the prediction errors, such as those formulated by modal properties, frequency response functions, and static and dynamic responses, among others. This paper considers model updating for earthquake-excited building structures using incomplete acceleration measurements. In this case, due to the transient nature and limited duration of earthquake responses, as well as incomplete instrumentation and measurement noise, identifying accurate and adequate number of modal parameters for model updating is challenging. Furthermore, in presence of static nonlinear functions, model updating is a nonlinear inverse problem solved using nonlinear optimization methods. This often faces divergences due to issue of nonconvexity, resulting in unreasonable parameter estimations. Therefore, identifying an accurate model of building structure under earthquake excitation is still a challenge. In this paper, we propose a maximum a posteriori (MAP)- based approach using measured earthquake response time histories, which renders model updating as a regularized nonlinear optimization problem. The prior knowledge of structural parameters is incorporated to constrain the estimation. One main advantage of the proposed approach is that it makes no assumption of the upper and lower bounds while ensuring the physical meaning of the structural parameters. The proposed approach is validated through a numerical example.

Data acquisition methods for structural health monitoring (SHM) historically rely on schedule-based or transmit-all data collection strategies. For sensing systems that are self-sustaining (e.g. those relying on harvested energy), these data collection methods are unable to explicitly account for the availability of energy and the value of structural response data. As a result, sensing systems often fail to capture and transmit key data to end users and require an excessive amount of time to characterize statistical parameters of response data. As structural monitoring data is increasingly incorporated into decision-making processes for asset management, there is a need for an automated data collection and transmission strategy that facilitates the characterization of the statistical parameters of structural response data with minimum variance so that bridge managers can increase the frequency with which they track structural condition without compromising accuracy. This paper presents a stochastic data collection and transmission policy that minimizes the variance of estimated component parameters of a measured process subject to constraints imposed by a sensing node’s energy and data buffer sizes, stochastic models of the incoming energy and event arrivals, the value of data, and temporal death. This work then extends the optimal data collection and transmission policy to a proposed SHM application in which a standard steel pin-and-hanger assembly is monitored to track the safety of the component with respect to its net-section yielding limit state. The proposed monitoring system utilizes sensing nodes that operate using harvested solar energy and are subject to stringent energy constraints due to the size of the solar panels and availability of incoming energy, geographic location, and battery size. Numerical results are presented and illustrate the gains achieved by implementing the optimal policy, meaning the optimal policy minimizes the variance of the estimated measured process parameter, and in turn, minimizes the variance of the measured reliability index.

Previously, a sensor was proposed for detection of internal corrosion in pipelines made of ferromagnetic materials. The original sensor prototype was composed of a beam made of non-magnetic material, a strong permanent magnet, and the Fiber Bragg Grating strain sensor. The operating principle of the sensor is based on the fact that the magnetic attraction force between the magnet and the pipe wall decreases as the thickness of the pipe wall decreases due to internal corrosion. The FBG sensor is attached to the beam and allows to monitor the strain generated by the magnetostatic force acting on the magnet. Hence, the change in the force can be related to the change in the thickness of the pipe wall and detected using an optical strain gauge. In this work we present results of numerical investigations for two different alternative sensor configurations that are aimed at improving the detection range of the sensor. In the first configuration, the magnet is encased in an enclosure made of ferromagnetic material. In the second configuration we consider a more complex magnetic circuit that includes a magnetic counterbalance placed on the opposite side of the first magnet. We compare the absolute and relative changes in the magnetostatic force in both configurations to the baseline case of the original prototype and formulate suggestions for the improved design.

