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Special Issue "Structural Damage Detection and Quick Repair Assisted by AI Technologies"

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Special Issue Editors

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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

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Keywords

structural damage
structural control
damage detection
health monitoring
quick repair
AI technologies

Published Papers (3 papers)

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Research

Open AccessArticle

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

Sheng Shen

Zheng Cao

and

Changqin Lai

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

Viewed by 829

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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Open AccessArticle

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

and

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

Viewed by 489

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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Open AccessArticle

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

and

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

Viewed by 889

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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Special Issue "Structural Damage Detection and Quick Repair Assisted by AI Technologies"

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