This study presents a scalable multi‐camera system (S‐MCS) for high‐precision displacement measurement and deformation monitoring of long‐span arch bridges during construction. Traditional methods such as robotic total stations (RTS) and single‐camera systems face limitations in dynamic scalability, synchronous multi‐point monitoring, and robustness against environmental disturbances. To address these challenges, the proposed S‐MCS integrates dynamically expandable measuring cameras and dual correcting cameras to compensate for platform ego‐motion. A self‐calibration algorithm and spatiotemporal reference alignment framework are developed to ensure measurement consistency across evolving construction phases. The system was deployed on a 600‐m‐span arch bridge, achieving sub‐millimeter accuracy (root mean square error ≤ 1.09 mm) validated against RTS data. Key innovations include real‐time platform motion compensation, adaptive coverage expansion, and high‐frequency sampling for capturing transient structural responses. Comparative analyses under construction loads, thermal variations, and extreme crosswinds demonstrated the system's superiority in tracking multi‐point displacements, resolving dynamic behaviors and supporting safety assessments. The S‐MCS provides a robust solution for automated, large‐scale structural health monitoring, with potential applications in diverse infrastructure projects requiring adaptive, high‐resolution deformation tracking.
{"title":"A displacement measurement methodology for deformation monitoring of long‐span arch bridges during construction based on scalable multi‐camera system","authors":"Yihe Yin, Xiaolin Liu, Biao Hu, Wenjun Chen, Xiao Guo, Danyang Ma, Xiaohua Ding, Linhai Han, Qifeng Yu","doi":"10.1111/mice.13475","DOIUrl":"https://doi.org/10.1111/mice.13475","url":null,"abstract":"This study presents a scalable multi‐camera system (S‐MCS) for high‐precision displacement measurement and deformation monitoring of long‐span arch bridges during construction. Traditional methods such as robotic total stations (RTS) and single‐camera systems face limitations in dynamic scalability, synchronous multi‐point monitoring, and robustness against environmental disturbances. To address these challenges, the proposed S‐MCS integrates dynamically expandable measuring cameras and dual correcting cameras to compensate for platform ego‐motion. A self‐calibration algorithm and spatiotemporal reference alignment framework are developed to ensure measurement consistency across evolving construction phases. The system was deployed on a 600‐m‐span arch bridge, achieving sub‐millimeter accuracy (root mean square error ≤ 1.09 mm) validated against RTS data. Key innovations include real‐time platform motion compensation, adaptive coverage expansion, and high‐frequency sampling for capturing transient structural responses. Comparative analyses under construction loads, thermal variations, and extreme crosswinds demonstrated the system's superiority in tracking multi‐point displacements, resolving dynamic behaviors and supporting safety assessments. The S‐MCS provides a robust solution for automated, large‐scale structural health monitoring, with potential applications in diverse infrastructure projects requiring adaptive, high‐resolution deformation tracking.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"34 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cover image is based on the article A machine vision-based intelligent segmentation method for dam underwater cracks using swarm optimization algorithm and deep learning by Yantao Zhu et al., https://doi.org/10.1111/mice.13343.
{"title":"Cover Image, Volume 40, Issue 10","authors":"","doi":"10.1111/mice.13473","DOIUrl":"https://doi.org/10.1111/mice.13473","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>A machine vision-based intelligent segmentation method for dam underwater cracks using swarm optimization algorithm and deep learning</i> by Yantao Zhu et al., https://doi.org/10.1111/mice.13343.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 10","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143778284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cover image is based on the article Short-term Prediction of Railway Track Degradation Using Ensemble Deep Learning by Yong Zhuang et al., https://doi.org/10.1111/mice.13462.