The effect of low energy impacts can seriously impair the operational life span of composites in the field. These low-energy impacts can induce a permanent loss in the toughness of the composite without any visible indication of the material’s compromise. The detection of this damage utilizing nondestructive inspection requires dense measurements over much of the surface and has been traditionally achieved by removing the part from service for advanced imaging techniques. While these methods can accurately diagnose the damage inflicted internally by the impacts, they accrue non-trivial opportunity costs while the structure is inspected. To enable the capabilities of in-service monitoring of the composite, the novel soft elastomeric capacitor was investigated as a sensing solution. The sensor is made of three layers comprised of a styrene-ethylene-butylene-styrene (SEBS) matrix, a commercially available elastomer. These layers consist of a titania filled center layer that forms the dielectric of the capacitor and two highly conductive outer layers doped with carbon black. This simple formation allows for a capacitor that has extremely robust mechanical properties. The soft elastomeric capacitor functions by taking up deformations on the surface of the composite that is transduced into a measurable change in capacitance. This study provides an electro-mechanical model for impact damage and experimentally investigates the efficacy of these sensors for use in damage detection given their promising characteristics; that being that the sensor geometry can be arbitrarily large allowing for much fewer sensors than traditional sensor networks employed for this task at a much lower cost than installing traditional in-situ sensing solutions. To investigate these properties a set of impact trials were undertaken on a drop tower using small samples of glass fiber reinforced plate, of random orient and short fiber, with a soft elastomeric capacitor mounted directly opposite the impact site. The impactor head was only allowed one contact with the sample before being intercepted. The testing range for the samples ranged from well below the yield strength of the glass fiber reinforced plate to the ultimate strength of the plate. Experimental results reported a square root relation between the impact energy given to the plate when inducing plastic deformations and the sensor’s measured change in capacitance.

Automatic fatigue crack detection using commercial sensing technologies is difficult due to the highly localized nature of crack monitoring sensors and the randomness of crack initiation and propagation. The authors have previously proposed and demonstrated a novel sensing skin capable of fatigue crack detection, localization, and quantification. The technology is based on soft elastomeric capacitors (SECs) that constitute thin-film flexible strain sensors transducing strain into a measurable change in capacitance. Deployed in an array configuration, the SECs mimic biological skin, where local damage can be diagnosed over large surfaces. Recently, the authors have proposed a significantly improved version of the SEC, whereby the top surface of the sensor is corrugated in diverse non-auxetic and auxetic patterns. Laboratory investigations of non-auxetic patterns have shown that the use of corrugation can increase the sensor’s gauge factor, linearity, and signal stability when compared to untextured sensors, while numerical analyses of auxetic patterns have shown their superiority over non-auxetic corrugations. In this paper, we experimentally study the use of corrugated SECs, in particular with grid, diagrid, reinforced diagrid, and re-entrant hexagonal honeycomb-type (auxetic) patterns as a significant improvement to the untextured SEC in monitoring fatigue cracks in steel specimens. Results show that the use of corrugation significantly improves sensing performance, with both the reinforced diagrid and auxetic patterns yielding best results in terms of signal linearity, sensitivity, and resolution, with the reinforced diagrid having the added advantage of a symmetric pattern that could facilitate field deployments.

In this paper we present the design of a layered technical textile blocks for measuring temperature and strain using FBG gratings. In addition to research into individual layers of technical textiles, we also deal with joining methods to ensure sufficient strength while maintaining the flexibility of the created textile block. The textile with dimensions of 1x1 meter is designed for use in critical infrastructure security, such as a protection of secured area. A unique, robust connector using GRIN lenses was designed to connect the individual blocks. This special connector has dimensions 30x10x4.5mm and insertion loss of about 5dB.

The development of electronic components is rapidly progressing due to the recent growth of electronic technology. As a representative example, wearable electronic devices are being widely studied as next-generation electronic devices. Next-generation wearable electronic devices require the development of flexible electronic devices and sophisticated components. Besides, it requires high adhesion between the flexible substrate and electrical components such as multilayer flexible printed circuit boards. Various adhesion processes have been studied to improve adhesion. As a method for increasing the adhesion, the most important factor is to improve the adhesion of the adhesive, but there is additionally a method of improving the adhesion by changing the surface area of the medium. Plasma treatment in electronics, developed for application to multi-layer flexible printed circuit boards for next-generation flexible mobile electronics, can assist to strongly combine different materials by changing the surface area to form a stronger bonding strength between the medium and the adhesive. We conducted on how the adhesion strength is improved depending on the presence or absence of plasma treatment on the medium. Also, as the heat conduction behavior was enhanced due to the miniaturization of electronic components, the heat resistance of the adhesive is also tested. We find that the adhesive strength decreases as the temperature increases, which is explained in this study.

