This PDF file contains the front matter associated with SPIE Proceedings Volume 12046, including the Title Page, Copyright information, Table of Contents, and Conference Committee listings.

Active structural control of civil infrastructure in response to large external loads, such as earthquake or wind, requires the rapid integration of information between sensing nodes, computational nodes, and actuating nodes. Because of this, it is still not widely employed due to several key issues, such as latency in the system and challenges with information exchange. In this study, the Martlet, a high-speed data acquisition and computing node that was designed based on a Texas instruments piccolo microcontroller and capable of peer-to-peer wireless communication, is used for all three steps in the active control process. For rapid sensing, the Martlet is equipped with an interface board that interfaces with a displacement transducer and has an on-board differentiating circuit to derive velocity. The sensing Martlet transmits its data (i.e., displacement and velocity) to the actuating Martlet. The actuating Martlet calculates the necessary control force using an optimal control law, the full-state linear quadratic regulator. The resulting control force is then conveyed to the actuator via a controller interface board. This complete process is experimentally validated on a partial-scale, four-story shear structure and it is demonstrated that due to the fast processing speeds of the Martlet, real-time control of the structure can be achieved.

Wireless sensor offers impressive functionality compared with traditional wired sensor, including straightforward installation, lightweight, low-cost circuits, high-performance microcontroller and sensors, easy maintenance, and flexibility and scalability for projects. While, there are various challenges to be considered in wireless sensors such as energy efficiency, communication rate, reliability, and data loss. And these drawbacks constrain its applications on specific projects, e.g., long-term monitoring for infrastructures. To overcome the aforementioned drawbacks, this study aims to explore a comprehensive solution from two sides: both the hardware achievement and the communication algorithm. Ultralow power Urbano wireless sensing node with a high-performance computational microcontroller is proposed as the backbone of the wireless sensing system. Satellite communication is employed to ensure the proposed wireless sensing node is completely autonomous to suit a wider range of fields without the requirements of additional base stations (that host single-board computers). In addition, this paper advances compressive sampling (CS) framework as an alternative to traditional Nyquist/Shannon sampling for SHM. An embedded CS-based algorithm is developed to compress the acquired time-history signal, save the storage space of Urbano, reduce data rate requirements, and ensure the accuracy of data via ultra-low rate communication.

Steel bridges are susceptible to fatigue damage under traffic loading, and many bridges operate with existing cracks. The discovery and long-term monitoring of those fatigue cracks are critical for safety evaluations. In previous studies, the ability of the soft elastomeric capacitor (SEC) sensor that measures large-area strain was validated for detecting and monitoring fatigue crack growth in a laboratory environment. In this study, the performance of the technology is evaluated for field applications, for which an approach for long-term monitoring of fatigue cracks is developed. The approach consists of an integrated system, termed the wireless large-area strain sensors (WLASS), for wireless data collection and storage and a signal processing algorithm for monitoring fatigue cracks with bridge response induced by traffic loading. In particular, the WLASS consists of soft elastomeric capacitors (SECs) combined with sensor boards to convert capacitance to a measurable change in voltage and a wireless sensing platform equipped with event-triggered sensing, wireless data collection, cloud storage, and remote data retrieval. A modified crack growth index (CGI) is developed through detection of peak-to-peak amplitudes of the wavelet transform. Using the measurements from the WLASS, the modified CGI is able to obtain the crack status under various loading events due to random traffic loads. The performance of the developed approach is validated using a steel highway bridge.

This paper presents an implementation of the Martlet wireless sensing system for performing bridge weigh-in-motion and structural health monitoring at an in-service highway bridge. In this research, a field study is carried out on a steel I-girder bridge which carries Interstate 85 near LaGrange, GA. The bridge is instrumented with accelerometers, strain gages, thermistors, and LIDAR sensors interfaced with Martlet wireless sensing units. Multiple controlled bridge weigh-in-motion tests are performed by driving a loaded dump truck across the bridge. The installed system remains operational for long-term monitoring and near real-time bridge weigh-in-motion.

Structural damage identification using the impedance/admittance measurements of a piezoelectric transducer can be converted into a multi-objective optimization framework targeting the minimization of the discrepancy between prediction and experimental measurements, with damage locations and severities as unknown variables. However, the unknowns are usually on a large scale and show sparse characteristics since the damage only occurs at a small area. This places the burden on the optimization algorithms in the identification process. Here, a sparse initialization algorithm is introduced to generate a sparse population for the large-scale variables to tackle the challenge. The algorithm is combined with the particle swarm algorithm to locate and quantify the damage for verification purposes. Several cases are considered, and the results show that the algorithm can generate high-quality damage identification solutions with limited simulation and experimental measurements.

Demand for autonomous flying vehicles intended for transportation of people and goods is expected to accelerate in the next few years as urban air mobility maturity reaches level-4, a rating implying that hundreds of flights will simultaneously take off from urban aerodromes around the country. Wind sensors available on the vehicles and located in the air space will become a key necessity for ensuring safe navigation. Conventional anemometers suffer from various drawbacks due to their non-aerodynamic construction, high power consumption, complex signal processing, and cost. An airfoil-shaped low drag anemometer is presented for wind speed and direction measurement on tethered systems such as kites, balloons, and drones. The airfoil anemometer is equipped with a flexible, dual-layer capacitive pressure sensor with a polyvinylidene fluoride (PVDF) diaphragm for wind speed and a commercial digital magnetometer for wind direction measurement. The fabrication process for the diaphragm-type capacitive sensor is presented, along with characterization of the sensor in a pressure chamber. The completed sensor is then integrated into a NACA-2412 profile airfoil, along with a commercial magnetometer, for demonstration in a laboratory-scale wind tunnel.

Surface acoustic wave (SAW) devices consisting of interdigitated transducers printed on piezoelectric substrates have resulted in low-cost, low-power, and small-footprint thermometers for high temperature and radioactive environments. This study developed temperature-dependent finite element models in both time- and frequencydomain. Modeling accuracy was evaluated using an aerosol-jet printed SAW thermometer measured from room temperature to 200 Celsius. Time-domain simulation results enabled acoustic wave propagation visualization and successfully guided the signal denoising of measured scattering parameters. Frequency-domain simulation accurately predicted the temperature-driven natural frequency drift in SAW transducers while maintaining high computational efficiency. The models developed in this study will facilitate computer-aided design of future SAW transducers and expand their applications in harsh environments.

