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Machine Learning for Structural Health Monitoring
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Machine Learning for Structural Health Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 8466

Special Issue Editors

Dr. Yang Xu

E-Mail Website
Guest Editor
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: structural health monitoring; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Shiyin Wei

E-Mail Website
Guest Editor
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: structural safety assessment; data-driven modeling; machine learning
Dr. Rongrong Hou

E-Mail Website
Guest Editor
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: structural damage identification; Bayesian uncertainty analysis; structural temperature analysis
Prof. Dr. Yong Huang

E-Mail Website
Guest Editor
School of Civil Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: structural health monitoring; machine learning; bayesian inference; system identification; signal processing

Special Issue Information

Dear Colleagues,

Recently, machine learning has brought a novel paradigm and a huge revolution in structural health monitoring, which is further enhanced by cutting-edge deep learning and computer vision techniques. With the vigorous development of various neural networks and supervised, unsupervised, semi-supervised, and self-supervised, and reinforcement learning algorithms, machine learning enables the autonomous discovery of embedded knowledge and the intelligent diagnosis of structural health based on monitoring data in a purely data-driven manner or a data-model-driven manner. This Special Issue aims to provide a platform to share current scientific and technical progress about ML for SHM.

Dr. Yang Xu
Dr. Shiyin Wei
Dr. Rongrong Hou
Dr. Yong Huang
Guest Editors

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Keywords

  • artificial intelligence for structural health monitoring
  • learning-based data science and technology of structural health monitoring
  • knowledge guided mechanics modeling, structural dynamics, and system identification
  • computer-vision-assisted structural damage recognition, change detection, and disaster evaluation
  • machine-learning-enhanced structural condition assessment and reliability analysis
  • deep-learning-based Bayesian model solving for complex structural uncertainty analysis

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Published Papers (4 papers)

