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Recent Advances in Sensing and Data Centric Methods for Structural Health Monitoring and Resilience

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 28651

Special Issue Editors


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Guest Editor
Mechanical Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93401, USA
Interests: AI-based methods for structural health monitoring and dynamic response; random vibrations; hysteretic systems; seismic isolation; reliability and resilience
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, TO, Italy
Interests: disaster resilience; earthquake engineering; numerical simulations; special structures; structural control; structural monitoring; structural analysis, control and monitoring; structural and community resilience; structural degradation and damage detection; seismic risk; emergency and evacuation
Special Issues, Collections and Topics in MDPI journals
School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: reliability and safety in bridge engineering; bridge health monitoring; structural damage identification; machine learning methods in civil engineering; probabilistic digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, due to major advancements in sensing technology, diagnostics, data analytics (including various AI based methodologies) optimization and system identification significant developments have taken place in the field of structural health monitoring, intelligent systems and enhancing the resilience of engineering systems. This Special Issue aims to underscore the importance of latest developments in those areas and their collective impact on further progress in the fields of structural health monitoring and resilience of infrastructure, mechanical, aerospace and other engineering systems. Potential research contributions pertaining to this topic include but are not limited to the following areas:

  • New and novel innovations in structural health monitoring.
  • IoT and smart infrastructure.
  • SHM of historic and ageing structures.
  • AI-based methodologies, such as deep learning neural networks, big data, digital twins.
  • System identification.
  • Surrogate models.
  • Optimization techniques.
  • Probabilistic methods, such as uncertainty quantification, variability assessment, especially combined with AI methods.
  • Various machine learning tools.
  • Dynamic response prediction of highly nonlinear systems.
  • Feature extraction schemes.
  • Resilience of civil infrastructure in a life cycle.
  • Resiliency and recoverability of structures and isolation-structure systems.
  • Assessing the impact of SHM on urban infrastructure resilience.
  • Utilization of data analytics schemes in structural control and seismic isolation systems.
  • Data driven method for structural damage identification.
  • Reliability and safety of engineering structures.
  • Distributed sensors and Big Data in SHM application.
  • Damage identification (detection-localization-quantification-remaining life) to improve structural resilience.
  • Image processing methods for SHM.
  • Output-only methods for SHM.

Prof. Dr. Mohammad N Noori
Dr. Marco Domaneschi
Prof. Dr. Naiwei Lu
Guest Editors

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Published Papers (11 papers)