This paper aims to study the working mechanism of pulsed laser generated ultrasonics on metallic surfaces as well as evaluate the pulse laser parameter effect on the generated waves. A thermoelastic model is adopted assuming that the pulsed laser works in the thermoelastic regime and illuminates on a half space metallic structure. Through Hankel-Laplace transform, the surface temperature and displacement are solved in the transformed frequency and wavenumber domain. The numerical solutions are then obtained through inverse Hankel-Laplace transform. Laser parameter effect on the epicenter displacement is studied, showing that the increase of excitation energy and decrease of laser beam radius both increase the epicenter displacement.

When using fiber Bragg grating (FBG) sensors in structural health monitoring (SHM) applications, one of the drawbacks is that the sensor location is fixed once it is installed and it is difficult to extend an already existing system. The use of an acoustic coupler to transfer fiber guided traveling waves from one fiber to another could resolve this issue as the system could be modified for extension. In this study, we investigate the coupling of optical fiber guided waves between two different types of fibers through an acoustic coupler. Specifically, input waves are launched into an input fiber and coupled to an output fiber through the acoustic coupler. The input waves are measured using a high-resolution 3D laser Doppler Vibrometer (LDV) and the output waves from the output fiber are measured using an FBG. We demonstrate that the wave coupling between two fibers varies with the cross-sectional area of the input fiber.

In structural health monitoring (SHM) applications, fiber Bragg grating (FBG) sensors are typically bonded directly to the surface of a structure to detect ultrasonic waves for damage identification. The sensitivity of the bonded FBGs to guided waves varies significantly with input ultrasound wavelength (λ)-to-FBG grating length (L) ratio, i.e., λ/L. Recently, the authors have demonstrated that the detection sensitivity of an FBG can be potentially increased by remotely bonding the optical fiber at a distance away from the FBG, however its response as a function of λ/L has not been studied in detail. In this work, we investigate the ultrasound detection of directly bonded FBG and remotely bonded FBG with varying λ/L. Specifically, we maintain L constant and change λ by varying the ultrasound excitation frequency. Using a 3D laser Doppler vibrometer (LDV) we first characterize input Lamb waves as a function of frequency, which are excited in a thin plate using a broadband transducer. Next, we measure the output response of directly bonded FBG and remotely bonded FBG to the same input Lamb waves. Finally, we examine the output FBG responses normalized with the LDV measurements of input waves, investigating the FBG sensitivity as a function of λ/L ratio. Understanding this sensitivity is important because many guided wave signals, for example generated by acoustic emission, are broadband. Additionally, multiple frequencies are often used for guided wave imaging of structures.

Chemical Warfare Agents (CWAs), powerful chemical weapons in warfare and armed conflict, were prohibited by the Organization for the Prohibition of Chemical Weapons (OPCW), but the danger persists because of its misuse in terrorism and warfare. It is crucial to detect them rapidly because exposure to small quantity of CWAs can cause lethal damage or death in a short time. Generally, they have been detected by various conventional techniques such as photoionization, ion mobility spectroscopy, flame photometry, and IR spectroscopy. But most of these methods possess a crucial challenge in being a portable detector because they are heavy, bulky, and consumes large power. In this study, the surface acoustic wave (SAW) sensor, quartz crystal microbalance (QCM) sensor, and quantum cascade lasers (QCL) were used to detect simulant of CWA. The SAW sensor and QCM sensor, a chemical detector based on piezoelectric substrates, detect chemical compounds using mass loading effect. It is caused by the adsorption of chemical compounds on the sensing material coated on the surface of the delay line. Therefore, the synthesis method, coating process, type, and concentration of sensing material have a prime effect on the sensitivity of the sensor. QCL was used to make a contactless small chemical detector with a mid-infrared photodetector, drive circuit, and optical hardware. Most CWAs have very high absorption rates and different absorption spectra in the range of 8-12 μm wavelengths. This detection method provides real-time detection of CWAs based on advantages of being contactless, excellent selectivity, and high sensitivity.

