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Flexible capacitive pressure sensors have garnered significant attention in research areas such as electronic skin, wearable devices, medical diagnosis, physical health detection, and artificial intelligence due to their advantageous characteristics, including high sensitivity, flexibility, lightness, and easy integration. Over the past few years, the field of flexible capacitive pressure sensors has experienced notable advancements in materials, structural design, fabrication processes, and applications. This review aims to examine the different materials employed for sensor electrodes and dielectric layers, as well as the structural design of these sensors. Additionally, we delve into the diverse fabrication processes and techniques utilised, including electrode and dielectric layer fabrication, as well as weaving technology. Lastly, we explore the various applications of flexible capacitive pressure sensors, encompassing electronic skin, health monitoring device, electronic textiles, and structural health monitoring. [ABSTRACT FROM AUTHOR]

Advanced engineering materials like glass fabric-reinforced composites (GFRC) are frequently utilized in automotive components, civil structures, aviation sectors, etc. Contrary to metals, the anisotropic nature of composites makes it difficult to forecast damage and failure under real-time loads. GFRC is employed in this work to demonstrate structural health monitoring (SHM) using a reduced graphene oxide (rGO) coated glass fabric piezo-resistive sensor. The sensor was embedded in the GFRC composite to detect changes in the fractional electrical resistance under flexural strain. The developed composite specimens were subjected to three-point bending to examine the piezo-resistive performance. Throughout testing, the effect of sensor width and relative positions in the thickness direction within the composite specimen on strain and damage detection was assessed. The results of the tests revealed that these parameters were responsively associated with the piezo-resistance of the developed sensor. This study concluded that the developed piezo-resistive sensor has the potential to be used as a strain and damage detection mechanism for glass fabric-reinforced composite structures for disparate applications. [ABSTRACT FROM AUTHOR]

In this study, we propose a structural health monitoring and diagnostic method for layered (multi-story) structures using a convolutional neural network (CNN). The proposed method is a primary diagnostic one, and its purpose is to allow quick identification of the location of an abnormality after detecting it. An abnormality is defined as a decrease in the stiffness characteristics (spring constant) of the outer wall of a multi-story structure when it deteriorates or is damaged. The proposed method has the following features. A modal circle is generated by multiplying the frequency response functions (FRFs) simulated by a mathematical model and the FRFs from the actual structure, in frequency space, and then a CNN learns the features of the abnormality from the modal circle and diagnoses it in the actual multi-story structure. We first verified the validity of the proposed method by considering a three-story structure as a numerical example. When the method was applied to three types of abnormal conditions, it was shown that the abnormal diagnosis could be performed correctly. Next, we constructed an experimental model of a three-story structure, and realized three types of abnormal conditions similar to those in the numerical model. We verified the applicability of the proposed method and showed that correct diagnosis of an abnormality was possible. Both the validity and applicability of the proposed method were thus confirmed. [ABSTRACT FROM AUTHOR]

Piezothermoelasticity and wave interaction studies hold immense significance in designing functional devices ranging from transducers to sensors for a variety of purposes like energy harvesting and structural health monitoring. These applications catalyze interest in this article which addresses the problem of reflection of plane wave at the boundary of piezothermoelastic half-space. Through this study, the effect of impedance parameter on amplitude and energy ratios of the reflected waves is studied. Four wave modes are indicated upon reflection and a linear system of equations is formed to obtain a closed-form expression for amplitude and energy ratios. These equations are solved by suitable mathematical tools leading to expression for amplitude ratios as a function of incidence angle. For a suitable piezothermoelastic medium, the ratios are plotted against incidence angle and the findings are compared for two well-known theories of thermoelasticity, namely, Lord–Shulman (LS theory) and Green–Lindsay (GL theory). The analytical outcomes suggest approximate values of impedance and incidence angle for preferred energy division between reflected waves. It is recognized that adding impedance increases the amplitude of the quasi-longitudinal (qP) wave and decreases that of the quasi-transverse wave, making it suitable for devices that require a more robust qP wave signal detection. [ABSTRACT FROM AUTHOR]

In practical applications of wavelet transform, engineers and practitioners encounter challenges that arise due to the disparity between wavelet theory, which deals with continuous functions, and the digital nature of signals in engineering contexts. In particular, wavelet transform theory does not consider the effect of changes in digital signals on the result of the wavelet transform. This paper emphasizes the influence of the type of digital signals on the accuracy of wavelet transform in engineering applications and proposes an efficient wavelet function based on the derivative of the signal for better damage detection in beam structures. For this purpose, the obtained signals from the mode shapes of the steel beam are used to examine the efficiency of the proposed derivative-based wavelet transform. The effects of changes in boundary conditions, location of damage, and level of damage on the performance of the proposed method, are evaluated. Findings show that when we use the derivate of the signal in the wavelet transform, the location of damage in all damage scenarios is detected with high accuracy. This research demonstrates the importance of the type of signal used in the wavelet transform for enhancing the precision of fault and damage detection in signals. [ABSTRACT FROM AUTHOR]

