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Artificial Intelligence (AI) in Structural Health Monitoring
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Artificial Intelligence (AI) in Structural Health Monitoring

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Green Building".

Deadline for manuscript submissions: closed (2 September 2023) | Viewed by 7466

Special Issue Editors

Dr. Hanwei Zhao

E-Mail Website
Guest Editor
School of Civil Engineering/Structural Health Monitoring Institute, Southeast University, Nanjing 210096, China
Interests: structural health monitoring; big data mining and analytics; artificial intelligence in structural engineering; machine learning; digital twin; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yiming Zhang

E-Mail Website
Guest Editor
Department of Engineering, University of Cambridge, Cambridge CB2 1TN, UK
Interests: structural health monitoring; Bayesian dynamic model; probabilistic machine learning; uncertainty quantification
Dr. Pu Ren

E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Interests: scientific machine learning; dynamical systems; structural health monitoring
Prof. Dr. Youliang Ding

E-Mail Website
Guest Editor
School of Civil Engineering, Southeast University, Nanjing 210096, China
Interests: structural health monitoring; artificial intelligence in structural engineering; machine learning

Special Issue Information

Dear Colleagues,

Structural health monitoring technology has gained prominence in the state monitoring, damage detection and health management of engineering facilities (bridges, buildings, tunnels, pipe galleries, dams, mechanical systems, aircrafts, spacecrafts) since it was proposed in the 1980s. With the rapid development of artificial intelligence (AI), computer vision and big data analysis theory and technology, the time required for the processing, transmission and analysis of massive monitoring data of engineering facilities, in the multiple modalities of mechanics, thermotics, acoustics, optics, electricity and magnetism, has been greatly reduced. In-service state assessment and risk early warning for the systems of engineering facilities and their environments can be intelligently conducted based on online monitoring data.

Engineering facilities and their natural and social environments form a symbiosis. The behaviors of engineering facilities and the humans within them influence the changes in the surrounding environment, while the abnormality and deterioration of the surrounding environment will adversely influence the in-service state of the engineering facilities and the well-being of the humans within them. Based on massive monitoring data with multiple sources and multiple modalities, intelligent digital twin models that reflect the engineering facilities and surrounding environments can be synchronously established to realize the holographic risk perception and the early warning of dangers for the system of engineering facilities and surrounding environments. With the help of the most advanced AI technologies, bottleneck problems (e.g., data fusion, feature extraction, and nonlinear mapping modeling) in the core links of the operation and maintenance of engineering facilities can be overcome. The development and implementation of these technologies could help to reduce energy consumption and maintenance costs of engineering facilities during operation, and to extend the service life of engineering facilities. Furthermore, these tools can support the achievement of carbon neutrality targets.

These challenges demand innovative research and new engineering applications of AI in structural health monitoring. This Special Issue therefore seeks original research and review articles on recent advances, technologies, solutions, and applications of AI in structural health monitoring. Research areas may include (but are not limited to) the following:

  • Sensing by mechanics, thermotics, acoustics, optics, electricity or magnetism modalities;
  • Machine learning in structural health monitoring;
  • Computer vision in structural health monitoring;
  • Data cleaning and feature extraction in structural health monitoring;
  • Big data mining and analytics in structural health monitoring;
  • Advanced monitoring for bridges, buildings, tunnels, pipe galleries, dams, mechanical systems, aircrafts, and spacecrafts;
  • Environmental monitoring for engineering facilities;
  • Digital twins for engineering facilities;
  • Intelligent operation and maintenance for engineering facilities;
  • Life-cycle assessment for engineering facilities.

We look forward to receiving your contributions.