Piezoelectric wafer active transducers (PWaTs) play an important role for smart structures. Typically, they are bonded on the surface of structures to excite and sense mechanical vibrations or waves. The resonant characteristics of the surface bonded PWaT is very different from those of the free-standing PWaT. Since the resonances of the PWaTs strongly influence the transducer efficiency, it is important to understand their resonant behaviors. In this paper, an analytical model of the surface bonded PWaT is implemented to study the resonant characteristics of surface bonded PWaT in details. The study reveals that the PWaT supports two modes that have very different propagation constants below and above a cut-off frequency, leading to different resonance behaviors. These resonance behaviors can be explained with respect to the propagation of these two modes in the PWaT and the mode conversion at the edges of the PWaT. These fundamental understanding will benefit the design and optimization of the PWaTs.

Sensitization is a process that changes the microstructures of certain metal alloys, making them more susceptible to corrosion. In order to properly maintain and extend the life of structural components made of such materials, it is critical to develop in-situ SHM techniques to monitor the sensitization development on a continues base. This paper explores the influence of the sensitization on the resonant parameters of piezoelectric wafer active sensors (PWASs) bonded on samples underwent sensitization development. To differentiate the influence of sensitization and adhesive property changes, two different aluminum alloys, one sensitizes, and the other does not, were selected as the test and reference materials. The Nitric Acid Mass Loss Tests (NAMLT) of these materials confirmed that heat treatment (HT) at 80oC produced a gradual and approximately linear sensitization rate in the test material. On the other hand, the reference material showed negligible degree of sensitization (DoS). The specimens, with a PWAS bonded at the center, were heat treated and the EMI signatures of the PWASs were acquired at different HT durations. A digital signal processing algorithm was implemented to perform time-frequency analysis of the EMI signals, allowing the identification of the ultrasound propagation modes and the detailed analysis of the resonant phases. Different resonant parameters, such as the maximum amplitude, the rise time, and the frequency, were studied with respect to the HT durations. The experimental results demonstrated that changes of the resonant parameters, including the rise time and the maximum amplitude of the low and high frequency resonances can be correlated linearly to the DoS of the test material. In comparison, the changes of these parameters for the PWASs bonded on the reference material is much smaller. These results suggest that the resonance parameters of the surface bonded PWASs are sensitive to microstructural changes.

In our previous report, the active controlled microfluidic valve with annular boundary driven by the circular piezoelectric unimorph actuator was presented. In order to improve the performance of the active controlled microfluidic valve with annular boundary, the optimization of the active controlled microfluidic valve with annular boundary is presented. For simplify the optimal design, we mainly optimize the radius and thickness of piezoelectric ceramics based on the multiobjective parameter optimization theory according to the constraint conditions for the design variables. The research results show that: (1)The deflection of the circular piezoelectric unimorph actuator is nonlinear with the radius of piezoelectric ceramics. The deflection first increases and then decreases with the increase of radius; (2)The flowrate of the valve increases with the increase of piezoelectric ceramic radius. When the radius of piezoelectric ceramic is about 8 mm, the flowrate reaches the maximum value of 0.54 ml/s. When increases the radius of the piezoelectric ceramic, the flowrate of the active controlled microfluidic valve with annular boundary decreases; (3)The ACMV with annular boundary has a nonlinear relationship with the thickness of piezoelectric ceramic, which decreases with the thickness of piezoelectric ceramic. When increases the thickness of piezoelectric ceramic from 0.1 mm to 0.2 mm, the maximum flow difference is 0.49 ml/s. The optimization results provide a theoretical basis for the design of the microfluidic active control pump.