Water significantly influences the electrical resistivity and piezoresistive performance of piezoresistive cementitious composites (PCCs). In existing studies, it has been difficult to reflect the actual water permeability in real structures using overall moisture content of specimens. Thus, to facilitate structural health monitoring of piezoresistive cement-based sensors in aquatic service, this study evaluated cementitious composites containing multiwalled carbon nanotubes to create a piezoresistive cement-based sensor. The variations in electrical signals were monitored to assess the internal water permeability of the specimens. An improved method for the installation of laterally arranged copper electrode meshes was developed. The changes in electrical resistivity and gauge factors before and after water permeability experiment were defined as the fractional change in permeability electrical resistivity (FCPR) and the fractional change in gauge factor (FCGF), respectively. These metrics were utilized to assess the extent of water permeability in the water-permeated specimens based on the ranges of FCPR and FCGF. The experimental results indicated that (1) with an increase in water permeability time, the moisture content and seepage height of the water-permeated specimens gradually increase, the degree of decrease in electrical resistivity becomes more pronounced, and FCR has an increasing fluctuation with periodic rises and falls under the same connection; (2) the electrical signals in the semidry region above the water mark exhibit slight fluctuations, indicating that the piezoresistive cement-based sensor can provide advanced warning of water permeability; and (3) the more extensive the water permeability, the higher are the FCPR and FCGF exhibited by the piezoresistive cement-based sensors, allowing for the assessment of water permeation. This study provides a new understanding of the unique properties and potential applications of piezoresistive cement-based sensors in aquatic environments, paving the way for their future application in monitoring and maintaining aquatic services. Practical Applications: This paper introduces a piezoresistive cement-based sensor formed by incorporating carbon nanotubes into cementitious composites. However, during service of structural health monitoring in concrete structures using piezoresistive cement-based sensors. In contrast to other studies aiming to mitigate the impact of moisture, this paper leverages the high sensitivity of the piezoresistive cement-based sensor to moisture. The water permeation in the water-permeated specimens is evaluated through changes in the electrical signals. The objective is to establish the transverse and longitudinal arrangement of piezoresistive cement-based sensors arrays in RC structures in aquatic service for water permeability monitoring and damage monitoring. As the water gradually permeates the RC structure, the piezoresistive cement-based sensors at different water permeability conditions exhibit distinct electrical signal changes. This will allow for advanced warning of steel corrosion and real-time monitoring of damage development in aquatic service for RC structures. [ABSTRACT FROM AUTHOR]

Structural assessment of buried energy pipelines is often hindered by the abundance of external vibrations resulting in nebulous noises. Effective and secure nondestructive approaches need to be devised to efficiently reduce noise in multidimensional magnetic anomaly signals collected from a pipeline. This study focuses on the mechanism by which a measured source signal can be broken down into low- and high-frequency constituents known as intrinsic mode functions (IMFs). By doing so, a well-defined set of instantaneous frequencies is obtained utilizing improved complete ensemble empirical mode decomposition (ICEEMDAN) algorithms. These IMFs contain useful structural evidence across multiple scales that can be extracted for effective identification of the defect location. To accomplish this objective, first, the signal gradients are calculated using dual-density complex wavelet transform to diminish the influence of the geomagnetic field. The multiscale variance fusion (MVF) algorithm is then adopted to quantize the fluctuations occurring in each individual IMF. The output signals generated by computing the variances provide sufficient information about the location and severity of the pipeline defects. Numerical simulations for a buried pipeline model have been presented to validate the authenticity of the proposed technique. Indoor laboratory implantation on a pipeline test sample with prefabricated defects justifies the effectiveness of the ICEEMDAN-MVF model, to localize hidden structural flaws in energy pipelines without physical contact and even in more complex environments with multiple sources of magnetic interference. [ABSTRACT FROM AUTHOR]

Crack detection or segmentation on concrete structures is a vital process in structural health monitoring (SHM). Though supervised machine learning techniques have gained tremendous success in this domain, data collection and annotation continue to be challenging. Image data collection is challenging, tedious, and laborious, including accessing representative datasets and manually labeling training data in the SHM domain. According to the literature, there are significant issues with the hand-annotation of image data. To address this gap, this paper proposes a two-stage weakly supervised learning framework utilizing a novel "crack attention network (CrANET)" with attention mechanism to detect and segment cracks on images with no human annotations in pixel-level labels. This framework classifies concrete surface images into crack or no-cracks and then uses gradient class activation mapping visualization to generate crack segmentation. Professionals and domain experts subsequently evaluate these segmentation results via a human expert validation study. As the literature suggests that weakly supervised learning is a limited practice in SHM, this research title will motivate researchers in SHM to research and develop a weakly supervised learning approach processing as state of the art. [ABSTRACT FROM AUTHOR]

An innovative bolt loosening detection method based on double-layer slotted circular patch antenna is proposed. By integrating the patch antenna with the bolt, the longitudinal length variation of the bolt can be transformed into a change in the air gap thickness of the antenna and cause the resonant frequency to vary. Thus, we can detect the bolt loosening through the resonant frequency shift of the double-layer circular patch antenna. Furthermore, the circular radiation patch of the antenna is slotted to increase the effective length of current flow, thus achieving the miniaturization of the antenna sensor for bolt loosening detection. The proposed double-layer slotted circular patch antenna sensor can be as small as a coin with a high sensing sensitivity of 626.57 MHz/mm, and it can be interrogated either wired or wirelessly. A series of simulations and experimental tests demonstrate the feasibility and effectiveness of the proposed antenna sensor for bolt loosening detection. The experimental results show that the relationship between the bolt preload and the antenna resonant frequency is relatively linear, and the resonant frequency of the antenna shifts 18 MHz for an M24 bolt from completely loosened to fully fastened. [ABSTRACT FROM AUTHOR]