Nanofiller-modified composites have received immense attention for potential applications as sensor technology in civil, mechanical, and aerospace systems. Sensing in these materials is predicated on the piezoresistive effect–the material having deformation-dependent electrical conductivity. To date, work in this area has focused overwhelmingly on the relationship between direct current (DC) electrical transport and deformation. This is important because utilizing changes in alternating current (AC) transport as a metric of deformation has notable potential advantages such as enhanced data density (i.e. by relating both impedance and phase to deformation), higher sensitivity via electrodynamic principles, and reduced power requirements. Existent studies on AC piezoresistivity have focused mainly on macroscale equivalent circuit modeling that homogenizes the net input-output electrical response of the material corresponding to deformations. Therefore, in order to generate new basic knowledge in this field, this work presents a preliminary study into the effect of inter-filler transport on deformation-dependent impedance in piezoresistive nanocomposites. This is done by discretizing the nanofillers into a complex network of AC circuit elements, calculating the net frequency response of the network, modulating the tunneling resistance and inter-filler capacitance to replicate the effect of deformation, and then recalculating the net frequency response. The model was compared to experimental data for 1.0 wt.% carbon nanofiber (CNF)-modified epoxy subject to uniaxial stress. Results of this preliminary investigation show that both inter-filler tunneling resistance and capacitance play a strong role in the AC piezoresistive response of nanocomposites and modulation of these inter-filler transport parameters can qualitatively replicate experimental observations with good accuracy.

Electrical impedance tomography (EIT) is the process of spatially mapping the conductivity distribution of a domain based on voltage-current relationships observed at the boundary of the domain. Due to its low cost, ease of implementation, and benign nature (i.e. non-ionizing), EIT has received much attention for potential application to damage, strain, and saturation mapping in civil, mechanical, and aerospace venues that employ conductive materials (e.g. nanofiller-modified cements and polymers, carbon fiber composites, etc.). Mathematically, however, EIT is an ill-posed inverse problem predicated on reconstructing the conductivity distribution on a model of the domain. This process is prone to nonconvergence due to factors such as experiment-to-model domain shape discrepancies and electrode misplacement. Time difference imaging is commonly used to overcome this challenge–boundary measurements are collected when the material is in a pristine state and another set of boundary measurements are collected after some conductivity-changing perturbation such that experiment-to-model discrepancies largely subtract out. But this process of time-difference imaging is problematic for several reasons: one, a pristine state is not available for existent or already in-service structures. Two, pristine-state data can be lost over time. And three, the pristine-state data may no longer be applicable if there are significant shape changes, electrodes are reapplied, etc. Thus, it is desirable to have a baseline-free technique for EIT. To that end, we herein present the results of a preliminary investigation on damage detection via frequency-difference imaging (fdEIT). In this approach, we leverage the fact that materials generally exhibit frequency-dependent conductivity such that an EIT image can be formed from the difference between two different frequencies. Experimental measurements of AC conductivity up to 10 MHz were collected from a carbon nanofiber (CNF)-modified glass fiber/epoxy laminate. These measurements were then used in a computational simulation to explore the potential of fdEIT for damage detection and localization. The results of this study suggest that fdEIT may indeed be a viable method of eliminating baseline measurements for EIT thereby providing an important advance towards making EIT a practical inspection modality.

Nanofiller-modified composites have shown much potential for structural health monitoring (SHM) and nondestructive evaluation (NDE) because they exhibit self-sensing behavior via the piezoresistive-effect. To date, the piezoresistive effect has been used predominantly in isolation for cases of (quasi-)static loading. There is a comparative lack of work that investigates the piezoresistive-effect under mechanically dynamic conditions. This is important because combined piezoresistive-elastodynamic approaches could leverage the relationship between electrode geometry/topology and piezoresistive information carried in elastic waves. In other words, the question of how certain factors of electrode design, such as spacing between electrode pairs, impacts the measured piezoresistive signal has been little explored within the context of elastodynamics. Addressing this gap in the state of the art may yield insights into multiphysics approaches to SHM and NDE which seamlessly marry conductivity and vibration-based techniques. To this end, a simple prismatic carbon nanofiber (CNF)-modified epoxy rod was manufactured. Piezoresistive measurements were taken as a function of time by way of normalized resistance measurements between surface mounted electrodes. An electromagnetic shaker was employed to inject highly controlled one-dimensional stress waves into the CNF-modified epoxy rod. It was found that surface mounted piezoresistive measurements were able to accurately reconstruct the profile of propagating subsurface wave packets across the length of the rod. Transmission and propagation of the wave packets were extrinsically validated with the shaker force sensor and an external laser vibrometer (LV) system, respectively. Furthermore, artificial signal filtering was achieved by changing the distance between the electrode pairs. Lastly, the dispersion curves were constructed from piezoresistive measurements and extrinsically validated. Results from this preliminary investigation seeks to lay the foundational work for a new multiphysics SHM tool, piezoresistivity-coupled elastodynamics, that aims to provide an unparalleled level of understanding into material dynamic properties from direct electrical interrogation.

In this study, we developed a lane detection and motion-blur compensation system for tunnel inspection. It was undertaken considering the need for a highly safe and inexpensive inspection method for highway structures to replace the conventional visual and hummer sounding inspection. To further improve the positional accuracy of deterioration detection, we 1) proposed an accurate and convenient printing method that is based on silkscreen printing using retroreflective paint and 2) developed a method for creating barcode markers that can be printed on the surface of concrete.

Automating visual inspection of vials containing freeze-dried products is a difficult problem due to the complex appearance of freeze-dried materials. To existing inspection equipment, defects and typical product appearance variation often appear very similar. We contend that these shortcomings necessitate a new approach and propose a multimodal sensor integration framework. We study polarimetric imaging combined with edge detection to improve the detection of defects such as scratches in glass vials. Stokes vectors enable the extraction and display of different polarization characteristics. Edge detection compares the effectiveness of RGB and polarimetric imaging for defect detection. A novel mathematical description of the outline strength decreases edge detection complexity. Our work shows that polarimetry, in combination with edge detection, enhances the detection of defects. We find that a combination of polarization characteristics in the hue-saturation-value (HSV) false coloring scheme outperforms all individual polarization characteristics and RGB imaging, achieving a 100% detection accuracy at an outline strength of 0.7. Image resolution impacts the effectiveness of edge detection as well. A resolution of 1500 x 1000 pixel works best for defect detection in our dataset. Defect detection is most sensitive to scratch depth, while scratch length and thickness are less important. Our findings not only apply to defect detection on glass vaccine vials, but also to the inspection of other reflective surfaces such as plastics and metals. This work can be expanded to problems that require material detection. Denise Tellbach is a graduate student in Mechanical Engineering at MIT. Her research interests are in IoT, sensing, and applying machine learning to enhance the functionality of industrial quality control. Denise received a double master’s degree in Management Science and Mechanical Engineering from RWTH Aachen University and Tsinghua University in 2018 and her undergraduate degree from RWTH Aachen University in 2016. In the past, she has developed a maturity model for the digitalization of production control, she has worked on cyber-physical systems modeling and on reliability assessment focusing on the electric grid. She joined the AutoID Lab as a graduate student under Prof. Sarma in 2019 and is a Presidential Fellow at MIT (2019). Praveer Sharan is a researcher affiliated with the Auto ID Lab at the Massachusetts Institute of Technology. His research interests are in data analysis, unmet needs in healthcare, edge detection, and machine learning to boost efficiency of healthcare. Praveer is scheduled to graduate in 2022 and is applying for computer science programs. In the past, he has conducted a review on the ability of neural networks to automatically discriminate between healthy versus unhealthy coughs based on the processed audio of the cough. He has worked on research projects and conducted research with the MIT computer science department for the last two summers.

