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Structural Damage Detection and Quick Repair Assisted by AI Technologies

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 1 May 2024 | Viewed by 8889

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Special Issue Editors

Prof. Dr. Shaofei Jiang

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Guest Editor

College of Civil Engineering, Fuzhou University, Fuzhou, China
Interests: structural damage detection and rapid repair; green building and assembly industrialization; structural earthquake resistance; intelligent sensing and AI technologies

Prof. Dr. Feng-Liang Zhang

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Guest Editor

School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China
Interests: smart construction and smart city; structural health monitoring; AI technologies; big data; Bayesian theory

Dr. Yanchun Ni

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Guest Editor

College of Civil Engineering, Tongji University, Shanghai, China
Interests: structural health monitoring; bridge safety; Bayesian structural system; big data analysis

Special Issue Information

Dear Colleagues,

Damage to structures will appear gradually with increased usage duration. Detecting damage and carrying out repairs as early as possible is an important issue in structural health monitoring. Different methods have been developed to solve this problem in the past decade, and technological advancements are expected to make these methods more effective and accurate, such as AI technologies, which have provided a new solution for the above mentioned issues. This Special Issue aims to collect recent advances in structural damage detection and quick repair assisted by AI technologies. Topics of interest include, but are not limited to:

AI-assisted model updating;
AI-assisted modal identification;
AI-assisted damage detection;
AI-assisted quick repair;
AI-assisted sensor placement.

Prof. Dr. Shaofei Jiang
Prof. Dr. Feng-Liang Zhang
Dr. Yanchun Ni
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 damage
structural control
damage detection
health monitoring
quick repair
AI technologies

Published Papers (4 papers)

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Research

31 pages, 1942 KiB

Open AccessArticle

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

by Petros C. Lazaridis, Ioannis E. Kavvadias, Konstantinos Demertzis, Lazaros Iliadis and Lazaros K. Vasiliadis

Sustainability 2023, 15(17), 12768; https://doi.org/10.3390/su151712768 - 23 Aug 2023

Cited by 2 | Viewed by 1509

Abstract

Recently developed Machine Learning (ML) interpretability techniques have the potential to explain how predictors influence the dependent variable in high-dimensional and non-linear problems. This study investigates the application of the above methods to damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), permutation and impurity-based techniques. Following previous investigations that examine the interdependence between predictors and the cumulative damage caused by a seismic sequence using classic statistical methods, the present study deploy ML interpretation techniques to deal with this multi-parametric and complex problem. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution with respect to the range of final damage is evaluated. Non-linear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by the Park and Ang damage index (

D I_{P A}

) and Intensity Measures (IMs), respectively. In addition to the interpretability analysis, the study offers also a comprehensive review of ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined. Among the 17 examined predictors, the initial damage, caused by the first shock, and the IMs of the subsequent shock—

I_{F V F}

and

S I_{H}

—emerged as the most important ones. The novel results of this study provide useful insights in seismic design and assessment taking into account the structural performance under multiple moderate to strong earthquake events. Full article

(This article belongs to the Special Issue Structural Damage Detection and Quick Repair Assisted by AI Technologies)

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25 pages, 10438 KiB

Open AccessArticle

Scanning Scheme for Underwater High-Rise Pile Cap Foundation Based on Imaging Sonar

by Sheng Shen, Zheng Cao and Changqin Lai

Sustainability 2023, 15(8), 6402; https://doi.org/10.3390/su15086402 - 08 Apr 2023

Cited by 3 | Viewed by 1819

Abstract

This study developed a sonar scanning scheme for underwater high-rise pile cap foundations (HRPCFs) to improve the efficiency of bridge inspection and prolong structural durability. First, two key factors in the measurement point arrangement that significantly affect the accuracy of sonar measurement—the appropriate range of measurement distance and the pitch angle—were determined experimentally. Subsequently, an assembled platform was designed to firmly hold the sonar and conveniently move it under strong currents to effectively provide clear images of the pile. A strategy was developed to determine the appropriate number and horizontal and vertical positions of the measurement points around each pile in the pile group, particularly to avoid the obstruction of signal propagation caused by adjacent piles and pile caps. The method was applied to the scanning of an underwater high-rise pile cap foundation of a bridge, and the results showed that the scanning ranges of the imaging sonar at all arranged measurement points were not affected by adjacent piles. The imaging sonar carried by the proposed platform could obtain clear images stably at a water speed of ~2.0 m/s and obtain all surface data of the pile quickly and completely. Full article

(This article belongs to the Special Issue Structural Damage Detection and Quick Repair Assisted by AI Technologies)

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17 pages, 5947 KiB

Open AccessArticle

An Integrated Method Based on Convolutional Neural Networks and Data Fusion for Assembled Structure State Recognition

by Jianbin Luo, Shaofei Jiang, Jian Zhao and Zhangrong Zhang

Sustainability 2023, 15(7), 6094; https://doi.org/10.3390/su15076094 - 31 Mar 2023

Viewed by 986

Abstract

This article focuses on the Assembled Structure (AS) state recognition method based on vibration data. The difficulty of AS state recognition is mainly the extraction of effective classification features and pattern classification. This paper presents an integrated method based on Convolutional Neural Networks (CNNs) and data fusion for AS state recognition. The method takes the wavelet transform time-frequency images of the denoised vibration signal as input, uses CNNs to supervise and learn the data, extracts the deep data structure layer by layer, and improves the classification results through data fusion technology. The method is tested on an assembly concrete shear wall using shake-table testing, and the results show that it has a good overall identification accuracy (IA) of 94.7%, indicating that it is robust and capable of accurately recognizing very small changes in AS state recognition. Full article

(This article belongs to the Special Issue Structural Damage Detection and Quick Repair Assisted by AI Technologies)

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17 pages, 40929 KiB

Open AccessArticle

PCB-YOLO: An Improved Detection Algorithm of PCB Surface Defects Based on YOLOv5

by Junlong Tang, Shenbo Liu, Dongxue Zhao, Lijun Tang, Wanghui Zou and Bin Zheng

Sustainability 2023, 15(7), 5963; https://doi.org/10.3390/su15075963 - 29 Mar 2023

Cited by 13 | Viewed by 3492

Abstract

To address the problems of low network accuracy, slow speed, and a large number of model parameters in printed circuit board (PCB) defect detection, an improved detection algorithm of PCB surface defects based on YOLOv5 is proposed, named PCB-YOLO, in this paper. Based on the K-means++ algorithm, more suitable anchors for the dataset are obtained, and a small target detection layer is added to make the PCB-YOLO pay attention to more small target information. Swin transformer is embedded into the backbone network, and a united attention mechanism is constructed to reduce the interference between the background and defects in the image, and the analysis ability of the network is improved. Model volume compression is achieved by introducing depth-wise separable convolution. The EIoU loss function is used to optimize the regression process of the prediction frame and detection frame, which enhances the localization ability of small targets. The experimental results show that PCB-YOLO achieves a satisfactory balance between performance and consumption, reaching 95.97% mAP at 92.5 FPS, which is more accurate and faster than many other algorithms for real-time and high-precision detection of product surface defects. Full article

(This article belongs to the Special Issue Structural Damage Detection and Quick Repair Assisted by AI Technologies)

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