In recent years, the nonnegligible corrosion of steel strand for overhead ground wires has also been detected during operation. It is of great significance to efficiently monitor the corrosion of the steel strand in real-time. In this paper, a new kind of tensile corrosion sensor based on fiber Bragg grating sensing technique was proposed and developed, which can accurately monitor the corrosion of the steel strand in real-time. The performance of the corrosion sensor was studied by an electrochemical corrosion accelerated experiment. The experimental results show that the corrosion sensor can monitor slight and moderate corrosion of the steel strand accurately and effectively, therefore the corrosion sensor is practicable and has broad application prospects in transmission line engineering.

Operation maintenance work is an important part for the structural health assessment of transmission tower. The routine management and maintenance work mainly relies on engineers and technicians with practical experience to carry out visual inspection and fill in the questionnaire. However, human based visual inspection is an arduous and time-consuming task, and its detection results largely depend on the subjective judgment of human inspectors, as the same time the workers working at height are very dangerous. Aiming at the deficiency of artificial vision detection method, a detection method of transmission tower component recognition based on image recognition is proposed. UAV is used to detect the transmission tower in an all-round way, Thousands of images are used to train, verify and test the convolutional neural network (CNN) classifier based on Alexanet. Aiming at the problem of damage identification of transmission tower components, fast R-cnn based on improved ZF network is trained, verified and tested by using images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results show that the method can accurately identify the components and damage of transmission tower.

Constructions filled with carbon-based additives are intended to be the next-generation multifunctional materials with advanced mechanical capabilities and ideal smart strain sensing characteristics. Such additives are the graphene nanoplatelets that represent a new class of carbon nanoparticles/nanopowder and consist of small stacks of graphene sheets with an overall thickness of approximately 3-10 nanometers. Their unique size and platelet's morphology make these particles effective at providing barrier characteristics to the supreme applications that are used. The present study has as aim to report on the effect of graphene nanoplatelets presence on mechanical and electrical behavior as well as on the fracture mode of graphene nano-modified cementitious mortars. Pure bending, compression, and fracture tests with the simultaneous acoustic emission monitoring were carried out on specimens fabricated by the introduction of 0 to 1.2 wt. % pure few-layer graphene nanoplatelets in the different mixtures. A suspension for every graphene loading was produced under the ultrasonication procedure. A water-based superplasticizer was selected as dispersion agent based on its efficiency in inhibiting air entrapment inside the specimens and on its chemical stability. As concern the graphene-enhanced mortars the great improvements in mechanical characteristics and also the notable differences in fracture energy of specimens were documented at specific graphene loadings; the improvement was assessed simultaneously by acoustic emission data. In addition, the electrical response of the graphene-modified cement mortars, via the electrical conductivity measurements, is another property that was studied, and the total results are presented and discussed in the present paper.

It is well known that the behavior and properties of construction materials largely rely on the characteristics of their internal microstructure. It is important the curing process in freshly poured cementitious materials to be understood to successfully carry out every stage of construction development. Shortly after the mixing procedure, at the state when the suspension transmutes from the liquid to the solid-state phase, the ultrasonic wave propagation and the low pulse velocity of cement-based materials exhibit simultaneously a significant decrease. This is followed by an increase in both the ultrasonic pulse velocity and the signal amplitude. The point of solidification is responsible for the load-bearing capacity of the cement composite and its long-term behavior. At the point of phase change which occurs during curing, the nonlinear behavior of the material exhibits a notable sensitivity. This work aims at the comparison between nano-enhanced and plain cement-based composites regarding their hydration process. Multi-walled carbon nanotubes (MWCNTs) have been used as nano-enhancement in the cement paste specimens. The MWCNTs were synthesized via catalytic chemical vapor deposition, while a water-based superplasticizer was selected as the dispersion agent. The early stages of freshly poured materials were monitored using nonlinear elastic waves. A contact ultrasonic transducer and a noncontact optical detection device (Laser Doppler Vibrometer) were used for the experimental measurements. This method assesses the amplitudes of harmonic vibrations of an elastic wave with a specific fundamental frequency, propagating through the material, leading to the evaluation of its internal structure.