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Research

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26 pages, 11092 KiB  
Article
Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection
by Sardorbek Niyozov, Marco Domaneschi, Joan R. Casas and Rick M. Delgadillo
Sensors 2023, 23(11), 5187; https://doi.org/10.3390/s23115187 - 30 May 2023
Viewed by 1334
Abstract
Bridges are vital components of transport infrastructures, and therefore, it is of utmost importance that they operate safely and reliably. This paper proposes and tests a methodology for detecting and localizing damage in bridges under both traffic and environmental variability considering non-stationary vehicle-bridge [...] Read more.
Bridges are vital components of transport infrastructures, and therefore, it is of utmost importance that they operate safely and reliably. This paper proposes and tests a methodology for detecting and localizing damage in bridges under both traffic and environmental variability considering non-stationary vehicle-bridge interaction. In detail, the current study presents an approach to temperature removal in the case of forced vibrations in the bridge using principal component analysis, with detection and localization of damage using an unsupervised machine learning algorithm. Due to the difficulty in obtaining real data on undamaged and later damaged bridges that are simultaneously influenced by traffic and temperature changes, the proposed method is validated using a numerical bridge benchmark. The vertical acceleration response is derived from a time-history analysis with a moving load under different ambient temperatures. The results show how machine learning algorithms applied to bridge damage detection appear to be a promising technique to efficiently solve the problem’s complexity when both operational and environmental variability are included in the recorded data. However, the example application still shows some limitations, such as the use of a numerical bridge and not a real bridge due to the lack of vibration data under health and damage conditions, and with varying temperatures; the simple modeling of the vehicle as a moving load; and the crossing of only one vehicle present in the bridge. This will be considered in future studies. Full article
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23 pages, 11585 KiB  
Article
Stochastic Propagation of Fatigue Cracks in Welded Joints of Steel Bridge Decks under Simulated Traffic Loading
by Naiwei Lu, Jing Liu, Honghao Wang, Heping Yuan and Yuan Luo
Sensors 2023, 23(11), 5067; https://doi.org/10.3390/s23115067 - 25 May 2023
Cited by 5 | Viewed by 1094
Abstract
The fatigue cracking of orthotropic steel bridge decks (OSDs) is a difficult problem that hinders the development of steel structures. The most important reasons for the occurrence of fatigue cracking are steadily growing traffic loads and unavoidable truck overloading. Stochastic traffic loading leads [...] Read more.
The fatigue cracking of orthotropic steel bridge decks (OSDs) is a difficult problem that hinders the development of steel structures. The most important reasons for the occurrence of fatigue cracking are steadily growing traffic loads and unavoidable truck overloading. Stochastic traffic loading leads to the random propagation behavior of fatigue cracks, which increases the difficulty of the fatigue life evaluations of OSDs. This study developed a computational framework for the fatigue crack propagation of OSDs under stochastic traffic loads based on traffic data and finite element methods. Stochastic traffic load models were established based on site-specific, weigh-in-motion measurements to simulate fatigue stress spectra of welded joints. The influence of the transverse loading positions of the wheel tracks on the stress intensity factor of the crack tip was investigated. The random propagation paths of the crack under stochastic traffic loads were evaluated. Both ascending and descending load spectra were considered in the traffic loading pattern. The numerical results indicated that the maximum value of KI was 568.18 (MPa·mm1/2) under the most critical transversal condition of the wheel load. However, the maximum value decreased by 66.4% under the condition of transversal moving by 450 mm. In addition, the propagation angle of the crack tip increased from 0.24° to 0.34°—an increase ratio of 42%. Under the three stochastic load spectra and the simulated wheel loading distributions, the crack propagation range was almost limited to within 10 mm. The migration effect was the most obvious under the descending load spectrum. The research results of this study can provide theoretical and technical support for the fatigue and fatigue reliability evaluation of existing steel bridge decks. Full article
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15 pages, 6860 KiB  
Article
Probabilistic Seismic Response Prediction of Three-Dimensional Structures Based on Bayesian Convolutional Neural Network
by Tianyu Wang, Huile Li, Mohammad Noori, Ramin Ghiasi, Sin-Chi Kuok and Wael A. Altabey
Sensors 2022, 22(10), 3775; https://doi.org/10.3390/s22103775 - 16 May 2022
Cited by 16 | Viewed by 2530
Abstract
Seismic response prediction is a challenging problem and is significant in every stage during a structure’s life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural network with deterministic parameters is [...] Read more.
Seismic response prediction is a challenging problem and is significant in every stage during a structure’s life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural network with deterministic parameters is unable to predict the random dynamic response of structures. In this paper, a deep Bayesian convolutional neural network is proposed to predict seismic response. The Bayes-backpropagation algorithm is applied to train the proposed Bayesian deep learning model. A numerical example of a three-dimensional building structure is utilized to validate the performance of the proposed model. The result shows that both acceleration and displacement responses can be predicted with a high level of accuracy by using the proposed method. The main statistical indices of prediction results agree closely with the results from finite element analysis. Furthermore, the influence of random parameters and the robustness of the proposed model are discussed. Full article
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17 pages, 4095 KiB  
Article
An Unsupervised Tunnel Damage Identification Method Based on Convolutional Variational Auto-Encoder and Wavelet Packet Analysis
by Yonglai Zhang, Xiongyao Xie, Hongqiao Li and Biao Zhou
Sensors 2022, 22(6), 2412; https://doi.org/10.3390/s22062412 - 21 Mar 2022
Cited by 13 | Viewed by 2277
Abstract
Finding a low-cost and highly efficient method for identifying subway tunnel damage can greatly reduce catastrophic accidents. At present, tunnel health monitoring is mainly based on the observation of apparent diseases and vibration monitoring, which is combined with a manual inspection to perceive [...] Read more.