Naval structures are subjected to damage that occurs on short-term (i.e. impact) and long-term (i.e. fatigue) time scales. Digital twins of ship structures can provide real-time condition assessments and be leveraged by a decision-making framework to enable informed response management that will increase ship survivability during engagements. A key challenge in the development of digital twins is the development of methodologies that can distinguish the various fault cases. Moreover, these methodologies must be able to operate on the resource-constrained computing environments of naval structures while meeting real-time latency constraints. This work reports on recent progress in the development of a multi-event model updating framework specially designed to meet stringent latency constraints while operating on a system with constrained computing resources. The proposed methodology tracks structural damage for both impact and fatigue damage through a swarm of particles that represent numerical models with varying input parameters with set latency and computational restraints. In this work, numerical validation is performed on a structural testbed subjected to representative wave loadings. Results demonstrate that continuous fatigue crack growth and plastic deformation caused by impact can be reliably distinguished. The effects of latency and resource constraints on the accuracy of the proposed system are quantified and discussed in this work.

Impacts in fiber reinforced polymer matrix composites can severely inhibit their functionality and lead to failure of the composite prematurely. This research focuses on determining the efficacy of a novel capacitive sensor, termed as the soft elastomeric capacitor or SEC, for the purpose of monitoring the magnitude of out-of-plane deformations in composites. This work aims to forward the development of a sensing skin that can be used as an in situ monitoring tool for composites. The capacitive sensor can be made to arbitrary sizes and geometries. The sensor is composed of an elastomer composite that inherits the strains of the material it is bonded to. The structure of the sensor, manufactured to function as a parallel plate capacitor, responds to impacts by transducing strains into a measurable change in capacitance. In this work, the large area capacitive sensors are deployed on randomly oriented fiberglass-reinforced plate with a polyester resin matrix. The material is impacted at various energy levels until the material reached its yielding point. The behavior of the sensor in impacts below the proof resilience shows little to no change in capacitance of the sensor. As the impacts surpassed this yielding point, the sensor responds linearly with induced change in area. The results performed within expectations of the proposed model and demonstrated the efficacy of the proposed large area sensor as a damage quantification tool in the structural health monitoring of composites.

This work presents a control scheme for wind-induced vibration mitigation for tall buildings based on a gated recurrent unit (GRU) encoder-decoder model which operates using readings from multiple sensors to define a unique system state. The sensors include a distributed network of pressure probes installed on surrounding buildings, accelerometers installed on the principal building, and atmospheric conditions. The encoder-decoder GRU is trained from timeseries sensor readings to construct a unique internal representation (hidden state) of the evolving wind and building conditions. A 1:400-scale aeroelastic building model with motorized plates acting as aerodynamic control surfaces is used in wind tunnel experiments to conduct this study. An online genetic reinforcement learning (GRL) algorithm uses a series of multilayer perceptron (MLP) networks to determine optimum actuator orientations for different flow conditions. The algorithm stores previously discovered solutions in the MLPs sorted by their fitness. The GA operates by obtaining a solution from each of the MLPs and performing GA operations on them to choose the next combination of plate angles to try. A chance also exists for trying completely random plate angles to prevent the GA from stalling. The MLPs are continuously trained during online optimization using findings obtained from new trials. The system eliminates the need for holding wind conditions, which are uncontrollable, constant during online training but still uses a pseudo-random search technique to obtain global optimum solutions. Results show a considerable reduction in building RMS acceleration when compared with a large collection of results with random constant plate angle orientations.

This paper presents a feasibility study for the visualization of hidden damage using an integrated highspeed stereo-camera system. The reconstructed wavefields produced by the 3D-enabled stereo-camera system were compared to those from a single-camera system, which is limited to 2D sampling. To demonstrate this stereo-camera concept, guided waves were generated in a thin aluminum plate with surrogate damage using a surface-mounted piezoelectric actuator under continuous sinusoidal excitation. The resulting wavefield was captured with two calibrated high-speed cameras and reconstructed through stereo-digital image correlation. After signal or image filtering and processing, the reconstructed wavefield was produced. To overcome the limited data rate of state-of-the-art high-speed cameras, sample interleaving was implemented to effectively increase the sampling rate. For the single-camera 2D-DIC case, image stitching was needed to increase the field of view, which left artifacts in the damage image due to slight misalignments between samples. The implementation of two high-speed cameras for stereo-digital image correlation enables the full-field sampling of both in-plane and out-of-plane components of the wavefield displacement. Because the out-of-plane displacement is typically much larger than other wave modes due to the thin plate dimensions, this advantage of capturing out-of-plane vibrations unique to the stereo-camera setup lowered the required resolution and enabled the capture of a larger field-of-view without the need for image stitching.

Fatigue cracks can develop in mechanical, aerospace, and civil engineering structures over time due to repetitive loads. Growing fatigue cracks could reduce the lifespan of the structure and lead to catastrophic collapse. Distortion-induced fatigue cracks are specifically concerning in steel bridges. Computer vision-based crack detection have shown great potential in crack detection for being robust and easy-to-deploy. A vision-based feature point tracking method measures the changes in surface motion to detect fatigue crack and performs well in the presence of other crack like features like corrosion marks, boundaries, etc. When the video is recorded using a moving camera like a handheld camera, unmanned aerial vehicle, and mixed reality headset worn by an inspector, feature point movement contains camera motion as well as the true object motion. To accurately detect cracks, feature point displacement needs to be free from camera motion. Distortion induced fatigue cracks occur in regions with complex geometries like web-gap regions in girder bridges. Due to parallax effects, a single geometric transformation is not enough to compensate the camera motion accurately in videos with such complex geometry. The bundled camera paths approach divides a video into multiple mesh grid cells and estimates motion in each cell individually. These camera paths are then optimized to remove camera jitters and rolling shutter effects producing stable video. However, the global camera motion is still present in the smoothed video. We have extended the bundled camera paths method to remove the global motion from the smoothed video. The proposed approach was successfully tested in a laboratory experiment to compensate camera motion and detect distortion induced fatigue cracks.

