Showing papers on "Structural health monitoring published in 2022"
TL;DR: An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.
89 citations
TL;DR: In this paper , the authors summarized the applications of the wireless IoT technology in the monitoring of civil engineering infrastructure and discussed several case studies on real structures and laboratory investigations for monitoring the structural health of real-world constructions.
Abstract: Structural health monitoring (SHM) and damage assessment of civil engineering infrastructure are complex tasks. Structural health and strength of structures are influenced by various factors, such as the material production stage, transportation, placement, workmanship, formwork removal, and concrete curing. Technological advancements and the widespread availability of Wi-Fi networks has resulted in SHM shifting from traditional wire-based methods to Internet of Things (IoT)-based real-time wireless sensors. Comprehensive structural health assessment can be performed through the efficient use of real-time test data on structures obtained from various types of IoT sensors, which monitor several health parameters of structures, available on cloud-based data storage systems. The sensor data may be subsequently used for various applications, such as forecasting masonry construction deterioration, predicting the early-stage compressive strength of concrete, forecasting the optimum time for the removal of formwork, vibration and curing quality control, crack detection in buildings, pothole detection on roads, determination of the construction quality, corrosion diagnosis, identification of various damage typologies and seismic vulnerability assessment. This review paper summarizes the applications of the wireless IoT technology in the monitoring of civil engineering infrastructure. In addition, several case studies on real structures and laboratory investigations for monitoring the structural health of civil engineering constructions are discussed.
76 citations
TL;DR: The review primarily focuses on the recently used wireless data acquisition system and execution of AI resources for data prediction and data diagnosis in RCC buildings and bridges and indicates the lag in real-world execution of structural health monitoring technologies despite advances in academia.
72 citations
TL;DR: A comprehensive review of advances in data acquisition, processing, diagnosis, and retrieval stages of Structural Health Monitoring both academically and commercially is presented in this article , which primarily focuses on the recently used wireless data acquisition system and execution of AI resources for data prediction and data diagnosis in RCC buildings and bridges.
58 citations
TL;DR: In this article , a detailed review of data mining techniques for structural health monitoring (SHM) applications is presented, where a brief background, models, functions, and classification of DM techniques are presented.
46 citations
TL;DR: A comprehensive review of the recent progress that used signal processing techniques for vibration-based structural health monitoring (SHM) approaches is presented in this article , where the feature extraction process through the signal processing technique is the basic skeleton of this review.
42 citations
TL;DR: Wang et al. as discussed by the authors proposed an innovative data-driven method based on an improved variational mode decomposition (IVMD) and conditional kernel density estimation (CKDE), where the raw deformation data are firstly pretreated by IVMD, and then an auto-regression integrated moving average model is established to excavate the linear feature hidden in the data.
38 citations
TL;DR: In this paper , the authors present a survey of non-destructive approaches for wind turbine condition monitoring and structural health monitoring, which can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs).
Abstract: A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.
38 citations
TL;DR: The feasibility of structural health monitoring based on natural frequencies is investigated for a steel bowstring railway bridge in Leuven, Belgium, and a comparison is made between standard linear regression and robust principal component analysis (PCA), two black-box modeling techniques adopted to remove natural frequency variations resulting from changes in the environmental conditions.
38 citations
TL;DR: This study presents a Bayesian dynamic regression (BDR) method to reconstruct the missing SHM data and shows that the multivariate BDR model exhibits excellent performance to rebuild the missing data in terms of both computational efficiency and accuracy.
Abstract: Massive data that provide valuable information regarding the structural behavior are continuously collected by the structural health monitoring (SHM) system. The quality of monitoring data is directly related to the accuracy of the structural condition assessment and maintenance decisions. Data missing is a common and challenging issue in SHM, compromising the reliability of data-driven methods. Thus, the accurate reconstruction of missing SHM data is an essential step for the reliable evaluation of the structural condition. Data recovery can be considered as a regression task by modeling the correlation among sensors. The Bayesian linear regression (BLR) model has been extensively used in probabilistic regression analysis due to its efficiency and the ability of uncertainty quantification. However, because of the fixed coefficients (refer to a static model) and linear assumption, the BLR model fails to accurately capture the relationship and accommodate the changes in related variables. Given this limitation, this study presents a Bayesian dynamic regression (BDR) method to reconstruct the missing SHM data. The BDR model assumes that the linear form is only locally suitable, and the regression variable varies according to a random walk. In particular, the multivariate BDR model can reconstruct the missing data of different sensors simultaneously. The Kalman filter and expectation maximum (EM) algorithms are employed to estimate the state variables (regressors) and parameters. The feasibility of the multivariate BDR model is demonstrated by utilizing the data from a building model and a long-span cable-stayed bridge. The results show that the multivariate BDR model exhibits excellent performance to rebuild the missing data in terms of both computational efficiency and accuracy. Compared to the standard BLR and linear BDR models, the quadratic BDR model owns better reconstruction accuracy.