{"title":"Cover Image, Volume 40, Issue 10","authors":"","doi":"10.1111/mice.13474","DOIUrl":"https://doi.org/10.1111/mice.13474","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Short-term Prediction of Railway Track Degradation Using Ensemble Deep Learning</i> by Yong Zhuang et al., https://doi.org/10.1111/mice.13462.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 10","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143778280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roof inspections are crucial but perilous, necessitating safer and more cost-effective solutions. While robots offer promising solutions to reduce fall risks, robotic vision systems face efficiency limitations due to computational constraints and scarce specialized data. This study presents real-time roof defect segmentation network (RRD-SegNet), a deep learning framework optimized for mobile robotic platforms. The architecture features a mobile-efficient backbone network for lightweight processing, a defect-specific feature extraction module for improved accuracy, and a regressive detection and classification head for precise defect localization. Trained on the multi-type roof defect segmentation dataset of 1350 annotated images across six defect categories, RRD-SegNet integrates with a roof damage identification module for real-time tracking. The system surpasses state-of-the-art models with 85.2% precision and 76.8% recall while requiring minimal computational resources. Field testing confirms its effectiveness with F1-scores of 0.720–0.945 across defect types at processing speeds of 1.62 ms/frame. This work advances automated inspection in civil engineering by enabling efficient, safe, and accurate roof assessments via mobile robotic platforms.
{"title":"A computational method for real-time roof defect segmentation in robotic inspection","authors":"Xiayu Zhao, Houtan Jebelli","doi":"10.1111/mice.13471","DOIUrl":"https://doi.org/10.1111/mice.13471","url":null,"abstract":"Roof inspections are crucial but perilous, necessitating safer and more cost-effective solutions. While robots offer promising solutions to reduce fall risks, robotic vision systems face efficiency limitations due to computational constraints and scarce specialized data. This study presents real-time roof defect segmentation network (RRD-SegNet), a deep learning framework optimized for mobile robotic platforms. The architecture features a mobile-efficient backbone network for lightweight processing, a defect-specific feature extraction module for improved accuracy, and a regressive detection and classification head for precise defect localization. Trained on the multi-type roof defect segmentation dataset of 1350 annotated images across six defect categories, RRD-SegNet integrates with a roof damage identification module for real-time tracking. The system surpasses state-of-the-art models with 85.2% precision and 76.8% recall while requiring minimal computational resources. Field testing confirms its effectiveness with F1-scores of 0.720–0.945 across defect types at processing speeds of 1.62 ms/frame. This work advances automated inspection in civil engineering by enabling efficient, safe, and accurate roof assessments via mobile robotic platforms.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"73 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To overcome the limitations of fragility analysis in the assessment of partition walls, specifically data shortage, general uncertainties, and subjective criteria, this study proposes a probabilistic method to evaluate seismic damage of partition walls. A proposed multi-spring numerical model balances the damage representation and computational efficiency in simulations, thus avoiding extensive experimental testing. By accounting for parameter uncertainties in individual partition walls, the uncertainties introduced by the fragility group are avoided, and the description of the seismic damage is probabilistic, enhancing the reliability of the assessment results. Using damaged areas as the assessment criterion alleviates epistemic uncertainty exacerbated by subjective judgments on repair actions. Furthermore, it eliminates the assumption of a log-normal distribution for damage in fragility analysis, improving the calculations of damage probabilities and expected repair costs. The results are anticipated to be valuable for assessing the seismic risk and repair costs of partition walls.
{"title":"Probabilistic seismic damage assessment for partition walls based on a multi-spring numerical model incorporating uncertainties","authors":"Jiantao Huang, Masahiro Kurata","doi":"10.1111/mice.13472","DOIUrl":"https://doi.org/10.1111/mice.13472","url":null,"abstract":"To overcome the limitations of fragility analysis in the assessment of partition walls, specifically data shortage, general uncertainties, and subjective criteria, this study proposes a probabilistic method to evaluate seismic damage of partition walls. A proposed multi-spring numerical model balances the damage representation and computational efficiency in simulations, thus avoiding extensive experimental testing. By accounting for parameter uncertainties in individual partition walls, the uncertainties introduced by the fragility group are avoided, and the description of the seismic damage is probabilistic, enhancing the reliability of the assessment results. Using damaged areas as the assessment criterion alleviates epistemic uncertainty exacerbated by subjective judgments on repair actions. Furthermore, it eliminates the assumption of a log-normal distribution for damage in fragility analysis, improving the calculations of damage probabilities and expected repair costs. The results are anticipated to be valuable for assessing the seismic risk and repair costs of partition walls.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"11 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a deep line segment detection model named DLSD, for identifying four ubiquitous line segments on concrete pavements: joint, sealed joint, bridge expansion joint, and roadway boundary. DLSD associates a category with the triple-point representation to encode a line segment. Its network employs a localization head and a classification head, attaching several auxiliary branches to integrate the line segment shape context. A novel dual-attention mechanism further improves the line segment classification. From experiments, the structural average precision (sAP) and mean sAP of the DLSD model on class-agnostic and class-aware line segment detection achieve 85.0% and 73.4%, respectively. The former outperforms the existing best-performed method by 2.7%, and the latter sets a state-of-the-art performance. An automated pipeline combines the line segments with cracks to detect corner break and shattered slab on concrete pavements for an accurate distress assessment, reducing the error rate of distress ratio value from 38.7% to 11.5%.
