Computer-Aided Civil and Infrastructure Engineering最新文献_第9页

Efficient quantifying track structure cracks using deep learning 利用深度学习有效量化轨道结构裂纹

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-10 DOI: 10.1111/mice.13477

Hongshuo Sun, Li Song, Zhiwu Yu

{"title":"Efficient quantifying track structure cracks using deep learning","authors":"Hongshuo Sun, Li Song, Zhiwu Yu","doi":"10.1111/mice.13477","DOIUrl":"10.1111/mice.13477","url":null,"abstract":"High-speed railway ballastless track structure crack detection usually has a high demand for the efficiency of crack detection technology. To overcome the limitation that current crack quantification methods usually require multiple steps, this paper proposes an efficient quantification method for track structure cracks using deep learning. This method applies the deep neural network (DNN) to the direct prediction of crack severity index values by modifying DNNs used for image classification. This method adopts a deep learning-based multi-step crack quantification method to calculate crack severity index values, establishes a dataset for predicting track structure interlayer crack severity index values using crack width mean values as labels, establishes a dataset for predicting track structure complex crack severity index values using crack width mean values and crack area values as labels, and utilizes the established datasets to train the modified DNNs. This method crops the track structure panorama in spatial order to obtain images, which not only facilitates DNN prediction but also enables the acquisition of more information such as crack distribution. Under the condition of using the data enhancement method, the mean absolute errors (MAEs) of the prediction results of the trained DNNs under the corresponding testing sets are 0.0191 and 0.0183, and the prediction results are in good agreement with the reference values. The image processing rates of the trained DNNs under the corresponding testing sets are all close to 75 images per second (resolution 512 × 512), which are 8.57 and 13.93 times as computationally efficient as the adopted deep learning-based multi-step crack quantification method.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 19","pages":"2969-2986"},"PeriodicalIF":8.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814185","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}

引用次数: 0

Key origin–destination pairs perception reasoning approach 关键出发地对感知推理方法

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-09 DOI: 10.1111/mice.13476

Zheyuan Jiang, Ziyi Shi, Zheng Zhu, Xiqun (Michael) Chen

{"title":"Key origin–destination pairs perception reasoning approach","authors":"Zheyuan Jiang, Ziyi Shi, Zheng Zhu, Xiqun (Michael) Chen","doi":"10.1111/mice.13476","DOIUrl":"10.1111/mice.13476","url":null,"abstract":"This paper proposes a key origin–destination (OD) pairs perception reasoning (KODPR) approach for route guidance (RG) in urban traffic networks with numerous OD pairs. First, to reduce a real-world RG problem's complexity with large OD sizes, a long-term perception module is developed to identify a few critical OD pairs, making real-world application feasible. Second, the issue of multi-OD cooperation and system resource allocation is addressed through the cooperative perception reasoning method that performs a sequential action update mechanism among agents. Additionally, a balanced reward function is designed in the Markov decision process framework for optimizing dynamic RG strategies. Experimental results using a real-world road network in Hangzhou, China, within a simulation of urban mobility-based simulation platform, demonstrate the superior performance of the proposed approach. The KODPR achieves optimization results close to dynamic user equilibrium by adjusting only 30% of the OD pairs in the network, significantly outperforming comparison methods. Its ability to coordinate extensive OD pairs in densely populated urban environments presents a promising solution for urban traffic RG.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 20","pages":"3093-3117"},"PeriodicalIF":9.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813601","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}

引用次数: 0

Cover Image, Volume 40, Issue 10 封面图片，第 40 卷第 10 期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-04 DOI: 10.1111/mice.13473

引用次数: 0

Cover Image, Volume 40, Issue 10 封面图片，第40卷，第10期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-04 DOI: 10.1111/mice.13474

引用次数: 0

A displacement measurement methodology for deformation monitoring of long-span arch bridges during construction based on scalable multi-camera system 基于可伸缩多摄像机系统的大跨度拱桥施工变形监测的位移测量方法

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-03 DOI: 10.1111/mice.13475

Yihe Yin, Xiaolin Liu, Biao Hu, Wenjun Chen, Xiao Guo, Danyang Ma, Xiaohua Ding, Linhai Han, Qifeng Yu

{"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":"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":"40 13","pages":"1871-1885"},"PeriodicalIF":8.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13475","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A computational method for real-time roof defect segmentation in robotic inspection 机器人检测中顶板缺陷实时分割的计算方法

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-03 DOI: 10.1111/mice.13471

Xiayu Zhao, Houtan Jebelli

{"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}

引用次数: 0

Probabilistic seismic damage assessment for partition walls based on a multi-spring numerical model incorporating uncertainties 基于不确定性多弹簧数值模型的隔墙概率震害评估

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-26 DOI: 10.1111/mice.13472

Jiantao Huang, Masahiro Kurata

{"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":"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":"40 12","pages":"1733-1749"},"PeriodicalIF":8.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13472","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep line segment detection for concrete pavement distress assessment 混凝土路面损伤评估的深线段检测

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-26 DOI: 10.1111/mice.13467

Yuanhao Guo, Yanqiang Huo, Ning Cheng, Zongjun Pan, Xiaoming Yi, Jiankun Cao, Haoyu Sun, Jianqing Wu

{"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":"10.1111/mice.13467","url":null,"abstract":"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%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 19","pages":"2922-2942"},"PeriodicalIF":8.5,"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}

引用次数: 0

Complete-coverage path planning for surface inspection of cable-stayed bridge tower based on building information models and climbing robots 基于建筑信息模型和攀爬机器人的斜拉桥塔面检测全覆盖路径规划

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-25 DOI: 10.1111/mice.13469

Zhe Xia, Jiangpeng Shu, Wei Ding, Yifan Gao, Yuanfeng Duan, Carl James Debono, Vijay Prakash, Dylan Seychell, Ruben Paul Borg

{"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}

引用次数: 0

Bridge damage identification using a small amount of damage labeling data 利用少量损伤标注数据进行桥梁损伤识别

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-25 DOI: 10.1111/mice.13470

Hongshuo Sun, Li Song, Zhiwu Yu

{"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":"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":"40 18","pages":"2729-2750"},"PeriodicalIF":8.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13470","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0