Dr. Hanwei Zhao
Dr. Yiming Zhang
Dr. Pu Ren
Prof. Dr. Youliang Ding
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring
  • artificial intelligence
  • multimodal sensing
  • machine learning
  • computer vision
  • data mining
  • digital twin
  • intelligent operation and maintenance of infrastructures
  • environmental monitoring

Published Papers (6 papers)

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Research

31 pages, 5852 KiB  
Article
Adaptive Pathways Using Emerging Technologies: Applications for Critical Transportation Infrastructure
by Nisrine Makhoul, Dimitra V. Achillopoulou, Nikoleta K. Stamataki and Rolands Kromanis
Sustainability 2023, 15(23), 16154; https://doi.org/10.3390/su152316154 - 21 Nov 2023
Cited by 2 | Viewed by 975
Abstract
Hazards are becoming more frequent and disturbing the built environment; this issue underpins the emergence of resilience-based engineering. Adaptive pathways (APs) were recently introduced to help flexible and dynamic decision making and adaptive management. Especially under the climate change challenge, APs can account [...] Read more.
Hazards are becoming more frequent and disturbing the built environment; this issue underpins the emergence of resilience-based engineering. Adaptive pathways (APs) were recently introduced to help flexible and dynamic decision making and adaptive management. Especially under the climate change challenge, APs can account for stressors occurring incrementally or cumulatively and for amplified-hazard scenarios. Continuous records from structural health monitoring (SHM) paired with emerging technologies such as machine learning and artificial intelligence can increase the reliability of measurements and predictions. Thus, emerging technologies can play a crucial role in developing APs through the lifetimes of critical infrastructure. This article contributes to the state of the art by the following four ameliorations. First, the APs are applied to the critical transportation infrastructure (CTI) for the first time. Second, an enhanced and smart AP framework for CTI is proposed; this benefits from the resilience and sustainability of emerging technologies to reduce uncertainties. Third, this innovative framework is assisted by continuous infrastructure performance assessment, which relies on continuous monitoring and mitigation measures that are implemented when needed. Next, it explores the impact of emerging technologies on structural health monitoring (SHM) and their role in enhancing resilience and adaptation by providing updated information. It also demonstrates the flexibility of monitoring systems in evolving conditions and the employment of AI techniques to manage pathways. Finally, the framework is applied to the Hollandse bridge, considering climate-change risks. The study delves into the performance, mitigation measures, and lessons learned during the life cycle of the asset. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Structural Health Monitoring)
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20 pages, 4900 KiB  
Article
Vector Quantized Variational Autoencoder-Based Compressive Sampling Method for Time Series in Structural Health Monitoring
by Ge Liang, Zhenglin Ji, Qunhong Zhong, Yong Huang and Kun Han
Sustainability 2023, 15(20), 14868; https://doi.org/10.3390/su152014868 - 13 Oct 2023
Cited by 1 | Viewed by 874
Abstract
The theory of compressive sampling (CS) has revolutionized data compression technology by capitalizing on the inherent sparsity of a signal to enable signal recovery from significantly far fewer samples than what is required by the Nyquist–Shannon sampling theorem. Recent advancement in deep generative [...] Read more.
The theory of compressive sampling (CS) has revolutionized data compression technology by capitalizing on the inherent sparsity of a signal to enable signal recovery from significantly far fewer samples than what is required by the Nyquist–Shannon sampling theorem. Recent advancement in deep generative models, which can represent high-dimension data in a low-dimension latent space efficiently when trained with big data, has been used to further reduce the sample size for image data compressive sampling. However, compressive sampling for 1D time series data has not significantly benefited from this technological progress. In this study, we investigate the application of different architectures of deep neural networks suitable for time series data compression and propose an efficient method to solve the compressive sampling problem on one-dimensional (1D) structural health monitoring (SHM) data, based on block CS and the vector quantized–variational autoencoder model with a naïve multitask paradigm (VQ-VAE-M). The proposed method utilizes VQ-VAE-M to learn the data characteristics of the signal, replaces the “hard constraint” of sparsity to realize the compressive sampling signal reconstruction and thereby does not need to select the appropriate sparse basis for the signal. A comparative analysis against various CS methods and other deep neural network models was performed in both synthetic data and real-world data from two real bridges in China. The results have demonstrated the superiority of the proposed method, with achieving the smallest reconstruction error of 0.038, 0.034 and 0.021, and the highest reconstruction accuracy of 0.882, 0.892 and 0.936 for compression ratios of 4.0, 2.66, and 2.0, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Structural Health Monitoring)