{"title":"Deep line segment detection for concrete pavement distress assessment","authors":"Yuanhao Guo, Yanqiang Huo, Ning Cheng, Zongjun Pan, Xiaoming Yi, Jiankun Cao, Haoyu Sun, Jianqing Wu","doi":"10.1111/mice.13467","DOIUrl":"https://doi.org/10.1111/mice.13467","url":null,"abstract":"This study proposes a <i>d</i>eep <i>l</i>ine <i>s</i>egment <i>d</i>etection model named DLSD, for identifying four ubiquitous line segments on concrete pavements: joint, sealed joint, bridge expansion joint, and roadway boundary. DLSD associates a category with the triple-point representation to encode a line segment. Its network employs a localization head and a classification head, attaching several auxiliary branches to integrate the line segment shape context. A novel dual-attention mechanism further improves the line segment classification. From experiments, the structural average precision (sAP) and mean sAP of the DLSD model on class-agnostic and class-aware line segment detection achieve 85.0% and 73.4%, respectively. The former outperforms the existing best-performed method by 2.7%, and the latter sets a state-of-the-art performance. An automated pipeline combines the line segments with cracks to detect corner break and shattered slab on concrete pavements for an accurate distress assessment, reducing the error rate of distress ratio value from 38.7% to 11.5%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"212 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhe Xia, Jiangpeng Shu, Wei Ding, Yifan Gao, Yuanfeng Duan, Carl James Debono, Vijay Prakash, Dylan Seychell, Ruben Paul Borg
Climbing robots present transformative potential for automated structural inspections, yet their deployment remains limited by the reliance on manual control due to the absence of effective environment perception and path-planning solutions. The critical bottleneck lies in the difficulty of generating accurate planning maps solely through onboard sensors due to the challenge of capturing open, large-scale, and irregular environments (e.g., cable-stayed bridge towers). This study proposes a building information modeling (BIM)-based complete-coverage path planning (BCCPP) framework, leveraging BIM to enable autonomous robotic inspection. The framework constructs accurate grid maps through BIM data, addressing the map-perception problem for robots in open, large-scale, and irregular environment while refining the boustrophedon-A* algorithm with multi-heuristic optimization, which reduces path repetition and improves energy efficiency. Field and simulated experiments on a cable-stayed bridge tower show the BCCPP achieves 93.5% coverage with 9.1% repetition, and planned paths were executable within a 0.2 m tolerance and collisions avoided. This work bridges BIM, climbing robot, and path planning, offering a scalable solution for intelligent infrastructure inspection.