Accurate identification of impact loads is vital for structural assessment and design. Traditional methods rely on complex equipment, such as accelerometers or strain gauge, which can be expensive and prone to failure. This study introduces a non-contact intelligent identification approach incorporating visual sensing technology, providing a convenient means to identify impact loads. Numerical simulations explore the differences in identifying impact forces through acceleration and displacement responses, particularly by considering such variables as measurement noise and number of measurement points. A meticulously designed experiment verified the feasibility of the proposed method for measuring the displacement and velocity of rapidly moving targets, and evaluated its performance in terms of accuracy. A series of impact loading experiments were conducted on a simply supported girder bridge model to validate the effectiveness of the proposed impact force identification method. Results indicate strong agreement between displacement response measurements and percentile meters. The proposed non-contact method accurately identifies single or continuous impact loads, with a minimum peak relative error of 0.2%. This study represents a pioneering application of intelligent visual sensing technology in the field of impact load identification. Moreover, the current research introduces a novel approach to address the challenges faced by conventional methods in identifying impact loads. Future research can leverage the groundwork laid by this study to further optimize and expand the proposed method, enhancing its capabilities and fully harnessing its potential to offer advanced solutions in structural health monitoring. [ABSTRACT FROM AUTHOR]

The combined use of finite element modeling and structural health monitoring is becoming increasingly relevant in bridge maintenance. A growing number of new and old structures is instrumented with different kinds of sensors driven by dedicated hardware and software, while numerical models are used to simulate the response of the structures under various scenarios. This paper presents the monitoring of the Smithfield Street Bridge, one of the oldest and most iconic bridges in the city of Pittsburgh (Pennsylvania, USA). The bridge was instrumented by an independent party, not involved with the research presented here, with strain, displacement, and rotation wireless sensors. A detailed finite element model of one of the spans of the bridge was developed to calculate the strains induced by standardized trucks under pristine and simulated damage conditions. In addition, the model enabled to determine the sensitivity necessary to detect relevant structural changes in the bridge. The results of the numerical analyses were then compared with the results of a test in which a truck of known weight crossed the bridge multiple times. Finally, the data relative to 3 years of uninterrupted monitoring were processed and analyzed to identify eventual anomalies. [ABSTRACT FROM AUTHOR]

Temperature variability is one of the critical environmental conditions that causes confusing changes in structural properties and dynamic responses of bridges similar to damage. In this case, false alarms and mis-detection are among the major errors in health monitoring of such civil structures. High damage detectability is another significant challenge in bridge health monitoring. To deal with these issues, this article proposes an unsupervised damage assessment technique comprising two steps of data normalization and novelty detection. For the first step, an adjusted artificial neural network is considered to remove the effects of temperature variability from dynamic features (modal frequencies). This process is carried out by an auto-associative neural network by adjusting its hidden layer neurons through a new hyperparameter selection algorithm. Using normalized features obtained from the first step, this article proposes three multivariate covariance-based distances called linear dissimilarity analysis, multivariate Kullback–Leibler divergence, and multivariate Bregman distance to compute damage indices or novelty scores for damage assessment. The fundamental principles of these distances lie in three aspects: dividing the normalized features into segments, estimating the covariances of segmented feature sets, and incorporating the estimated covariances into the proposed distance measures. The major contributions of this article include proposing three non-parametric distance measures and developing an unsupervised data normalization framework via a new hyperparameter tuning algorithm for adjusting an artificial neural network. A concrete box-girder bridge is considered to verify the proposed approach, along with several comparative studies. Results show that the method presented here can mitigate severe temperature variability and increase damage detectability with superiority over some traditional and state-of-the-art damage assessment techniques. [ABSTRACT FROM AUTHOR]

In recent years, the deployment of structural health monitoring (SHM) systems has become paramount for safeguarding critical infrastructures. Notwithstanding, the development of an unsupervised deep learning framework capable of learning from long-term sensor data remains a critical challenge, particularly in accurately assessing the exact damage location. This study addresses this gap by proposing a novel approach for rapid bridge damage assessment. The proposed method employed a deep overcomplete encoder–decoder network (DOEDN) to reconstruct the acceleration data acquired from each sensor node on the bridge. The reconstruction losses generated by the DOEDN framework are then used as damage-sensitive features. Additionally, a damage indicator based on Gaussian processes is introduced to assess the damage location and evaluate its severity. The performance and sensitivity of the proposed DOEDN framework are evaluated through long-term monitoring acceleration data from a numerical highway bridge model and the well-known full-scale Z24 bridge. Furthermore, comparative assessments against the regular deep undercomplete encoder–decoder network are conducted using metrics including mean absolute error, coefficient of determination (R 2), and mean intersection over union. The results show that the proposed DOEDN framework can reasonably assess the damage location and evaluate its severity across various structural scenarios in the bridge, even in the presence of temperature variations, thus providing a practical and effective solution for bridge health monitoring. [ABSTRACT FROM AUTHOR]