SHM and NDE/NDT are developed to evaluate the amount of damage to a structure. Since human inspections are prone to misdiagnosis and/or miss-interpretations, the damage assessment and identification methods are shifting from human-based to computer-based. In this article, a computer vision method is used to detect and quantify the cracks on a concrete surface. After processing the crack images, cracks are modeled as graphs for feature extraction. To study the proposed method, concrete surface crack images from a reinforced concrete shell under quasi-linear load at each load step are used. Pearson correlation analysis is implemented to evaluate the relationship between graph measures at the north and south faces of the wall. All correlation values are above 50%, and eight out of ten graph measures demonstrated a correlation above 85%. The average correlation value between both sides of the wall reports 84% compatibility. High correlation values between the two sides of the wall attribute to the fidelity of graph features to the crack patterns on the two sides of the wall. Moreover, the monotonic increases in different graph measures and damage indexes indicate that the extracted features from the converted crack patterns to graph are meaningful information. Results of this study reveal that the proposed crack quantification method has the potential of translating crack patterns for further machine learning applications. This novel idea of using different graph measures introduces more features and can act as a base to study the fundamental and mathematical relation between crack patterns and graph theory.

A classification strategy with a deep learning model to categorize stiffness reduction in cross-ply laminates from transverse cracks is presented. Deep learning models are very successful in image-based classification which can be conveniently used on structural health monitoring data. In the current study, the transverse cracks in 90-degree pliés are modeled with a finite element model in Abaqus; and are solved for fundamental Lamb wave modes (symmetric S0 and antisymmetric A0) independently. The data from the simulation models are grouped into three different classes of stiffness ranges depending on the associated crack density in the 90-degree pliés. The time-domain data for each case is then post-processed with continuous wavelet transforms, and Hilbert transforms to obtain a featured information representation with visual images. The digital image from the database is represented as a matrix encoded in red-green-blue channels (RGB matrix) and is used as an input to train a convolution neural network (CNN) model to perform a categorical classification. The results from the test and validation data obtained for identical configuration and excitation frequency show that the CNN model can classify an image into respective stiffness categories accurately. The proposed model is applied successfully on cross-ply laminates of different configurations delivering a satisfactory performance with excellent validation and test accuracy, demonstrating the potential for developing a deep learning-based tool to classify and quantify damage in composite structures.

Vibration measurement serves as the basis for structural damage detection. To detect damage, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In this work, we demonstrate that structural damage prediction can be achieved using a deep learning neural network architecture. In this paper, we seek to learn and see the structural damage directly from videos using deep convolutional neural networks (CNN). The key idea is to use each pixel of an image taken from a digital camera, extracting the spatiotemporal information, like a sensor to capture the modal frequencies of a vibrating structure. We develop attention-based architecture to detect subtle signals from a specific source to visualize high resolution of dynamic properties of the structures to infer existing structural damage. We first extract the high discriminative features of video frames using the CNN. Then we leverage conv-long short-term memory (ConvLSTM) with the extracted features as inputs to capture the temporal dynamics in videos. The attention mechanisms are embedded in the network to ensure the model learns to focus selectively on the dynamic frames across the video clips. Our computer vision-based deep learning model takes the video of a vibrating structure as input and outputs about the health of the structure. We demonstrate, using reliable empirical results, the proposed model is efficient, autonomous, and accurate. The proposed method is verified using a few laboratory experiments. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even.

Health assessment of structures is an essential and innovative method to evaluate the behavior of the structure and to estimate the performance during the service period. Health assessment is important not only for structural safety and integrity but also for human safety requirements. A discontinuity in the form of damage changes the global structural stiffness, which in turn modifies the modal response of the structure. This assimilation can be achieved from changes in the structure's natural frequencies, one of the modal parameters that can change globally. In this present investigation, modeling of different cantilever beams having different levels of damage severities is performed. Finite Element (FE) simulations is performed using 3D modal analysis methodology for different material properties. For this, eight noded brick elements is used with fine mesh size. A dataset is created in the form of an array with natural frequencies for different cases. In recent years, neural networks have shown promising results in applications like image and text classification, machine translation, anomaly detection, and many more. In this work, modal analysis is used along with Artificial Neural Networks (ANN) for damage severity assessment. We have utilized ANN for classifying the modal signatures into four different damage severity classes. The dataset is used to train a classification-based neural network, categorizing natural frequencies (inputs) into damage severity classes (outputs). We have used a categorical cross-entropy loss function with the Adam optimization scheme. We have seen that the networks are able to train with low loss and higher accuracy. The performance of the network is measured using metrics like classification accuracy, F1-score, confusion matrix and Receiver Operating Characteristic (ROC) curves, training, and prediction time. New signatures are fed into the trained networks to identify the damage levels autonomously without waiting for a time-taking expert's analysis procedure. It is seen that the network can generalize well on unseen examples and can predict the health of the structure.

Designing structural health monitoring (SHM) is logically equivalent to designing a civil structure. The capacity must be greater than the demand to achieve the required performance. Monitoring capacity and demand are the counterparts of structural capacity and demand in the semi-probabilistic structural design. They are defined as the uncertainty of key-parameters that represent the structure behaviour and will be estimated through the monitoring system: the capacity is the uncertainty resulting from the estimation, the demand is the design target. As far as concrete and prestressed concrete bridges are concerned, important key-parameters are long-term temperature-compensated responses, such as strain trend, displacement trend, and rotation trend. Their estimation as well as the estimation of their uncertainty can be easily performed a posteriori through Bayesian inference, once monitoring data are available. However, in the design phase measurements are not yet available. We propose an approach for designing a structural health monitoring system accounting for temperature compensation, which allow to quantify the uncertainty of structural response trends a pre-posteriori, before monitoring data are available. We analyse the impact of sensors’ accuracy, monitoring duration, and seasonal temperature variation on the expected uncertainty. Finally, we test our framework on a real-life case study, the Colle Isarco viaduct, one of the longest prestressed concrete highway bridges in the European Alpine region.

Inter-story drift or roof drift plays an important role in the damage assessment of building structures. Hence, three performance levels regarding drift limits for reinforced concrete (RC) structures and steel structures are addressed in the seismic rehabilitation prestandard of the Federal Emergency Management Agency (FEMA 356). However, directly measuring the structural displacement is very difficult in most field applications and, alternatively, accelerometers are generally deployed to collect the structural responses instead of using displacement meters for building seismic monitoring. During the past decade, different techniques have been developed to provide displacement estimation using acceleration measurements but the conventional integration scheme relies on baseline correction methods to have a reasonable result and none of these techniques can properly estimate the residual displacement. In this study, the techniques of singular spectrum analysis (SSA) and the integration scheme are combined to estimate the displacement directly from the recorded acceleration. In the proposed method, SSA decomposes the acceleration measurement into approximation and detailed parts; the approximation parts are used as weighting factors, and the detailed parts are integrated twice to have the temporary displacements. Then, the displacement waveform can be correctly reproduced by fusing these two parts. Moreover, the proposed method is verified by using the roof responses of the RC specimen recorded during the shaking table test. The displacement waveform is correctly estimated as well as the induced permanent deformation during earthquake excitation. Last of all, a conclusion is briefly drawn and the applicability of the proposed method to building seismic monitoring is described.