36 citations
TL;DR: In this paper, the authors model the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system, assuming that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system's modal characteristics.
TL;DR: In this article , the feasibility of structural health monitoring based on natural frequencies is investigated for a steel bowstring railway bridge in Leuven, Belgium, where the data used in the study are obtained from an ongoing long-term monitoring campaign on the railway bridge and include acceleration measurements on the bridge deck and the arches.
TL;DR: In this paper, a convolutional neural network based deep learning architecture is proposed to automatically extract the discrete damage features from the scalogram images and use them to classify damage-source regions in the composite panel.
Abstract: Structural health monitoring for lightweight complex composite structures is being investigated in this paper with a data-driven deep learning approach to facilitate automated learning of the map of transformed signal features to damage classes. Towards this, a series of acoustic emission (AE) based laboratory experiments have been carried out on a composite sample using a piezoelectric AE sensor network. The registered time-domain AE signals from the assigned sensor networks on the composite panel are processed with the continuous wavelet transform to extract time-frequency scalograms. A convolutional neural network based deep learning architecture is proposed to automatically extract the discrete damage features from the scalogram images and use them to classify damage-source regions in the composite panel. The proposed deep-learning approach has shown an effective damage monitoring potential with high training, validation and test accuracy for unseen datasets as well as for entirely new neighboring damage datasets. Further, the proposed network is trained, validated and tested only for the peak-signal data extracted from the raw AE data. The application of peak-signal scalogram data has shown a significant improvement in damage-source classification performance with high training, validation and test accuracy.
01 Oct 2022
TL;DR: In this article , a piezoelectric yarn sensor based on electrospinning and 2D braiding technology is proposed to monitor advanced 3D textile composites, which can generate a voltage of about 1 V and sustain long-term cycles at high frequency of 4 Hz.
Abstract: Real-time online damage monitoring is essential and critical to the safe service of the advanced fibers reinforced composites. This paper firstly reports a piezoelectric yarn sensor based on electrospinning and 2D braiding technology, to monitor advanced 3D textile composites, which can generate a voltage of about 1 V and sustain long-term cycles at high frequency of 4 Hz. The polyvinylidene fluoride (PVDF) piezoelectric yarn is embedded into 3D orthogonal composites to realize the online health monitoring of advanced 3D textile composites through the three-point bending test. Following the bending fatigue and modal tests, the PVDF piezoelectric yarn sensor proposed in this work enables long-term, low-frequency, high-frequency, and stable monitoring, thus showing good potential and wide application in damage monitoring as a piezoelectric sensor in composites.
TL;DR: In this article , the authors model the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system, assuming that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system's modal characteristics.
TL;DR: In this article , the authors discuss the current process and future trends of bridge monitoring focusing on the cutting-edge structural health monitoring (SHM) technologies, transmission and analytics methods of the sensing data, and prediction and early warning models.
TL;DR: A methodology that exploits the large bridge responses induced by train passage while rejecting the confounding influences of the environment in such a way that false positive detections are mitigated is proposed, and an adaptable confidence decision threshold is introduced that further improves damage detection over time.
TL;DR: In this article , the current state of the art about the guided waves (GW) based structural health monitoring of aerospace composite structures is reviewed, looking at the implementation of the methodologies proposed and assessed by the authors.
TL;DR: In this article , a remotely controlled monitoring system vibrates the PZT patches, acting as actuators by an amplified harmonic excitation voltage, and then transmits them wirelessly and in real time.
Abstract: The addition of short fibers in concrete mass offers a composite material with advanced properties, and fiber-reinforced concrete (FRC) is a promising alternative in civil engineering applications. Recently, structural health monitoring (SHM) and damage diagnosis of FRC has received increasing attention. In this work, the effectiveness of a wireless SHM system to detect damage due to cracking is addressed in FRC with synthetic fibers under compressive repeated load. In FRC structural members, cracking propagates in small and thin cracks due to the presence of the dispersed fibers and, therefore, the challenge of damage detection is increasing. An experimental investigation on standard 150 mm cubes made of FRC is applied at specific and loading levels where the cracks probably developed in the inner part of the specimens, whereas no visible cracks appeared on their surface. A network of small PZT patches, mounted to the surface of the FRC specimen, provides dual-sensing function. The remotely controlled monitoring system vibrates the PZT patches, acting as actuators by an amplified harmonic excitation voltage. Simultaneously, it monitors the signal of the same PZTs acting as sensors and, after processing the voltage frequency response of the PZTs, it transmits them wirelessly and in real time. FRC cracking due to repeated loading ad various compressive stress levels induces change in the mechanical impedance, causing a corresponding change on the signal of each PZT. The influence of the added synthetic fibers on the compressive behavior and the damage-detection procedure is examined and discussed. In addition, the effectiveness of the proposed damage-diagnosis approach for the prognosis of final cracking performance and failure is investigated. The objectives of the study also include the development of a reliable quantitative assessment of damage using the statistical index values at various points of PZT measurements.