Concrete crack detection is a crucial task for the safety and durability of engineering structures. Extensive research has been conducted on deep-learning methods employing horizontal bounding boxes (HBBs) for crack detection. However, due to the inherently random distribution of concrete cracks, HBB-based methods often produce excessive overlaps and encompass extensive background regions, obstructing the effective interpretation and adaptation of the detection results. To address this issue and achieve efficient utilization of bounding box space for detecting cracks at any orientation, a rotated bounding box (RBB)-based method, that is, Rotated Faster R-CNN with a post processing strategy (RFR-P), was proposed. To realize this method, an RBB-based crack annotation strategy was introduced to standardize the annotation baseline for the evolutionarily established RBB-based crack detection dataset. Then, an RBB-based post-processing strategy was inventively developed to quantify the patterns of cracks with their corresponding rotation angles encompassing longitudinal cracks, transverse cracks, and diagonal cracks. Subsequently, experimental results showed that the RFR-P method provides more reasonable and elaborate detection results in terms of crack distribution patterns when compared to HBB-based methods. Based on the comprehensive consideration of evaluation metrics and detected results, it can be concluded that the RFR-P is aptly designed for detecting cracks at any rotation angle with relatively high accuracy. Finally, an RBB-based concrete crack detection platform was established to automatically detect in situ concrete bridge cracks for real-world applications. The proposed RFR-P model introduces a new perspective on crack detection methods and offers practical references for structural condition evaluation. [ABSTRACT FROM AUTHOR]

Bolt loosening in bolted lap-joint connections can result in catastrophic failures in industrial settings. Therefore, it is crucial to investigate these connections thoroughly and implement preventative measures. However, examining such connections requires considering multiple influencing parameters. In this study, numerical simulations were conducted using three-dimensional finite element analysis in Abaqus software to evaluate these parameters in detail. The study employed a guided wave-based structural health monitoring approach and a precise ultrasonic-guided Lamb wave propagation method. Time signals were analyzed using phase shift and transmission coefficient (TC) factors as suitable indicators. Additionally, the S0 mode of the captured waves provided a highly informative wave packet for analysis due to its specific order. Initially, various simulations were carried out to determine the optimal length of the overlapping region that would result in the most effective transmission of waves. Increasing the bolt torque and the contact friction coefficient between the two plates in the overlapping region reduces the time for waves to reach the sensor and increases the TC of the passing waves. However, the saturation state does not cause any change in the received signals. Increasing the bolt torque is more effective in facilitating wave transmission through the connections than increasing the contact friction coefficient. Furthermore, variations in excitation voltage do not affect the arrival time or TC of the waves. The continuous wavelet transformation enabled a more detailed time-frequency analysis. The results demonstrate that changes in bolt torque do not impact the wave frequencies reaching the sensors, making the frequency insensitive to bolt loosening. Ultimately, the numerical simulation results were validated through experiments on a table-top laboratory sample. This research enhances researchers' understanding of monitoring the health status of bolted lap-joint connections by investigating the impact of various parameters on guided Lamb wave transmission through these connections. [ABSTRACT FROM AUTHOR]

This paper presents a critical review of literature on the emerging technology known as digital twin and its application in structural integrity management for marine structures. The review defines digital twin in relation to structural integrity management as a virtual representation of a physical structure that mirrors the same structural conditions in real time. Twinning is a dynamic process that involves reducing the discrepancy between the virtual representation and physical structure, which is achieved with the aid of monitored data. Regarding the state-of-the-art concerning marine structure applications, all require the creation of a finite element model to represent the physical structure. Several practical schemes for physical to virtual interconnection have been proposed, but few researchers have concentrated on virtual to physical feedback. In addition, most works have focused only on assessing the current states of structures. To address this, a digital twin-based monitoring framework is proposed and three key enabling technologies, namely model updating, real-time simulation, and data-driven forecasting are demonstrated using a numerical case study. Such technologies enable structural diagnostics, as well as prognostics, to support decision making such as inspection/maintenance planning. Based on the case study, the opportunities and associated challenges of digital twin are discussed. For instance, to fully exploit the potential of digital twin, challenges related to monitoring systems such as standardisation, enhanced redundancy for long-term application, and monitored data quality assurance need to be addressed. Further, because digital twin can avail a vast amount of data, a dedicated data mining capability should also be incorporated. [ABSTRACT FROM AUTHOR]

Several recent studies have investigated the opportunities of differential interferometry synthetic aperture radar (DInSAR) using satellite data for structural health monitoring (SHM) of civil structures. However, its use in structural engineering is still debated because of the lack of a general understanding of the potential and limitations of the technology for bridge SHM. To overcome this issue, specific methods of data processing and displacement assessment with error quantification need to be developed. The present paper aims at giving an insight into the use of small baseline subset-differential synthetic aperture radar interferometry (SBAS-DInSAR) as a remote-sensing technology for civil infrastructure monitoring combining information at a large scale with those associated with a single bridge. In particular, an operational framework for the selection and processing of a measurement time series representative of the structural behavior of the bridge of interest is designed in a way that fully remote characterization and assessment can be carried out by exploiting web-mapping platforms according to the crowd-sensing paradigm. The workflow, designed consistently with a structural engineering perspective, has been tested with reference to a number of bridges crossing the Tiber river in Rome (Italy), showing that the proposed fully remote monitoring procedure can effectively support satellite data processing and interpretation. Moreover, some issues in view of the general use of satellite data for bridge monitoring emerged from the study. Gaps in the measurement point distribution over the bridge decks are frequently observed over all the monitored area, therefore simplified methodologies aimed at estimating expected displacement ranges of different bridge typologies under serviceability loads have been defined enabling a rational interpretation of the data in the light of the selected radar sensor band of operation. Simplified formulations and charts for service and environmental loads are then presented with a twofold objective: (1) supporting the satellite data elaboration and interpretation by means of easy to manage open-source tools; and (2) providing estimates of the expected operational displacements of healthy structures to guide data interpretation and even algorithms for radar signals processing able to incorporate the response of structures to temperature variations. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) construction stands as one of the most prevalent engineering endeavors, underscoring the importance of routine structural health assessments. Numerous factors, including overloading, design deficiencies, and fatigue, pose risks to RC structures by inducing cracks in various components. These damages significantly compromise the strength of RC constructions, necessitating vigilant monitoring to avert catastrophic structural failures. Structural health monitoring (SHM) techniques primarily aim to detect such damages. Leveraging piezoelectric (PZT) principles, the electromechanical impedance (EMI) methodology emerges as a promising SHM approach. EMI relies on variations in responses recorded through the electromechanical interaction between PZT and the structure to identify structural damages. Admittance signatures, comprising conductance (G) and susceptance (B), serve as indicators of structural condition, with deviations in the plot of G across a frequency range signaling structural deterioration. PZT transducers, either embedded within the structure or surface bonded, facilitate damage monitoring. This study aims to assess the damage detection capabilities of surface bonded PZT and embedded concrete vibrational sensors (CVS) in grade M25 RC beams. The root mean square deviation (RMSD) serves as the damage index, derived from variations in signatures recorded at different load levels. Experimental testing, conducted on nine RC beam specimens using a universal testing machine (UTM), yielded reliable results for comparison. Comparative analysis between surface bonded PZT and embedded CVS evaluated their performance in detecting structural anomalies under various load levels using RMSD values. Baseline conductance signatures revealed a lower amplitude for CVS due to inherent damping effects within the concrete material. Additionally, CVS consistently displayed lower RMSD values than PZT across different load levels, indicating lesser sensitivity to surface cracks and defects owing to its embedded position within the concrete. [ABSTRACT FROM AUTHOR]