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25 pages, 11183 KiB  
Article
Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM
by Zili Xia, Junxiao Guo, Zixiang Yue, Youliang Ding, Zhiwen Wang and Shouwang Sun
Sustainability 2023, 15(12), 9623; https://doi.org/10.3390/su15129623 - 15 Jun 2023
Viewed by 896
Abstract
Based on big data, we can build a regression model between a temperature field and a temperature-induced deflection to provide a control group representing the service performance of bridges, which has a positive effect on the full life cycle maintenance of bridges. However, [...] Read more.
Based on big data, we can build a regression model between a temperature field and a temperature-induced deflection to provide a control group representing the service performance of bridges, which has a positive effect on the full life cycle maintenance of bridges. However, the spatial temperature information of a cable-stayed bridge is difficult to describe. To establish a regression model with high precision, the improved PCA-LGBM (principal component analysis and light gradient boosting machine) algorithm is proposed to extract the main temperature features that can reflect the spatial temperature information as accurately and efficiently as possible. Then, in this article, we searched for a suitable digital tool for modeling the regressive relationship between the temperature variables and the temperature-induced deflection of a cable-stayed bridge. The multiple linear regression model has relatively low precision. The precision of the backpropagation neural network (BPNN) model has been improved, but it is still unsatisfactory. The nested long short-term memory (NLSTM) model improves the nonlinear expression ability of the regression model and is more precise than BPNN models and the classical LSTM. The architecture of the NLSTM network is optimized for high precision and to avoid the waste of computational costs. Based on the four main temperature features extracted via the PCA-LGBM, the NLSTM network with double hidden layers and 256 hidden units in each hidden layer has much higher precision than the other regression models. For the NLSTM regression model of the temperature-induced deflection of a cable-stayed bridge, the mean absolute error is only 4.76 mm, and the mean square error is only 18.57 mm2. The control value of the NLSTM regression model is precise and thus provides the potential for early detection of bridge anomalies. This article can provide reference processes and a data extraction algorithm for deflection modeling of other cable-stayed bridges. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Structural Health Monitoring)
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22 pages, 13090 KiB  
Article
Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring
by Guoqiang Zhong, Yufeng Bi, Jie Song, Kangdi Wang, Shuai Gao, Xiaonan Zhang, Chao Wang, Shang Liu, Zixiang Yue and Chunfeng Wan
Sustainability 2023, 15(11), 9028; https://doi.org/10.3390/su15119028 - 2 Jun 2023
Viewed by 945
Abstract
A health monitoring system typically collects and processes data to observe the health status of a bridge. The cost limitations imply that only the measurement point data of a few key points can be obtained; however, the entire bridge monitoring information cannot be [...] Read more.
A health monitoring system typically collects and processes data to observe the health status of a bridge. The cost limitations imply that only the measurement point data of a few key points can be obtained; however, the entire bridge monitoring information cannot be established, which significantly interferes with the data integrity of the structural monitoring system. In this study, a solution is proposed for reconstructing the monitoring data of the entire bridge. By updating the finite element (FE) model based on structural thermal analysis, numerical simulation technology, and other methods, the temperature field integration model of a cable-stayed bridge is realized. The temperature spatial expansion method of deep learning is established by using the complete simulated temperature field of the entire bridge based on limited measured temperature data; this data is verified and applied to the main beam and bridge tower, thereby establishing the complete measured temperature field of the whole bridge. This is of great significance in supplementing health monitoring information, allowing for the accurate monitoring and evaluation of the structural safety and service performance of long bridges. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Structural Health Monitoring)