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Research

18 pages, 8856 KiB  
Article
Investigation of Issues in Data Anomaly Detection Using Deep-Learning- and Rule-Based Classifications for Long-Term Vibration Measurements
by Imdad Ullah Khan, Seunghoo Jeong and Sung-Han Sim
Appl. Sci. 2024, 14(13), 5476; https://doi.org/10.3390/app14135476 - 24 Jun 2024
Cited by 1 | Viewed by 1131
Abstract
Structural health monitoring (SHM) systems are widely used for civil infrastructure monitoring. Data acquired from the SHM systems play an important role in assessing structural integrity and determining further maintenance activities. Considering that sensors in the SHM systems are installed in a harsh [...] Read more.
Structural health monitoring (SHM) systems are widely used for civil infrastructure monitoring. Data acquired from the SHM systems play an important role in assessing structural integrity and determining further maintenance activities. Considering that sensors in the SHM systems are installed in a harsh environment for long-term measurements, some sensors can malfunction and produce faulty data. As a large amount of measured data is often desired to be automatically processed and can adversely affect structural assessments, identifying such abnormal data is important. This paper provides critical investigations of the automated detection of data anomalies using existing deep-learning-based classification in conjunction with a simple rule-based approach. The issues investigated in this study include (1) the presence of ambiguous data that cannot be categorized as an anomaly class in the literature, (2) information loss during the conversion of time-series data into images for the deep-learning-based approach, and (3) additional issues, such as misclassification by trained models and requirements of the threshold section in the rule-based approach. The results of these key investigations can be utilized to develop an effective anomaly detection process. Full article
(This article belongs to the Special Issue Machine Learning for Structural Health Monitoring)
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18 pages, 15614 KiB  
Article
An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios
by Yue Wang, Qingjie Qi, Lifeng Sun, Wenhao Xian, Tianfang Ma, Changjia Lu and Jingwen Zhang
Appl. Sci. 2024, 14(7), 2909; https://doi.org/10.3390/app14072909 - 29 Mar 2024
Cited by 1 | Viewed by 1101
Abstract
With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development [...] Read more.
With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development and application of computer vision have provided modern methods for crack detection, which are low in cost, less labor-intensive, continuous, and timely. In this paper, an intelligent model based on a target detection algorithm in computer vision was proposed to accurately detect and classify four classes of cracks. Firstly, by vehicle-mounted camera capture, a dataset of pavement cracks with complicated backgrounds that are the most similar to actual scenarios was built, containing 4007 images and 7882 crack samples. Secondly, the YOLOv5 framework was improved from the four aspects of the detection layer, anchor box, neck structure, and cross-layer connection, and thereby the network’s feature extraction capability and small-sized-target detection performance were enhanced. Finally, the experimental results indicated that the proposed model attained an AP of the four classes of 81.75%, 83.81%, 98.20%, and 92.83%, respectively, and a mAP of 89.15%. In addition, the proposed model achieved a 2.20% missed detection rate, representing a 6.75% decrease over the original YOLOv5. These results demonstrated the effectiveness and practicality of our proposed model in addressing the issues of low accuracy and missed detection for small targets in the original network. Overall, the implementation of computer vision-based models in crack detection can promote the intellectualization of road maintenance. Full article
(This article belongs to the Special Issue Machine Learning for Structural Health Monitoring)
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18 pages, 13518 KiB  
Article
An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
by Jiaxing Guo, Zhiyi Tang, Changxing Zhang, Wei Xu and Yonghong Wu
Appl. Sci. 2023, 13(9), 5659; https://doi.org/10.3390/app13095659 - 4 May 2023
Cited by 1 | Viewed by 2467
Abstract
Structural health monitoring systems continuously monitor the operational state of structures, generating a large amount of monitoring data during the process. The structural responses of extreme events, such as earthquakes, ship collisions, or typhoons, could be captured and further analyzed. However, it is [...] Read more.
Structural health monitoring systems continuously monitor the operational state of structures, generating a large amount of monitoring data during the process. The structural responses of extreme events, such as earthquakes, ship collisions, or typhoons, could be captured and further analyzed. However, it is challenging to identify these extreme events due to the interference of faulty data. Real-world monitoring systems suffer from frequent misidentification and false alarms. Unfortunately, it is difficult to improve the system’s built-in algorithms, especially the deep neural networks, partly because the current neural networks only output results and do not provide an interpretable decision-making basis. In this study, a deep learning-based method with visual interpretability is proposed to identify seismic data under sensor faults interference. The transfer learning technique is employed to learn the features of seismic data and faulty data with efficiency. A post hoc interpretation algorithm, termed Gradient-weighted Class Activation Mapping (Grad-CAM), is embedded into the neural networks to uncover the interest regions that support the output decision. The in situ seismic responses of a cable-stayed long-span bridge are used for method verification. The results show that the proposed method can effectively identify seismic data mixed with various types of faulty data while providing good interpretability. Full article
(This article belongs to the Special Issue Machine Learning for Structural Health Monitoring)
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18 pages, 6190 KiB  
Article
A Robust Deep Learning-Based Damage Identification Approach for SHM Considering Missing Data
by Fan Deng, Xiaoming Tao, Pengxiang Wei and Shiyin Wei
Appl. Sci. 2023, 13(9), 5421; https://doi.org/10.3390/app13095421 - 26 Apr 2023
Cited by 8 | Viewed by 2250
Abstract
Data-driven methods have shown promising results in structural health monitoring (SHM) applications. However, most of these approaches rely on the ideal dataset assumption and do not account for missing data, which can significantly impact their real-world performance. Missing data is a frequently encountered [...] Read more.
Data-driven methods have shown promising results in structural health monitoring (SHM) applications. However, most of these approaches rely on the ideal dataset assumption and do not account for missing data, which can significantly impact their real-world performance. Missing data is a frequently encountered issue in time series data, which hinders standardized data mining and downstream tasks such as damage identification and condition assessment. While imputation approaches based on spatiotemporal relations among monitoring data have been proposed to handle this issue, they do not provide additional helpful information for downstream tasks. This paper proposes a robust deep learning-based method that unifies missing data imputation and damage identification tasks into a single framework. The proposed approach is based on a long short-term memory (LSTM) structured autoencoder (AE) framework, and missing data is simulated using the dropout mechanism by randomly dropping the input channels. Reconstruction errors serve as the loss function and damage indicator. The proposed method is validated using the quasi-static response (cable tension) of a cable-stayed bridge released in the 1st IPC-SHM, and results show that missing data imputation and damage identification can be effectively integrated into the proposed unified framework. Full article
(This article belongs to the Special Issue Machine Learning for Structural Health Monitoring)
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