The safety of the utility of all structural elements is determined using testing procedures simulating chosen situations from the real world. The goal of the paper is to present experimental investigations focused on fire resistance of structures with rechargeable energy storage subsystems and construction elements. The analyses will be focused on the influences of sensors placement over the structures and environmental factors on the achieved results. The aim of the analyses are better understanding the processes occurring in the structures during their exposition to an open flame and increasing the quality of the testing methodology to increase the safety level.

Structural health monitoring (SHM) of important structures such as bridges, skyscrapers, and tunnels are gaining attention due to the feasibility, availability, and prodigious development of data acquisition systems (DAQ) as well as various type of sensors. However, in order to get the most out of the sensory information/technologies an appropriate monitoring and management strategy is inevitable. Thus, this study focuses into identification & monitoring strategy by deploying a multi-sensor approach on a laboratory scale prototype bridge. The enforced sensors include directly mounted sensors like: (i) accelerometers, (ii) distributed strain sensing (DSS) systems, (iii) remote sensing techniques based on camera video streams, and (iv) laser distance meters. To attain the goals of this study, initially, the measured data of different sensors are evaluated and the results are prepared to interpret the performance of the sensors. Later, the crucial dynamical results in time and frequency domains (e.g. eigenfrequencies) of the tested bridge have been assessed for all sensors type. In a nutshell, the response observed via those sensors provides useful information which are essential for monitoring and assessments. Even though, any of those sensors are capable of measuring dynamic responses but accuracy and reliability might be a concern. Hence, a multi-sensor strategy would be beneficial for a flawless monitoring strategy to deal with various uncertainties (i.e., noise, resolution, measurement error) of the sensors. More specifically, monitoring with a multi-sensor strategy will be advantageous when accuracy is concerned (e.g. data-based model, feedback-loop) along with the complexities induced by extreme vibration.

This paper investigates the mechanism behind the wind-induced vibration of high mast illumination pole (HMIP) structures using wireless smart sensors. Several video recordings revealed significant vibrations of an HMIP under wind loading in Kansas, resulting in large cyclic displacements. In this study, to facilitate the estimate for the main cause of the HMIP’s vibration, finite element modeling and video analysis are employed to evaluate the fundamental natural frequencies and the recorded vibration frequencies of the HMIP of interest. Meanwhile, a 100-foot-tall, galvanized steel HMIP with three LED luminaires is selected for long-term vibration monitoring. A wireless smart sensor network is designed to monitor the structure's acceleration response, wind speed, and wind direction to further investigate the primary cause of the excessive wind-induced vibrations.

This paper discusses the established and potential applications of high-dimensional data analysis in the fields of structural health monitoring and non-destructive evaluation. Despite the significant potential of high dimensional data analytic methods, a few applications have been implemented in structural health monitoring and non-destructive evaluation. Further, as measuring technologies improve, the requirement of applying these approaches grows. This paper uses thermal videos as an example of high-dimensional data in the non-destructive evaluation field. These thermal videos are used to detect and localize delamination in composite plates, typically found in aircraft wings. Using traditional statistical approaches to analyze videos presents theoretical and practical challenges due to their high dimensionality. Tensor analysis methods help to overcome these issues. To locate the damage, two tensor factorization algorithms are used. For a rectangular damage zone, two vectors are enough to localize the extent of the damage. For more sophisticated cases like a damage in the shape of a circle, higher order of core tensors with larger projection tensors are needed. The results demonstrate these methods are accurate and efficient in terms of computing cost.

The rapid assessment of infrastructure following extreme weather or seismic events is important to ensure the stability of structures before their continued use. This work presents an amplitude compensation technique for accurate acceleration measurements formulated for unmanned aerial vehicle’s (UAV) deliverable sensor packages. These packages are designed for measuring the acceleration of structures, for instance, railroad bridges and power transmission towers. Current technology for structural health monitoring is expensive, stationary, and requires maintenance by certified personnel. These attributes prevent rapid assessment of remote and hard-to-reach structures. Low-cost, UAV-delivered sensor packages are an ideal solution due to their ability to be deployed on a large scale in a timely manner; cutting down on cost and the danger affiliated with structural health monitoring following extreme and hazardous events. One challenge to this approach is that the UAV deployable sensor package consists of several systems, including mounting hardware, embedded electronics, and energy storage that result in a loss of transmissibility between the structure and the package’s accelerometer. This work proposes a frequency response-based filter to isolate the structure’s vibration signature from interference caused by the sensor package itself. Utilizing an input-output relationship between the sensor package and a calibrated reference accelerometer, a model transfer function is constructed. Compensation is performed in the post-processing stage using the inverse transfer function model. This approach is shown to enhance the signal-to-noise ratio by 1.2 dB, an increase of 7.17%. This work investigates algorithm robustness and sensitivity to noise across the sensor package’s bandwidth of 6-20 Hz. A discussion on the limitations of the system is provided.

Real-time model updating for active structures experiencing high-rate dynamic events such as: hypersonic vehicles, active blast mitigation, and ballistic packages, require that continuous changes in the structure’s state be updated on a timescale of 1 ms or less. This requires the development of real-time model updating techniques capable of tracking the structure’s state. The local eigenvalue modification procedure (LEMP) is a structural dynamic modification procedure that converts the computationally intensive global eigenvalue problem, used in modal analysis, into a set of second-order equations that are more readily handled. Implementation of LEMP for tracking a structure’s state results in secular equations that must be solved to obtain the modified eigenvalues of the structure’s state. In this work, the roots of the secular equations are solved iteratively using a divide and conquer approach, leading to faster root convergence. The present study reports on developing a real-time computing module to perform LEMP in the context of real-time model updating with a stringent timing constraint of 1 ms or less. In this preliminary work, LEMP is applied to tracking the condition of a numerical cantilever beam structure, which depicts changes in a structure’s state as a change in the roller position. A discussion of variations in timing results and accuracy are discussed.