This study introduces an innovative "Big Model" strategy to enhance Bridge Structural Health Monitoring (SHM) using a Convolutional Neural Network (CNN), time-frequency analysis, and fine element analysis. Leveraging ensemble methods, collaborative learning, and distributed computing, the approach effectively manages the complexity and scale of large-scale bridge data. The CNN employs transfer learning, fine-tuning, and continuous monitoring to optimize models for adaptive and accurate structural health assessments, focusing on extracting meaningful features through time-frequency analysis. By integrating Finite Element Analysis, time-frequency analysis, and CNNs, the strategy provides a comprehensive understanding of bridge health. Utilizing diverse sensor data, sophisticated feature extraction, and advanced CNN architecture, the model is optimized through rigorous preprocessing and hyperparameter tuning. This approach significantly enhances the ability to make accurate predictions, monitor structural health, and support proactive maintenance practices, thereby ensuring the safety and longevity of critical infrastructure. [ABSTRACT FROM AUTHOR]

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring, such as that carried out by a series of accelerometers placed on the structure, is certainly a goal of extreme and current interest. In the present work, the results obtained from the processing of experimental data of a real structure are shown. The analyzed structure is a lattice structure approximately 9 m high, monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels. The data used refer to continuous monitoring that lasted for a total of 1 year, during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure. Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested, as well as a methodology combining the two techniques. The results obtained are extremely interesting, as all the minor damage caused to the structure was identified by the processing methods used, based solely on the monitored data and without any knowledge of the real structure being analyzed. The results use 15 acquisitions in environmental conditions lasting 10 min each, a reasonable amount of time to get immediate feedback on possible damage to the structure. [ABSTRACT FROM AUTHOR]

Bridges may develop breathing cracks under excessive overloading vehicles, while conventional beam models are ineffective in analyzing the effect of spatial distribution of these cracks. This study proposes a data‐driven detection model with the consideration of spatial distribution of breathing cracks that can detect the multiple damage locations and degrees of breathing cracks in plate‐like bridges. Firstly, a 2D vehicle–bridge interaction model containing breathing cracks is established, and the damage indicator, contact point displacement variation (CPDV), is calculated using vehicle acceleration data. Next, a dataset with CPDV as the input feature is generated using the finite element method to train the CatBoost‐based damage prediction model, which considers the random distribution of single and multiple cracks, as well as the influence of different vehicle speeds. Finally, by calculating the CPDV related to the actual bridge and feeding it into the trained model, the location and degree of the damage can be predicted. The numerical simulation results demonstrate that this approach can accurately detect complex crack information under various vehicle speeds and exhibits robustness against road roughness. A laboratory experiment further confirms the effectiveness, applicability, and feasibility of this method to multiple damage locations and degree of breathing cracks. [ABSTRACT FROM AUTHOR]

Structural health monitoring is essential for the lifecycle maintenance of tunnel infrastructure. Distributed fiber‐optic sensor (DFOS) technology, which is capable of distributed strain measurement and long‐range sensing, is an ideal nondestructive testing (NDT) approach for monitoring linear infrastructures. This research aims to develop a distributed sensing network utilizing DFOS for structural integrity assessment of concrete immersed tunnels. The primary innovations of this study lie in the development of a general flowchart for establishing a sensing network and obtaining reliable field data, as well as its subsequent validation through a detailed case study. Concentrated joint deformations in typical immersed tunnels, detectable by the DFOS, are key indicators of structural integrity. This study addresses crucial elements of field monitoring system design, including the selection of appropriate optical fibers or cables and the determination of vital interrogator system parameters. It also covers sensor parameter determination, installation techniques, field data collection, and postanalysis. Furthermore, this research is exemplified by a case study that illustrates the successful implementation of a distributed sensing network in an operational immersed tunnel, and monitoring data reveals cyclic structural deformations under impacts of daily tide and seasonal temperature variations. The data obtained from this network play a significant role in subsequent condition assessments of tunnel structures. The research findings contribute to the assessment of large‐scale infrastructure health conditions through the application of DFOS monitoring. [ABSTRACT FROM AUTHOR]