Finding a low-cost and highly efficient method for identifying subway tunnel damage can greatly reduce catastrophic accidents. At present, tunnel health monitoring is mainly based on the observation of apparent diseases and vibration monitoring, which is combined with a manual inspection to perceive the tunnel health status. However, these methods have disadvantages such as high cost, short working time, and low identification efficiency. Thus, in this study, a tunnel damage identification algorithm based on the vibration response of in-service train and WPE-CVAE is proposed, which can automatically identify tunnel damage and give the damage location. The method is an unsupervised novelty detection that requires only sufficient normal data on healthy structure for training. This study introduces the theory and implementation process of this method in detail. Through laboratory model tests, the damage of the void behind the tunnel wall is designed to verify the performance of the algorithm. In the test case, the proposed method achieves the damage identification performance with a 96.25% recall rate, 86.75% hit rate, and 91.5% accuracy. Furthermore, compared with the other unsupervised methods, the method performance and noise immunity are better than others, so it has a certain practical value. Full article
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26 pages, 24052 KiB  
Article
Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements
by Farid Ghahari, Niloofar Malekghaini, Hamed Ebrahimian and Ertugrul Taciroglu
Sensors 2022, 22(3), 1278; https://doi.org/10.3390/s22031278 - 08 Feb 2022
Cited by 11 | Viewed by 3616
Abstract
Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation [...] Read more.
Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism. Full article
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24 pages, 5663 KiB  
Article
Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification
by Pooya Mohebian, Seyed Bahram Beheshti Aval, Mohammad Noori, Naiwei Lu and Wael A. Altabey
Sensors 2022, 22(3), 1275; https://doi.org/10.3390/s22031275 - 08 Feb 2022
Cited by 20 | Viewed by 2041
Abstract
Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series [...] Read more.
Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures. Full article
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22 pages, 650 KiB  
Article
OBET: On-the-Fly Byte-Level Error Tracking for Correcting and Detecting Faults in Unreliable DRAM Systems
by Duy-Thanh Nguyen, Nhut-Minh Ho, Weng-Fai Wong and Ik-Joon Chang
Sensors 2021, 21(24), 8271; https://doi.org/10.3390/s21248271 - 10 Dec 2021
Cited by 1 | Viewed by 3109
Abstract
With technology scaling, maintaining the reliability of dynamic random-access memory (DRAM) has become more challenging. Therefore, on-die error correction codes have been introduced to accommodate reliability issues in DDR5. However, the current solution still suffers from high overhead when a large DRAM capacity [...] Read more.
With technology scaling, maintaining the reliability of dynamic random-access memory (DRAM) has become more challenging. Therefore, on-die error correction codes have been introduced to accommodate reliability issues in DDR5. However, the current solution still suffers from high overhead when a large DRAM capacity is used to deliver high performance. We present a DRAM chip architecture that can track faults at byte-level DRAM cell errors to address this problem. DRAM faults are classified as temporary or permanent in our proposed architecture, with no additional pins and with minor DRAM chip modifications. Hence, we achieve reliability comparable to that of other state-of-the-art solutions while incurring negligible performance and energy overhead. Furthermore, the faulty locations are efficiently exposed to the operating system (OS). Thus, we can significantly reduce the required scrubbing cycle by scrubbing only faulty DRAM pages while reducing the system failure probability up to 5000∼7000 times relative to conventional operation. Full article
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17 pages, 7140 KiB  
Article
Fast γ Photon Imaging for Inner Surface Defects Detecting
by Min Yao, Guangdong Luo, Min Zhao, Ruipeng Guo and Jian Liu
Sensors 2021, 21(23), 8134; https://doi.org/10.3390/s21238134 - 05 Dec 2021
Viewed by 1809
Abstract
Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner [...] Read more.
Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments. Full article
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29 pages, 9102 KiB  
Article
Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis
by Atik Faysal, Wai Keng Ngui, Meng Hee Lim and Mohd Salman Leong
Sensors 2021, 21(23), 8114; https://doi.org/10.3390/s21238114 - 04 Dec 2021
Cited by 11 | Viewed by 2305
Abstract
Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition [...] Read more.
Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis. Full article
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11 pages, 6122 KiB  
Article
Experimental Study on Vibration and Noise Characteristics of Steel-Concrete Railway Bridge
by Lucjan Janas
Sensors 2021, 21(23), 7964; https://doi.org/10.3390/s21237964 - 29 Nov 2021
Cited by 3 | Viewed by 1609
Abstract
The paper presents the results of vibroacoustic tests of a plate girder railway bridge consisting of two parallel dilated structures and a common ballast trough. The requirements currently set for railway bridges relate to, among others, vibrations considered as one of the criteria [...] Read more.
The paper presents the results of vibroacoustic tests of a plate girder railway bridge consisting of two parallel dilated structures and a common ballast trough. The requirements currently set for railway bridges relate to, among others, vibrations considered as one of the criteria for traffic safety and to noise emissions that may pose a threat to the environment. In this article, the results of tests conducted on vibrations of elements of the analyzed structure are presented, and the level of these vibrations in terms of meeting the requirements of the European standards is assessed. Vibrating criteria of structure performance were checked, and safety was assessed. The results of noise measurements in the vicinity of the analyzed bridge are also presented, and the environmental impact of this structure is determined. The test results show that the bridge meets the requirements for vibration acceleration and noise. An increased acoustic emission in the analyzed case does not pose a significant threat, but if this type of structure was on high supports in an urbanized area, it would be a nuisance to the environment. Full article
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Review

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40 pages, 6479 KiB  
Review
Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review
by Kareem Eltouny, Mohamed Gomaa and Xiao Liang
Sensors 2023, 23(6), 3290; https://doi.org/10.3390/s23063290 - 20 Mar 2023
Cited by 22 | Viewed by 5740
Abstract
Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the [...] Read more.
Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods. Full article
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