In our project, a novel gear health monitoring system called a “smart gear sensor system” has been being developed. This system has a smart gear sensor and a monitoring antenna. The smart gear sensor has been created by using a laser sintering technique that allows a conductive ink to be sintered directly on the gear surface to form a sensor chain. This technique is also used to form an antenna pattern of the monitoring antenna. Experimentally, when the smart gear with the integrated sensor chain is brought parallelly close to the monitoring antenna while this antenna is connected to a network analyzer, a magnetic coupling happens. This wireless magnetic coupling is presented via the return loss signal gained by the network analyzer. Moreover, the conductivity of the sensor chain on the smart gear has been proven to affect the magnetically coupled return loss signal. Specifically, the shape of the return loss signal will be changed correspondingly. Since the sensor chain is sintered directly on the surface of the gear, the physical characteristics of the gear such as healthy or cracked will result in the quality of the sensor conductivity. Therefore, the smart gear's healthy or cracked characteristics can be observed via the return loss signal chart obtained by the network analyzer. In other words, the return loss signal can be considered essential data for the gear health monitoring process. Nevertheless, because the temperature will have a certain influence on the physical state of not only the gear but also the sintered sensor, the evaluation of the temperature to the magnetic coupling and its return loss signal becomes unavoidable. As a result, this study focuses majorly on the consideration of the influence of the temperature so that the accuracy of the proposed gear health monitoring method can be improved. The final experimental result and analysis demonstrate that the temperature also significantly impacts the return loss of the smart gear system.

In this paper, the feasibility and effectiveness of a guided-wave approach are investigated as a first step toward the development of an automated non-contact ultrasonic system for inspecting railroad spikes to locate cracks and breaks. An automated system is desired that can perform the inspection more effectively and reliably than current manual tapping or pulling methods. The goal of this research is to detect damage in railroad spikes in a well-controlled laboratory environment. A recent study using contact ultrasonic transducers for spike inspection revealed many limitations, including high signal attenuation, high sensitivity to transducer positioning, and the need to use an ultrasonic couplant. To circumvent these limitations and provide a more robust inspection method, an “airborne ultrasound” system comprised of an air-coupled transducer (ACT) for actuation and a laser Doppler vibrometer (LDV) for sensing is proposed and evaluated. The inspection system employs the concept of guided waves, which propagate with less attenuation at lower ultrasonic frequencies and, hence, a larger amplitude than that from the bulk waves typically used for ultrasonic nondestructive inspection (NDI). The detection of broken spikes is based on the premise that the wave energy will be attenuated in the transmitted wave inhibited by the damage and amplified in the reflected wave from additional waves scattered from the damage. Initial tests on cut spikes demonstrated that the proposed system performs with confidence of damage detection based on baseline comparisons with pristine spikes.

Fatigue damage characterization at the early stage is difficult using conventional inspection methods. Low-frequency nonlinear guided waves have shown promising sensitivity to fatigue damage. However, the frequency pair selection for wave mixing in the quasi-synchronism range relies on numerous trials to achieve good performance of combinational harmonic generation and to reduce the overlapping between the combinational and second harmonics, since an infinite number of combinations can be selected for the frequency pair. In this study, a frequency pair selection method is proposed to simplify these tedious procedures and to provide a guide on the selection of frequency pair for quasi-synchronous wave mixing. The proposed method can be applied to different quasi-synchronous wave modes, and a S-index is introduced to characterize the performance of the combinational harmonic generation. In this study, the fundamental symmetric mode (S0) waves are used for collinear wave mixing. The frequency pairs with different S-indices are used for wave mixing, and to demonstrate the effectiveness and performance of the proposed method on predicting the combinational harmonic generation. The web of a tapered flange beam construction section is modelled in the numerical simulation, which shows similar results as a thick plate and combinational harmonic can be generated. The numerical results show good agreement with the S-index prediction, and the overlapping between combinational and second harmonics decreases with the increase of S-index. The results with lower values of S-index show significant coupling between fundamental harmonics, which results in significant overlapping between combinational and second harmonics. Fatigue damage characterization using quasi-synchronous wave mixing shows the potential to be applied on thick plates. The finding of this study provides a guide on the selection of frequency pair for quasi-synchronous wave mixing.

For a metallic component, prediction of a remaining useful life (RUL) is of extreme importance to prevent a sudden failure of the structure. If a fatigue crack exists, the nonlinear interaction of a low frequency pumping wave (LF) and a high frequency probing wave (HF) generates additional responses at the frequencies equal to the sum and difference of the input frequencies. In this study, the nonlinear ultrasonic modulation is used for fatigue crack prognosis of the lifting-lug that connects a tension cable. Nonlinear ultrasonic modulation components are used to define a fatigue index. The functional relationship between fatigue index and the number of loading cycles is established based on Paris–Erdogan theories. Then, by curve fitting of the established function to the fatigue index values obtained at various loading cycles, the RUL corresponding to a fatigue index is estimated from the fitted function. The proposed RUL estimation method is associated with the following advantages: (1) the fatigue index is defined as a function of nonlinear ultrasonic modulation components, (2) the functional relationship between fatigue index and loading cycles is established for lifting-lug structure, and (3) online remaining useful life is predicted and updated using sequentially measured ultrasonic signals. To validate the proposed method, the lifting-lug specimen is used and subjected to cyclic loading. The experiment is conducted using surface-mounted piezoelectric sensors for ultrasonic signal excitation and measurement. The experimental results confirm that the proposed method can estimate the RUL successfully.

Recent developments in autonomous vehicle (AV) or connected AVs (CAVs) technology have led to predictions that fully self-driven vehicles could completely change the transportation network over the next decades. However, at this stage, AVs and CAVs are still in the development stage which requires various trails in the field and machine learning through autonomous driving miles on real road networks. Until the complete market adoption of autonomous technology, a long transition period of coexistence between conventional and autonomous cars would exist. It is important to study and develop the expected driving behavior of future autonomous cars and the traffic simulation platforms provide an opportunity for researchers and technology developers to implement and assess the different behaviors of self-driving vehicle technology before launching it to the actual ground. This study utilizes PTV VISSIM microsimulation platform to evaluate the mobility performance of unmanned vehicles at a 4-way signalized traffic intersection. The software contains three different AV-ready driving logics such as AV-cautious, AV-normal, and AV-aggressive which were tested against the performance of the conventional vehicles, and the results of the study revealed that the overall network operational performance improves with the progressive introduction of AVs using AV-normal, and AV-aggressive driving behaviors while the AV-cautious driving behavior stays conservative and deteriorates the traffic performance.

With the rapid development of smart cities, interest in vehicle automation continues growing. Autonomous vehicles are becoming more and more popular among people and are considered to be the future of ground transportation. Autonomous vehicles, either with adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), provide many possibilities for smart transportation in a smart city. However, traditional vehicles and autonomous vehicles will have to share the same road systems until autonomous vehicles fully penetrate the market over the next few decades, which leads to conflicts because of the inconsistency of human drivers. In this paper, the performance of autonomous vehicles with ACC/CACC and traditional vehicles in mixed driver environments, at a signalized intersection, were evaluated using the micro-simulator VISSIM. In the simulation, the vehicles controlled by the ACC/CACC and Wiedemann 99 (W99) model represent the behavior of autonomous vehicles and human driver vehicles, respectively. For these two different driver environments, four different transport modes were comprehensively investigated: full light duty cars, full trucks, full motorcycles, and mixed conditions. In addition, ten different seed numbers were applied to each model to avoid coincidence. To evaluate the driving behavior of the human drivers and autonomous vehicles, this paper will compare the total number of stops, average velocity, and vehicle delay of each model at the signalized traffic intersection based on a real road intersection in Minnesota.