Nowadays, the use of adhesives in building materials for enhancing the mechanical properties and the final performance at the maximum level is standard practice. Moreover, the interest of the construction industry for extensively testing the effectiveness of the new modified products is increased. This study aims to examine the fracture behavior of mortar specimens modified with waterproofing adhesives using acoustic emission (AE). For the mortar beams' production, the portion of active mix water has been changed with the use of different kinds of emulsion resins for investigating the waterproofing mechanism at final usage. For this, the slurries that have been examined are commonly used at the construction of swimming pools. The specimens were tested in three-point bending and compression. The ultrasonic velocity of the samples was also determined. The results indicate that the use of adhesives in mortars can be successfully characterized by AE and ultrasonic parameters, making elastic wave nondestructive evaluation a valuable tool in the growing sector of building materials using adhesives.

In this study, fracture experiments on fiber reinforced concrete beams are conducted. The aim is to examine the level of restoration in the different types of fiber reinforced concrete specimens by means of acoustic emission (AE) technique. The concrete specimens have been reinforced with three different types of metal fibers by means of shape and geometry and were tested in four-point bending. Consequently, they were repaired by means of suitable epoxy agent and mechanically loaded again. The repair has been conducted with epoxy resin injection to the main macrocrack that has been developed during the four-point bending. This work discusses the passive monitoring of fracture in repaired with epoxy resin fiber reinforced concrete specimens and shows that AE parameters provide good insight of the microstructure and characterize the level of restoration which is important especially when other NDE techniques cannot be used because of construction limitations.

The long-term adverse effect caused by environmental factors is one of the main causes for performance degradation of bridges. Accurately obtaining the environmental meteorological data on the bridge site can provide basis information for analyzing and assessing the negative effects of environments on bridges. To improve the efficiency and reduce the cost of meteorological data collection, this paper proposed a method to predict the meteorological data of bridge site based on the available shared date of weather station near the concerned bridge. Focusing on bridge temperature effects in this study, two major meteorological parameters, air temperature and wind speed, are investigated for data predication. Meanwhile, different geomorphologic conditions at different regionsin China are discussed. Four interpolation methods, inverse distance weighting, Kriging interpolation, radial basis function, and minimum curvature method, are investigated. Cross-validation has been used to evaluate the performance of these four methods. The results show that Kriging method has the best predication on air temperature, and the inverse distance weighted method and Kriging method are both good for wind speed, however, Kriging shows better performance as a whole. Spatial interpolation method is feasible to predicate the meteorological parameters of bridge site, which provides a higher efficiency and less cost approach to obtaining the on-site meteorological data for the bridge temperature effects analysis and assessment.

An aluminum embedded fiber Bragg grating (FBG) sensor configuration for a decoupled temperature and strain measurement was proposed and demonstrated. This configuration consists of a three-point regenerated FBG (RFBG) sensor array with a distance of 8 mm in one fiber. One RFBG was in direct contact with the aluminum, whereas the other RFBGs were protected from the aluminum by a steel capillary. The RFBG in contact with the aluminum was influenced by temperature and strain, and the other RFBGs only by temperature. The casting mold used here had a second symmetric arm used for an additional temperature reference measurement with three RFBGs in one capillary. Two casting experiments with this configuration were made, and decoupled temperature and strain measurements based on one sensing fiber were demonstrated. The temperature values in the strain sensing fiber showed good agreement with the reference temperatures in the second casting arm. Thereby, it could be demonstrated that one fiber was sufficient for both temperature and strain measurement and that the temperature compensation for the strain sensor was successful. This is a significant step towards the application of smart casts.