The Probabilistic Multistage (PM) algorithm was developed by authors for identifying and localizing damages in isotropic plates. More specifically, PM algorithm consists of a fully automated probabilistic damage imaging methodology based on ultrasonic guided wave propagation and a 5-sensor array working in a pitch-catch approach. The algorithm processes the gathered data in three steps to identify key features associated with damage. The aim of this work is to assess the effectiveness of the PM algorithm on more complex structures. Specifically, the study investigates three cases of study, made of Carbon Fiber Reinforced Plastic (CFRP) composite, characterized by different geometries and layups. The algorithm is initially tested on panels with artificial damage in the form of a Teflon disk located in a specific location between the middle laminae of the panels, which is used to replicate the effect of delamination. In order to expand the experimental dataset without incurring additional costs or waste, new damage conditions are simulated by adding masses on the upper surface of the panels. Each plate is investigated considering three different damage sizes and 16 different damage locations. The proposed algorithm successfully detects damages both within and outside the sensor network. The PM algorithm produces a clear damage positioning map and a positioning (probabilistic field) range for the identified damage. This information can be used to assist operators in conducting inspections more efficiently by focusing on the highlighted areas, which may potentially lead to reduced maintenance and repair expenses. [ABSTRACT FROM AUTHOR]

Material extrusion (ME) is a widely used additive manufacturing (AM) technique, known for its versatility, cost-effectiveness, and ability to produce complex parts on-demand with greater customization and reduced waste. However, the process is impeded by unpredictable factors causing defects such as voids, overextrusions, and underextrusions, which smart manufacturing in Industry 4.0 aims to mitigate. In this study, we report a novel infrared (IR) thermography–based continuous data acquisition and processing framework that can differentiate various levels of in situ underextrusions. While existing underextrusion detection techniques require mid-print interruptions, our framework detects defects without any interruption. The methodology includes integrating an IR camera into a commercially available extrusion-based 3D printer for continuous in-printing data acquisition. The G-code for printing a rectangular block is intentionally modified to induce various levels of known underextrusions. Additionally, a novel signal processing algorithm is developed to automate real-time data processing and analysis, including signal normalization, artifact removal, and feature extraction. Results are obtained by developing a correlation matrix to compare the correlation coefficients of time series thermal data from the printed samples. Time-domain thermal features are also extracted to identify extrusion levels of 25%, 50%, 75%, and 100%. This study demonstrates that by utilizing the proposed framework, thermal data can identify various extrusion levels without mid-print interruption and determine the severity of process deviations within 5 s. This framework paves the way for integrating a thermal data-driven closed-loop monitoring and adjustment system capable of producing first-time-ready parts. [ABSTRACT FROM AUTHOR]

The conventional approaches for detecting structural degradation are time-consuming, labor-intensive, and costly. The physical monitoring of the structure also poses risks to the health and safety of supervisors. Therefore, damage estimation of any structure using artificial intelligence (AI), more specifically deep learning (DL), is becoming more significant in civil infrastructure. In the presented research article, an efficient two-stage damage detection method is proposed for structural damage detection (SDD) from time domain vibration signals. The proposed method utilizes two-dimensional convolutional neural network (2D-CNN) architecture as a DL algorithm for damage detection. Here, a computer-aided damage detection method for steel beam and frame-type structures is developed using 2D-CNN algorithm in the Google Colab platform. The effectiveness of the proposed method is first verified, and it provides more than 90% accuracy for identifying the damage location and severity of a cantilever beam for single- and multi-damage scenarios from numerically simulated noisy displacement data. The algorithm is also experimentally validated through the raw acceleration data of damaged steel frame joints collected from the Qatar University Grandstand simulator (QUGS). The proposed 2D-CNN algorithm performs better than other DL algorithms by achieving 100% accuracy within 10 epochs for damage detection of steel frames using QUGS data. It demonstrates significant potential for detecting damage location and quantifying damages for single- and multi-damage scenarios using noise-free and noisy datasets. The primary contribution of this study resides in the implementation of two-stage damage detection algorithm utilizing 2D-CNN with time domain vibration response for multiclass damage identification and quantification. [ABSTRACT FROM AUTHOR]

In structural health monitoring (SHM), an important issue is the limited availability of measurement data due to the spatial sparsity of sensors installed on the structure. These measurements are insufficient to accurately depict the actual dynamic behavior and response of the structure. Therefore, full‐field (i.e., every degree of freedom) structural response reconstruction based on sparse measured data has drawn a lot of attention in recent years. Kalman filter (KF) is an effective technology for response reconstruction (also known as state estimation), providing an optimal solution for systems that can be well‐represented by a fully known Gaussian linear state‐space model. This implies that both the process noise and measurement noise follow known zero‐mean Gaussian distribution, which is impractical in many civil engineering applications considering the unavoidable modeling errors and variations of environmental conditions. To address this challenge, a data‐physics hybrid‐driven method, i.e., KalmanNet, is proposed in this study for response reconstruction of partially known systems. By integrating a recurrent neural network (RNN) module into the KF framework, KalmanNet can efficiently learn and compute the Kalman gain using available monitoring data, without any Gaussian assumptions or explicit noise covariance specifications (e.g., covariance matrices of process and measurement noise). Both numerical and experimental investigations are conducted to validate this method. The results demonstrate that under the influence of non‐Gaussian noise and modeling errors, KalmanNet can effectively and accurately reconstruct the structural response from sparse measurements in real‐time and has higher accuracy and robustness compared to traditional KF even with optimal parameter settings. [ABSTRACT FROM AUTHOR]