Polymer-based composite incorporated with inorganic filler has shown great potential for developing high-performance piezoelectric sensors as they offer unique properties and design flexibility. The aim of this paper is to investigate the impact of nanoparticles on the piezoelectric property enhancement of the developed composite. The piezoelectric substrate made of PVDF-TrFE/ BaTiO₃ is replicated reversely through a master mold made of IP-Q resin with IDT design printed using a two-photon polymerization (2PP) method. IDT channels are filled with a conductive material, and excess material on the surface is etched using oxygen plasma. The crystalline phase characteristics and surface morphology of fabricated substrate are examined using Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscope (SEM). Strain detection of the developed sensor is evaluated by determining the change in scattering parameter using a network analyzer.

Sensors detect a change in a physical parameter around them and convert it to a measurable signal that can be construed and analyzed for further applications such as automation, wearable devices, human-machine interfaces, and medical devices, among countless other applications. For example, sensors in the biomedical field help to detect the physiological course of a body. Conventionally sensors are produced using photolithography and screen printing. These conventional methods are limited by the complexity and flexibility in manufacturing the sensors. But, additive manufacturing presents a flexible option to produce the sensors and integrate them into structures as a single unit. This paper shows an extrusion-based 3D printing method to print sensors using conductive filaments. A comb-shaped capacitive sensor was 3D printed using commercially available conductive filament on a 3D printed PLA substrate. Printing two different materials with different properties always remains a challenge in FDM, so this paper also explains the stepwise process for printing a conductive composite material along with a thermoplastic. Further experimental characterization of the filament and sensor was also performed. This paper compares the electrical properties of the feedstock and 3D printed parts to understand the impact of extrusion on the material. A custom-made experimental setup was designed to characterize the sensor. One of the challenges is the low melting point of 3D printed parts, limiting their use to low-power applications. However, most biomedical sensing applications are low power, so these sensors remain suitable for such applications. Hence, in the end, the authors have explored the viability of the integration of these 3D printed sensors with medical implants to manufacture smart biomedical devices.

Zinc oxide (ZnO) nanopowder-based nanoparticles (NPs) with a mean diameter of less than 100 nm were mixed with synthesized electrophoretic deposition (EPD) solutions under different concentrations and deposited onto silicon (Si), 3-Aminopropyl-Triethoxysilane (APTES) functionalized silicon (APTES-Si), and aluminum (Al) substrates. The wavelength ranges and intensity of each structure's emission spectrum are determined using fluorescence spectroscopy. We will describe our findings considering our hypothesis that the underlying substrate conductivity and concentrations of ZnO nanoparticles in the electrophoretic deposition solutions are significant components in achieving higher intensity fluorescence of ZnO thin films required for devices. This study shows that ZnO NPs with higher concentrations deposited on a conductive substrate emit higher emission intensity. The presence of visible emission spectra for ZnO NPs deposited on all three substrate types show potential for future optical device applications employing the next generation of nanoscale electronic materials.

Environmental conditions, variability in building material, and fabrication error are several contributors that shorten a structure’s service life. While infrastructure might not have visible exterior damage, there is always the risk for failure due to internal or obscured damage. There has been growing research in the area of Nondestructive testing. However, these methods often disregard uncertainties in the construction and maintenance processes. Nevertheless, providing an accurate and robust method for detecting, localizing, and estimating the severity of existing damage relies on parameterizing uncertainties surrounding material properties, composition, and loading history. This work combines conventional physics-based modeling with data-driven search strategies to infer unobservable information about the structure. The proposed approach uses Finite Element Analysis with automated adjoint calculation to efficiently obtain gradients in the solution with respect to the model parameters. The hybrid model updates the postulated material properties to match the mechanical compliance of a simulated structure with observations from a reference structure. Here we use a simple cantilever beam model to study and evaluate the effectiveness of the proposed approach. In addition, we analyze the results from multiple search strategies used to obtain the mechanical properties. We find that the benefit in search efficiency gained from the analytical evaluation of the gradients is offset by the additional computational cost of adjoint simulation compared to gradient-free search. However, we expect that the benefit of adjoint simulation would be more pronounced in problems with more degrees of freedom.

This research demonstrates an alternative deep learning-based approach for predictive modeling of wave propagation signals within damaged structural elements. Our goal is to evaluate the wave propagation spatio-temporal solution matrix for a given crack depth and crack location within the structural element. To achieve this purpose, we first collected the wave propagation time histories in a cracked rod for various crack depths and locations by solving governing differential equations via the spectral element method. Then we applied the Fourier neural operator (FNO) based autoencoder to learn the wavefield representation. The encoder part of the autoencoder maps the high dimensional parametric solution to a low dimensional space (latent space) and a feed-forward neural network is trained to learn this latent space by feeding corresponding parameters. For a new parameter set (crack depth and location), trained feed-forward neural network predicts the encoded solution, and the decoder part of the trained autoencoder decodes it to the corresponding high dimensional solution. The FNO based autoencoder together with feed-forward neural network achieved accurate reconstructions/predictions with minimal mean square error. Compared to time-domain spectral element approach, our deep learning-based model produces comparable results with a narrow margin of error. This deep learning-based wave propagation predictive model can be a valuable resource for generating data for a given crack depth and location, which can be used for inverse formulations and various structural health monitoring applications.

An FBG based pressure and temperature measurement setup was developed for low speed aerodynamics studies. A bare FBG sensor for measuring dynamic pressure of flow and a specially designed strain-insensitive FBG temperature sensor were packaged in the form of a compact probe. The FBG temperature sensor probe was used for both temperature measurement of flow and temperature compensation of bare FBG sensor. The setup was calibrated for pressure and temperature using a calibrated flow generator that could produce low turbulence air jets at different velocities. The FBG sensor assembly was calibrated for flow velocities ranging from 5 m/s to 40 m/s. The flow of jet was arranged to be perpendicular to the axis of FBG sensors. A 2-D fluid structure interaction simulation was carried out in Comsol Multiphysics to validate the experiments. From simulation results, flow induced FBG strain was found to have a linear dependence with dynamic pressure of the flow with a slope of 0.038 με/Pa. Pressure values determined from FBG experiments were found to be in close agreement with the pressure values obtained from simulation within experimental errors. Because of their diminutive size and flexibility, FBG sensors can be used to measure pressure and temperature in ground based aerodynamic test facilities with minimum intrusion to the flow.