{"title":"Complete-coverage path planning for surface inspection of cable-stayed bridge tower based on building information models and climbing robots","authors":"Zhe Xia, Jiangpeng Shu, Wei Ding, Yifan Gao, Yuanfeng Duan, Carl James Debono, Vijay Prakash, Dylan Seychell, Ruben Paul Borg","doi":"10.1111/mice.13469","DOIUrl":"https://doi.org/10.1111/mice.13469","url":null,"abstract":"Climbing robots present transformative potential for automated structural inspections, yet their deployment remains limited by the reliance on manual control due to the absence of effective environment perception and path-planning solutions. The critical bottleneck lies in the difficulty of generating accurate planning maps solely through onboard sensors due to the challenge of capturing open, large-scale, and irregular environments (e.g., cable-stayed bridge towers). This study proposes a building information modeling (BIM)-based complete-coverage path planning (BCCPP) framework, leveraging BIM to enable autonomous robotic inspection. The framework constructs accurate grid maps through BIM data, addressing the map-perception problem for robots in open, large-scale, and irregular environment while refining the boustrophedon-A* algorithm with multi-heuristic optimization, which reduces path repetition and improves energy efficiency. Field and simulated experiments on a cable-stayed bridge tower show the BCCPP achieves 93.5% coverage with 9.1% repetition, and planned paths were executable within a 0.2 m tolerance and collisions avoided. This work bridges BIM, climbing robot, and path planning, offering a scalable solution for intelligent infrastructure inspection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"29 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a method for bridge damage identification using a small amount of damage labeling data. This method first trains a deep neural network (DNN) with undamaged bridge inclination responses as inputs and bridge equivalent loads as labels. The ratio curve related to the bridge damage state can be obtained by quantifying the change in the DNN prediction error before and after bridge damage. Then, this method achieves the efficient calculation of ratio curves corresponding to different damage states based on finite element static simulation, and damage index curves calculated based on ratio curves are used to produce bridge damage localization labeling data to achieve bridge damage localization. Finally, the quantification of bridge damage can be achieved by only calculating the ratio curves of different damage degrees at the damage location. The proposed method not only overcomes the limitations of high modeling cost, low efficiency, and poor robustness to measurement noise and modeling errors of the finite element dynamic simulation method in producing damage labeling data to some extent but also can achieve bridge damage localization by using only the damage labeling data of a single damage degree at each damage location, and can achieve the approximate prediction of multi-damage locations without including multi-damage localization labeling data. The feasibility of the proposed method under conditions of unknown loads, a small number of sensors, and the presence of modeling errors and measurement noise is verified by numerical simulations.
{"title":"Bridge damage identification using a small amount of damage labeling data","authors":"Hongshuo Sun, Li Song, Zhiwu Yu","doi":"10.1111/mice.13470","DOIUrl":"https://doi.org/10.1111/mice.13470","url":null,"abstract":"This paper proposes a method for bridge damage identification using a small amount of damage labeling data. This method first trains a deep neural network (DNN) with undamaged bridge inclination responses as inputs and bridge equivalent loads as labels. The ratio curve related to the bridge damage state can be obtained by quantifying the change in the DNN prediction error before and after bridge damage. Then, this method achieves the efficient calculation of ratio curves corresponding to different damage states based on finite element static simulation, and damage index curves calculated based on ratio curves are used to produce bridge damage localization labeling data to achieve bridge damage localization. Finally, the quantification of bridge damage can be achieved by only calculating the ratio curves of different damage degrees at the damage location. The proposed method not only overcomes the limitations of high modeling cost, low efficiency, and poor robustness to measurement noise and modeling errors of the finite element dynamic simulation method in producing damage labeling data to some extent but also can achieve bridge damage localization by using only the damage labeling data of a single damage degree at each damage location, and can achieve the approximate prediction of multi-damage locations without including multi-damage localization labeling data. The feasibility of the proposed method under conditions of unknown loads, a small number of sensors, and the presence of modeling errors and measurement noise is verified by numerical simulations.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"183 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bingchuan Bai, Bo Lu, Zhichao Wen, Han Yuan, Weijie Li, Xuefeng Zhao
Strain is one of the key indicators for structural health monitoring. In this study, we developed a low-cost microscopic vision-based real-time strain sensor using Raspberry Pi (called MISS-Dym). By strategies for image processing accelerated and the specific running logic, the strain can be outputted at a frequency of more than 30 Hz in real time. The MISS-Dym integrates multiple functions including real-time strain calculations, temperature compensation, data storage, and wireless transmission. Comparative experiments were performed with fiber Bragg grating to assess the accuracy of the sensor. In the static experiments, the maximum mean squared error was 1.77 µε, while the maximum relative error was 5.5% in the dynamic experiments. Additionally, a 10-day monitoring was conducted by MISS-Dym. The results show that the sensor can effectively capture both the vehicle-induced and the temperature-induced strain of the concrete bridge. The MISS-Dym provides an efficient and low-cost method for monitoring the dynamic strain responses of concrete structures.