TL;DR: In this article , a DT framework based on cloud computing and deep learning (DL) for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance, which consists of structural components, device measurements, and digital models formed by combining different submodels, including mathematical, finite element, and ML ones.
Abstract: Digital twin (DT) technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning (ML), 5G/6G, cloud computing, and Internet of Things, DT has been moving progressively from concept to practice. In this article, a DT framework based on cloud computing and deep learning (DL) for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different submodels, including mathematical, finite element, and ML ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using DL algorithms, with high accuracy of 92%.
TL;DR: A novel hybrid method is fully data-driven and extends the forecasting capabilities of existing time-domain and machine learning-based methods for fatigue prediction, paving the way towards the development of a preventive system that provides real-time safety and operational instructions and insights for structural health monitoring purposes.
TL;DR: In this article , the authors proposed a continuous online procedure for damage detection based on train-induced dynamic bridge responses, taking advantage of the large-magnitude loading for enhancing sensitivity to small-scale structural changes.
TL;DR: The results show that the CNN-based damage detection method using strain mode differences as the inputs has a high accuracy under different damage conditions, and has a relatively high damage quantification prediction accuracy, which provides a new research approach for the structural health monitoring system.
TL;DR: In this article , a hybrid architecture of a fully connected artificial neural network (ANN) and Gaussian process regression (GPR) is proposed to ensure enhanced predictive abilities and simultaneous uncertainty quantification (UQ) of the predicted TtF.
TL;DR: The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.
Abstract: We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.
TL;DR: The main goal of this article is to summarize the concept of SHM and point out key developments in research and applications of the SPR paradigm observed in bridges in the last three decades, including developments in sensing technology and data analysis, and to identify current and future trends to promote more coordinated and interdisciplinary research in the SHM of bridges.
Abstract: Bridges play a crucial role in modern societies, regardless of their culture, geographical location, or economic development. The safest, economical, and most resilient bridges are those that are well managed and maintained. In the last three decades, structural health monitoring (SHM) has been a promising tool in management activities of bridges as potentially it permits one to perform condition assessment to reduce uncertainty in the planning and designing of maintenance activities as well as to increase the service performance and safety of operation. The general idea has been the transformation of massive data obtained from monitoring systems and numerical models into meaningful information. To deal with large amounts of data and perform the damage identification automatically, SHM has been cast in the context of the statistical pattern recognition (SPR) paradigm, where machine learning plays an important role. Meanwhile, recent technologies have unveiled alternative sensing opportunities and new perspectives to manage and observe the response of bridges, but it is widely recognized that bridge SHM is not yet fully capable of producing reliable global information on the presence of damage. While there have been multiple review studies published on SHM and vibration-based structural damage detection for wider scopes, there have not been so many reviews on SHM of bridges in the context of the SPR paradigm. Besides, some of those reviews become obsolete quite fast, and they are usually biased towards applications falling outside of bridge engineering. Therefore, the main goal of this article is to summarize the concept of SHM and point out key developments in research and applications of the SPR paradigm observed in bridges in the last three decades, including developments in sensing technology and data analysis, and to identify current and future trends to promote more coordinated and interdisciplinary research in the SHM of bridges.
TL;DR: Wang et al. as mentioned in this paper applied the convolutional neural network (CNN) training with input data contaminated by random noises in structural damage estimation, and the results show that the CNN-based damage detection method using strain mode differences as the inputs has a high accuracy under different damage conditions, i.e., the proposed method not only has a significant damage localization ability, but also has a relatively high damage quantification prediction accuracy.
TL;DR: In this article , a transfer learning technique realized by domain adaptation is used to bridge the gap between the biased numerical model and the real structure and to guide the model updating process so that the updated model can accurately indicate the damage state.
TL;DR: In this article, a methodology that applies pattern recognition methods to guide Bayesian model updating (BMU) and supervise the identification of structural damage is proposed, where the transfer learning technique realized by domain adaptation is used to bridge the gap between the biased numerical model and the real structure and to guide the model updating process.
TL;DR: A Bayesian source localisation strategy that is robust to complexities within composite materials and structures that contain non-trivial geometrical features and demonstrates a favourable performance in comparison to other similar localisation methods.