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14 pages, 7585 KiB  
Article
Real-Time Intelligent Prediction Method of Cable’s Fundamental Frequency for Intelligent Maintenance of Cable-Stayed Bridges
by Yong-Qiang Li, Han-Wei Zhao, Zi-Xiang Yue, Yi-Wei Li, Yan Zhang and Da-Cheng Zhao
Sustainability 2023, 15(5), 4086; https://doi.org/10.3390/su15054086 - 23 Feb 2023
Cited by 4 | Viewed by 1486
Abstract
Cable’s fundamental frequency (CFF) is an important characteristic of the working state of long-span cable-stayed bridges. The change in the bridge’s temperature field will influence CFF by altering the cable’s tension and the cables’ sags. An accurate regression model between the temperature-induced variation [...] Read more.
Cable’s fundamental frequency (CFF) is an important characteristic of the working state of long-span cable-stayed bridges. The change in the bridge’s temperature field will influence CFF by altering the cable’s tension and the cables’ sags. An accurate regression model between the temperature-induced variation of CFF and the real-time changing temperature field should be established. Then, the reference value of the temperature-induced variation of CFF can be obtained after inputting the real-time temperature data. In this study, an intelligent real-time prediction model for CFF is proposed based on the full-bridge temperature field, including the average temperature of the main beam, the vertical temperature difference of the main beam, and the temperature of the cable tower. Besides, a machine learning method named the long short-term memory (LSTM) network is exploited to ensure the nonlinear fitting performance of the model, and a paradigm for optimal hyperparameter selection and training strategy selection is provided. To verify the superiority of the LSTM-based model, the output accuracy of the linear regression, BP network, and LSTM network was tested and compared using the monitoring data collected from cable sensors in the main span and side span, which provides an important basis for the intelligent maintenance and sustainable operation of the bridge cables. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Structural Health Monitoring)
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15 pages, 8593 KiB  
Article
Missing Structural Health Monitoring Data Recovery Based on Bayesian Matrix Factorization
by Shouwang Sun, Sheng Jiao, Qi Hu, Zhiwen Wang, Zili Xia, Youliang Ding and Letian Yi
Sustainability 2023, 15(4), 2951; https://doi.org/10.3390/su15042951 - 6 Feb 2023
Cited by 1 | Viewed by 1271
Abstract
The exposure of bridge health-monitoring systems to extreme conditions often results in missing data, which constrains the health monitoring system from working. Therefore, there is an urgent need for an efficient data cleaning method. With the development of big data and machine-learning techniques, [...] Read more.
The exposure of bridge health-monitoring systems to extreme conditions often results in missing data, which constrains the health monitoring system from working. Therefore, there is an urgent need for an efficient data cleaning method. With the development of big data and machine-learning techniques, several methods for missing-data recovery have emerged. However, optimization-based methods may experience overfitting and demand extensive tuning of parameters, and trained models may still have substantial errors when applied to unseen datasets. Furthermore, many methods can only process monitoring data from a single sensor at a time, so the spatiotemporal dependence among monitoring data from different sensors cannot be extracted to recover missing data. Monitoring data from multiple sensors can be organized in the form of matrix. Therefore, matrix factorization is an appropriate way to handle monitoring data. To this end, a hierarchical probabilistic model for matrix factorization is formulated under a fully Bayesian framework by incorporating a sparsity-inducing prior over spatiotemporal factors. The spatiotemporal dependence is modeled to reconstruct the monitoring data matrix to achieve the missing-data recovery. Through experiments using continuous monitoring data of an in-service bridge, the proposed method shows good performance of missing-data recovery. Furthermore, the effect of missing data on the preset rank of matrix is also investigated. The results show that the model can achieve higher accuracy of missing-data recovery with higher preset rank under the same case of missing data. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Structural Health Monitoring)
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