{"title":"Development of a low-cost microscopic vision-based real-time strain sensor using Raspberry Pi","authors":"Bingchuan Bai, Bo Lu, Zhichao Wen, Han Yuan, Weijie Li, Xuefeng Zhao","doi":"10.1111/mice.13468","DOIUrl":"https://doi.org/10.1111/mice.13468","url":null,"abstract":"Strain is one of the key indicators for structural health monitoring. In this study, we developed a low-cost microscopic vision-based real-time strain sensor using Raspberry Pi (called MISS-Dym). By strategies for image processing accelerated and the specific running logic, the strain can be outputted at a frequency of more than 30 Hz in real time. The MISS-Dym integrates multiple functions including real-time strain calculations, temperature compensation, data storage, and wireless transmission. Comparative experiments were performed with fiber Bragg grating to assess the accuracy of the sensor. In the static experiments, the maximum mean squared error was 1.77 µε, while the maximum relative error was 5.5% in the dynamic experiments. Additionally, a 10-day monitoring was conducted by MISS-Dym. The results show that the sensor can effectively capture both the vehicle-induced and the temperature-induced strain of the concrete bridge. The MISS-Dym provides an efficient and low-cost method for monitoring the dynamic strain responses of concrete structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"28 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High-frequency detection of track defects is crucial for accurate track condition assessment and system safety. Onboard vibration data collection devices can significantly increase detection density without additional costs. However, defect assessment based on this is significantly challenging, including the spatial heterogeneity of track parameters, distribution mismatch between vibration data and defect labels, and variability in vibration responses across different defects. This study proposes a multilevel track defect assessment framework based on vehicle body vibration. The correlation intensity between vibrations and heterogeneity factors was analyzed, and a correlation-view spectral clustering algorithm was designed to achieve effective data set partitioning. A spectral-normalized neural Gaussian process-based adaptive-threshold self-training method (SNGP-ASM) was developed to generate high-quality pseudo-labels and generate a fully labeled data set. An attention-guided multitask cascaded convolutional neural network (CNN) was constructed to progressively assess track defects using channel-wise attentions and a cross-hierarchical attention guidance module. Validations on multiple Chinese metro lines demonstrated that the framework achieved a high performance in training and testing for most defect assessment tasks within lines, and the trained model can effectively adapt to new lines with only lightweight fine-tuning. Moreover, the framework maintained a high computational efficiency, enabling high-frequency track condition monitoring in practical deployment scenarios.
{"title":"A multilevel track defects assessment framework based on vehicle body vibration","authors":"Xingqingrong Chen, Yuanjie Tang, Rengkui Liu","doi":"10.1111/mice.13466","DOIUrl":"https://doi.org/10.1111/mice.13466","url":null,"abstract":"High-frequency detection of track defects is crucial for accurate track condition assessment and system safety. Onboard vibration data collection devices can significantly increase detection density without additional costs. However, defect assessment based on this is significantly challenging, including the spatial heterogeneity of track parameters, distribution mismatch between vibration data and defect labels, and variability in vibration responses across different defects. This study proposes a multilevel track defect assessment framework based on vehicle body vibration. The correlation intensity between vibrations and heterogeneity factors was analyzed, and a correlation-view spectral clustering algorithm was designed to achieve effective data set partitioning. A spectral-normalized neural Gaussian process-based adaptive-threshold self-training method (SNGP-ASM) was developed to generate high-quality pseudo-labels and generate a fully labeled data set. An attention-guided multitask cascaded convolutional neural network (CNN) was constructed to progressively assess track defects using channel-wise attentions and a cross-hierarchical attention guidance module. Validations on multiple Chinese metro lines demonstrated that the framework achieved a high performance in training and testing for most defect assessment tasks within lines, and the trained model can effectively adapt to new lines with only lightweight fine-tuning. Moreover, the framework maintained a high computational efficiency, enabling high-frequency track condition monitoring in practical deployment scenarios.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"21 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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