A method using fiber Bragg grating (FBG) array-based shape sensing is proposed for structure health monitoring of cylindrical marine structures in this paper. Because of the characteristics of FBG, any strain applied on the FBG is encoded as a wavelength shift of the light reflected by FBG. FBG sensors, with flexibility and small size, are bonded on the surface of the cylindrical shell structure to measure the strain of each fiber Bragg grating on the same certain points. And then we analyze the relationship of each parameter between FBG sensors and cylindrical structure. Experimental results prove that it is feasible for this method using FBG array-based shape sensing to monitor the deformation of the cylindrical structure. This method can be widely adopted in structure health monitoring of curved structures due to high precision, simple operation and low cost.

The trailing edge of the variable camber wing is mainly composed of flexible skin and deformable ribs. The connection method of the skin and ribs will directly affect the performance of the wing. The use of corrugated flexible skin and segmented ribs can achieve the continuous, uniform and coordinated deformation of the trailing edge. This paper adopts an origami-style connection structure for the corrugated flexible skin and segmented ribs, which can store and transfer deformations, match the skin and rib segments geometrically, and transfer the load to the ribs evenly. The calculation results show that the variable camber trailing edge of this structure has good deformation capacity and high out-of-plane load-bearing capacity.

Variant aircraft is an aircraft that can change its shape during flight to obtain different aerodynamic characteristicstocope with different flight environments and complete different flight tasks. The wing with variable camber at the trailingedge, as a relatively easy deformation method in structure, has attracted much attention in the research field. In order torealize smooth and flexible deformation of wing with variable camber at trailing edge, distributed driving mode must beadopted. In this paper, a multi-section distributed driving device based on fiber reinforced flexible cavity with internal pressure is proposed. The influence of fiber braided sleeve on the flexible cavity with internal pressure, the internal structure layout design of the driving device, the connection between components in the device and the connectionbetween the device and the wing are studied. The driving device has been applied to a wing with variable trailingedgecamber, and satisfactory results have been obtained. It is proved that the driving device can improve the drivingreliability on the basis of meeting the driving requirements, so that the whole trailing edge variable camber wingcanmaintain good continuity during deformation.

Civil infrastructures are susceptible to damage due to external forces such as winds and earthquakes. These external forces cause damage to buildings and different civil structures. To prevent this, active control systems are executed. These systems use sensors to measure the displacement of the infrastructure, then actuators are utilized to provide a force that counteracts that displacement. In this study, a proportional integral derivative (PID) controller was used to minimize the impact of an earthquake disturbance on multi-story structures. The proportional, integral, and derivative gains of the controller were obtained using particle swarm optimization (PSO). This PID controller was validated on a simulated five-story structure based on the Kajima Shizuoka building with five ideal actuators. The effectiveness of the PID controller in reducing the seismic response of the structure with regards to inter-story displacement and acceleration was compared to the uncontrolled response of the structure. It is found that the PID controller with PID parameters obtained from the PSO algorithm offers effective control for the simulated five-story structure.

Strain is an important parameter reflecting structural state, which is particularly important in the field of structural health monitoring (SHM). It can be used to evaluate the mechanical properties, failure behavior, crack development and residual stress of structural members and materials. It is particularly important in the field of structural health monitoring (SHM). In this paper, a new type of large gauge strain sensor is proposed. The proposed sensor is based on micro vision and uses a camera to capture small displacement. The packaging structure of strain sensor with sensing gauge length of about 50 cm is designed. In order to realize the monitoring in the field environment, the microscopic images in the experiment are obtained by webcam, which has broad application prospects. In the field of view of the camera, the maximum distance that the probe in the sensor can move is certain, but when the sensing gauge distance increases, the accuracy of strain measurement will be improved. The strain data obtained by the sensor is compared with the data obtained by FBG sensors to verify the measurement accuracy. It is found that the measurement accuracy is suitable for SHM of infrastructure.

Smart cement-based nano-reinforced structures have improved properties and are expected to remain intact for a long time. Corrosion attack, mainly through the pores, causes reduction of properties in most materials. Nano-reinforced structural components intended to be used in the construction industry require an understanding of their corrosion resistance behavior. The present work deals with the investigation of chloride penetration mechanisms in mortars modified with multi-walled carbon nanotubes. The tested structures were artificially corroded via salt spray fog and their surface electrical resistivity, as well as their flexural and compressive strengths were assessed. One of the main goals of the study is to evaluate the effect of nanotube concentration on the accelerated corrosion damage. It was observed that the insertion of different percentages of carbon nanotubes affects the mortar chloride penetration, as well as enhances the flexural and compressive response of the material, comparing to plain specimens, due to the filling of pores with sodium chloride. Also, the electrical resistivity of the specimens was evaluated prior and after the exposure of the mortar samples to salt spray fog.

The authentication and sustainability of cultural heritage monuments are of great importance due to their uniqueness. Recent accidents and natural disasters have taught us that all monuments, even the most protected such as the cathedral Notre-Dame de Paris, can be destroyed by an unexpected event. For these reasons, it is of paramount importance the creation of a monument database containing fusion data not only of the accurate 3D model and precise representation of the artifacts/monuments but also about the condition and procedures of restoration and preservation and the structural health of the antiquities by means of quantitative non-destructive techniques, such as Infrared Thermography. The 3D model of a monument would guarantee the accurate restoration of the monument in case of a catastrophic disaster, while the 3D model of an artifact together with the entire related metadata would provide a digital identity, therefore guaranteeing the sustainability of the item. Also, in case of a movable artifact, it will be a useful tool towards the validation / authentication of the artifact in case it is damaged or missing.

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. Numerical analysis and 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.

Construction, design, and maintenance of civil structures involves huge money and manpower. To extract the maximum potential use of such structures they need continuous monitoring, for checking any deformity in early stages. Many studies incorporating conventional sensors like electric strain gauge (ESG), optical fibers wire, piezo ceramic materials i.e., Lead zirconate titanate (PZT) have been proven successful in detecting damage of structures but accompanied with many challenges including environmental and operational challenges, compatibility, and durability issues. This paper covers the development of a concrete-based sensors in size 50×50×50 mm suitable for embedding in concrete structures by doping four different dosages of multi-walled carbon nanotubes (MWCNTs) in concrete to develop concrete-based sensors ensure compatibility with surrounding concrete. They were evaluated under different loading conditions. Sensors possess enhanced sensitivity, high durability, and compatible life as the life of structure. They were found successful in detecting static strain measurements obtained in form of resistance changes. A frequency response function was also obtained using LCR meter in the range of frequency 100-500 kHz.