Changing environmental conditions and measured noises often affect the dynamic responses of structures and can obscure subtle changes in the vibration characteristics caused by damage. To address this issue, a new method for classifying damage in offshore structures under varying environmental conditions and measured noises is proposed using linear discrimination analysis (LDA). Two sets of data on dynamic characteristics, one from healthy structures and the other from unknown testing structures, are used to determine the optimal projection vector. This vector is perpendicular to the discriminant hyperplane and is used for damage classification. The damage‐sensitive features are extracted by projecting both sets of data onto this vector. These features are then used with the hypothesis test technique to determine the condition state of the testing structure. Numerical studies on offshore wind turbine structures and experimental validations of a deep‐sea mining system are being conducted to evaluate the effectiveness of the proposed approach. The study also examines the impact of mode combinations, measured noises and samples on the performance of the approach. The results indicate that the proposed approach can accurately assess the structural health state even in the presence of environmental changes and noise contamination, even with limited samples. The promising performance of the approach will facilitate the establishment of an online structural monitoring system to ensure the safety of offshore structures. [ABSTRACT FROM AUTHOR]

Our generation inherits this cultural heritage of historic material and historic reinforced concrete structures and thus bears a certain responsibility to preserve these historic buildings with the help of the new technologies of lifetime management, conservation concepts and the new digitalization as well as the emerging safety concepts of our time. The aim of this article is to analyze the new technologies of data‐based service life management in relation to the assessment of historic reinforced concrete structures and to test the applicability of these methods to these historic buildings. [ABSTRACT FROM AUTHOR]

Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques. [ABSTRACT FROM AUTHOR]

In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing computation cycles and energy consumption. We performed an analysis to devise key hyper-parameters, and then we show the results of tuning such parameters to obtain a low-latency and low-energy embedded LSNN (eLSNN) implementation. We demonstrate that it is possible to adapt the firing rate so that the samples belonging to the most frequent class are processed with less spikes. We implemented the eLSNN on a microcontroller-based sensor node and we evaluated its performance and energy consumption using a structural health monitoring application processing a stream of vibrations for damage detection (i.e. binary classification). We obtained a cycle count reduction of 25% and an energy reduction of 22% with respect to a baseline implementation. We also demonstrate that our methodology is applicable to a multi-class scenario, showing that we can reduce spiking activity between 68 and 85% at iso-accuracy. [ABSTRACT FROM AUTHOR]

Preserving the structural integrity of critical infrastructure systems necessitates a heightened focus on fortifying the protection of underground pipelines. To this end, this paper presents an innovative approach, namely the Multi-Sample Joint Localization Method (MSJLM) utilizing Cross-Correlation Convolutional Neural Networks (CC-CNN), aimed at precisely localizing intrusion sources in the vicinity of underground pipelines. Traditional techniques for detecting and pinpointing pipeline intrusions primarily rely on a single sensor monitoring point, which is susceptible to inherent errors and constraints. In contrast, the MSJLM proposed in this study leverages data from multiple samples, integrating diverse data sources through correlation analyses to elevate precision and reliability. The utilization of the CC-CNN framework for processing aggregated data has proven highly successful in extracting spatial features and identifying patterns. Furthermore, the effectiveness of this method is corroborated through validation via a pile-driving model test. [ABSTRACT FROM AUTHOR]

The fundamental shear horizontal (SH0) guided wave transducer shows unique promise for non-destructive testing and structural health monitoring in oil pipelines, railway tracks, solar power plants, and beyond due to its non-dispersive characteristics. Under this background, it is desirable to explore the high-performance piezoelectric crystals to develop the SH0 guided wave transducers. Herein, a novel piezoelectric crystal KCsMoP2O9 (KCMP) with dominant face shear mode d14 was grown by Kyropoulos method and proposed to excite and receive the SH0 wave based on the finite element simulation. The resonance frequency was found to be 190 kHz with the designed size of 6 mm × 6 mm × 1.5 mm. The group velocity of generated wave packet was determined to be 3040 m/s, affirming that the detected signal was the SH0 wave. The simulated and experimental results demonstrated the exceptional ability of KCMP-based guided wave transducer to generate and detect obvious SH0 waves in two orthogonal principal directions over a wide frequency range (160–360 kHz). Additionally, the KCMP-based SH0 wave transducer showcases its excellent defect localization ability with high signal-to-noise ratio (~ 30 dB), demonstrating its great potential for application in non-destructive testing and structural health monitoring. [ABSTRACT FROM AUTHOR]

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions. [ABSTRACT FROM AUTHOR]

The acquisition of bridge frequency by using the ubiquitous ordinary passenger vehicles and smartphones in cities has attracted an increasing amount of attention. This study proposes a bridge monitoring method and develops a vehicle–bridge crowdsourcing monitoring (VBCM) platform for public participation. It takes smartphones carried by vehicles as monitoring terminals for a long-term bridge monitoring task. To validate the feasibility of available smartphone brands as a signal collector, a small shake table test is carried out and the processing scheme for the smartphone sampling data is investigated. A smartphone sensor frequency gain technique is proposed to satisfy the fusion of multisensor data in different smartphones. Furthermore, a magnetic target identification technique is proposed to pick up and extract the bridge response signal segment corresponding to the vehicle’s entering and leaving, as well as to eliminate redundant data. To this end, the network of multiple smartphones is synchronized. For the sake of applicability and feasibility, a series of scaled experiments are conducted in the laboratory considering an adapted toy car driving over a simply supported steel bridge. The results demonstrated the practicality of the proposed methodology. The combination of easily accessible smartphones oriented to a crowdsourcing model and a driving vehicle lowers the threshold for the bridge monitoring process, which also pinpoints a potential for future structural health monitoring development. [ABSTRACT FROM AUTHOR]

Long-period ground motions pose potential risks to the safety and serviceability of supertall buildings, which was not paid sufficient attention in the past of structural seismic design. To address this issue, the comparative study presented in this paper systematically investigates the dynamic responses of a 600-m-high supertall building subjected to natural hazards including long-period ground motions, ordinary ground motions and a super typhoon with the same return periods of 50 years. The seismic responses and wind-induced responses of the supertall building are obtained based on the building’s finite element model under earthquake excitation records and structural health monitoring system installed in the skyscraper, respectively. The long-period ground motions cause larger dynamic responses of the supertall building than those resulted by the ordinary ground motions or the super typhoon. This combined study of numerical analysis and field measurement provides valuable insights into the dynamic responses of a supertall building under different natural hazards, especially the long-period ground motions. The findings are expected to be of interest and practical use to the design of supertall buildings. [ABSTRACT FROM AUTHOR]

An accurate finite element model (FEM) plays a critical role in the structural damage identification. However, due to the existence of the uncertainties, such as material properties and modeling errors, it always exists some gaps between the analytical FEM and experimental structure. While an artificial neural network (ANN)-based model updating methods have been widely adopted to narrow the gap and obtain a baseline FEM, it still faces inaccurate results and fails to meet the physical law. In this regard, the study proposes a novel physics-based loss function inspired by modal sensitivity analysis and incorporates it into the residual neural network, thereby forming a novel physics-guided neural network (PGNN) method. The mapping relationship between the input of structural responses and output of model updating variables is constrained to retain its physical meaning by guiding the training process instead of pure data association, which aims to improve the accuracy of the ANN-based method and achieve accurate and high-efficiency model updating. An experimental example of a continuous rigid frame bridge is adopted to verify the feasibility of the proposed method. Additionally, other common model updating methods, including moth-flame optimization and regularization method, are used to make a comparison. The noise-robustness of the proposed method is investigated as well. Compared to the existing method, the results illustrate that the proposed PGNN method can achieve better model updating and good noise-robustness under high uncertainties, which means the introduction of the physics-based loss function significantly enhances the parameters updating ability of the neural network. The proposed method exhibits high efficiency and promising potential for large-scale bridge structure model updating. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is an excellent tool to solve certain engineering problems connected to material science (such as sulfate swelling, internal degradation due to freezing, and alkali silicate swelling) and to understand specific processes (frost peeling, acid action). Albeit borne and mostly used in the medical domain, CT is increasingly used in the examination of the internal structure of building materials, where degradation processes occur to the detriment of their mechanical performance and durability. This paper presents five engineering problems concerning concrete freezing and thawing, concrete at high temperatures, timber charring, spalling in asbestos‐cement pipes, and deterioration in prestressed concrete pipes due to the corrosion of metallic inserts. In each case, the degradation processes are monitored via CT, something that may be crucial in the renovation and preservation of historical structures. [ABSTRACT FROM AUTHOR]

Bolt loosening detection is crucial for ensuring the safe operation of equipment. This paper presents a vision-based real-time detection method that identifies bolt loosening by recognizing anti-loosening line markers at bolt connections. The method employs the YOLOv10-S deep learning model for high-precision, real-time bolt detection, followed by a two-step Fast-SCNN image segmentation technique. This approach effectively isolates the bolt and nut regions, enabling accurate extraction of the anti-loosening line markers. Key intersection points are calculated using ellipse and line fitting techniques, and the loosening angle is determined through spatial projection transformation. The experimental results demonstrate that, for high-resolution images of 2048 × 1024 pixels, the proposed method achieves an average angle detection error of 1.145° with a detection speed of 32 FPS. Compared to traditional methods and other vision-based approaches, this method offers non-contact measurement, real-time detection capabilities, reduced detection error, and general adaptability to various bolt types and configurations, indicating significant application potential. [ABSTRACT FROM AUTHOR]

Structural composite materials have gained significant appeal because of their ability to be customized for specific mechanical qualities for various applications, including avionics, wind turbines, transportation, and medical equipment. Therefore, there is a growing demand for effective and non-invasive structural health monitoring (SHM) devices to supervise the integrity of materials. This work introduces a novel sensor design, consisting of three spiral resonators optimized to operate at distinct frequencies and excited by a feeding strip line, capable of performing non-destructive structural strain monitoring via frequency coding. The initial discussion focuses on the analytical modeling of the sensor, which is based on a circuital approach. A numerical test case is developed to operate across the frequency range of 100 to 400 MHz, selected to achieve a balance between penetration depth and the sensitivity of the system. The encouraging findings from electromagnetic full-wave simulations have been confirmed by experimental measurements conducted on printed circuit board (PCB) prototypes embedded in a fiberglass-based composite sample. The sensor shows exceptional sensitivity and cost-effectiveness, and may be easily integrated into composite layers due to its minimal cabling requirements and extremely small profile. The particular frequency-coded configuration enables the suggested sensor to accurately detect and distinguish various structural deformations based on their severity and location. [ABSTRACT FROM AUTHOR]