Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Computer vision wireless sensors for displacement influence line/surface measurement of footbridges using stationary pedestrian loading","authors":"Miaomin Wang, Huiqi Liang, Zuo Zhu, Huifang Wu, Fuyou Xu, Ki‐Young Koo, James Brownjohn","doi":"10.1111/mice.70008","DOIUrl":"https://doi.org/10.1111/mice.70008","url":null,"abstract":"Although using a heavy vehicle at a consistent speed is a common method to estimate displacement influence lines or displacement influence surfaces (DILs/DISs) of vehicular bridges, it requires algorithms to separate the dynamic and static components from measured displacements. This decomposition process can introduce uncertainties in the results. Additionally, employing vehicles is logistically impractical for most footbridges. To overcome these issues, this paper proposes a new, practical framework using computer vision to measure DILs/DISs on footbridges. It combines a stationary pedestrian loading strategy with a computer vision input–output wireless sensor network (CVIO‐WSN). This framework has two main features: (1) pedestrians follow the “step‐and‐stand” rule, and their weight acts as a static load when they stand still at discrete locations across the footbridge for DIL/DIS measurement; (2) CVIO‐WSN consists of input nodes for human load localization and output nodes for simultaneous structural response measurement, allowing load and response data to be collected in a contactless way that minimizes disruption to operational structures. Two laboratory experiments were conducted to validate this system. The first evaluated the timestamping accuracy between two identical sensor nodes tracking the same moving target, showing an average synchronization error of 2.39 ms. The second assessed the localization accuracy of the input nodes, with the average error of 14.0 mm on the X‐axis and 16.9 mm on the Y‐axis. The method was then applied to an experimental floor structure and an operational full‐scale footbridge. In the first application, the input nodes tracked a human through a sequence of 77 locations across the floor, while the output node measured structural displacement at the center, successfully obtaining the structural DIS. In the second application, the method localized four humans (pedestrians) moving to discrete locations across an operational arch footbridge and briefly remaining stationary while measuring displacement at two points of the structure. Although the measurement results were promising, using heavier pedestrians or increasing their number is recommended to improve the signal‐to‐noise ratio of the structural displacement measurements.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"9 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611156","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}

{"title":"Large language model for post‐earthquake structural damage assessment of buildings","authors":"Yongqing Jiang, Jianze Wang, Xinyi Shen, Kaoshan Dai","doi":"10.1111/mice.70010","DOIUrl":"https://doi.org/10.1111/mice.70010","url":null,"abstract":"A rapid and accurate assessment of structural damage to buildings in the aftermath of earthquakes is critical to emergency responses and engineering retrofit decisions. However, current in situ building damage assessment is primarily conducted through visual inspections by engineering professionals and deep learning techniques using single‐modal information, which are time‐consuming and unable to effectively integrate visual and textual information. In recent years, multimodal learning methods and large language models (LLMs), which could process visual and linguistic information, have emerged as viable alternatives for damage assessment of building constructions. In this study, a vision question–answering model for structural damage assessment (SDA‐Chat) is developed that automatically generates professional textual interpretations of structural damage images via multi‐round visual question–answering (VQA) interactions. A three‐stage training strategy that includes instruction fine‐tuning is designed to improve the model's VQA accuracy. The cross‐modality projector based on dimension reshaping and parallel network architecture is developed to enhance the accuracy and speed of alignment of multimodal features. Comparative experiments are conducted on the self‐constructed dataset containing 8195 pairs of structural damage images and corresponding damage description texts, focusing on various advanced LLMs. The results highlight that the SDA‐Chat can simultaneously process seven different tasks, demonstrating the effectiveness of the proposed method. The highest question–answering accuracy and efficiency of the model reached 83.04% and 435.31 tokens/s, respectively. In addition, high‐precision and lightweight solutions are designed for different application scenarios.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"46 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611162","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}

{"title":"Cover Image, Volume 40, Issue 18","authors":"","doi":"10.1111/mice.70012","DOIUrl":"https://doi.org/10.1111/mice.70012","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Prediction of the most fire-sensitive point in building structures with differentiable agents for thermal simulators</i> by Yuan Xinjie et al., https://doi.org/10.1111/mice.13534.\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 18","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598548","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}

{"title":"Cover Image, Volume 40, Issue 18","authors":"","doi":"10.1111/mice.70011","DOIUrl":"https://doi.org/10.1111/mice.70011","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Two-step rapid inspection of underwater concrete bridge structures combining sonar, camera, and deep learning</i> by Weihao Sun et al., https://doi.org/10.1111/mice.13401.\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 18","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598600","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}

{"title":"A characterization model for time‐dependent displacement of concrete dams","authors":"Kefu Yao, Huaizhi Su, Jean‐Michel Torrenti, Zhiping Wen, Matthieu Vandamme","doi":"10.1111/mice.70009","DOIUrl":"https://doi.org/10.1111/mice.70009","url":null,"abstract":"Time‐dependent displacement caused by creep and shrinkage can significantly affect the structural performance and even shorten the service life of a concrete structure, which is designed for decades or longer, resulting in a safety risk. Unlike in long‐spanning structures such as bridges, where the time‐dependent displacement is readily observable, time‐dependent displacement in dams is typically entangled with hydrostatic and thermal displacement, requiring specialized models and methods to isolate the time‐dependent displacement from field monitoring data. Data‐driven methods can decompose field data to estimate time‐dependent displacement, but the computation and results are subject to uncertainty due to the “black box” property. Physics‐based approaches utilize mechanical laws to simulate creep and shrinkage, providing a clear physical meaning; however, the limited availability of information and data in dam engineering hinders the deployment of physics‐based methods. For this reason, a poromechanical modeling concept that couples creep and shrinkage is employed in this study. Then, a creep–shrinkage coupled modeling tool is developed based on the modeling concept, enabling the numerical simulation of time‐dependent deformation due to creep and shrinkage. This model is further integrated with monitoring data and an intelligent optimization algorithm to enable the real‐time characterization of time‐dependent dam displacements. Specifically, environment data dynamically updates loads and boundary conditions on the dam, while effect data serve as a benchmark to calibrate model parameters through multiobjective optimization. From material to structure, two concrete experiments and one dam project are employed to demonstrate the model's capability in characterizing time‐dependent deformation. Comparisons with experiment and engineering data confirm that the model effectively captures the deformation behavior of the concrete material and structure, providing a physically consistent and data‐informed framework for characterizing time‐dependent displacement in concrete dams.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"26 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603271","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}

{"title":"An integrated texture and depth isomorphic imaging and cross‐modal network for rail surface defect detection and measurement","authors":"Peng Dai, Haoran Wang, Qiang Han, Jun Li, Haoran Song, Zichen Gu, Le Wang, Yunlong Guo, Qingyong Li, Yang Liu","doi":"10.1111/mice.70007","DOIUrl":"https://doi.org/10.1111/mice.70007","url":null,"abstract":"Rail defects significantly impact train operations, even posing serious safety risks. Existing methods can automatically collect images from the rail surface and identify apparent defects while facing challenges such as high false positive rates, visually subtle defects omit errors, and quantitative defect size measurement. To address these issues, an integrated 2D&amp;3D rail surface defect detection and measurement framework is proposed. Initially, this framework introduces an isomorphic imaging system with a long–short exposure mechanism, which uses a single camera to capture pixel‐level registered 2D texture and 3D depth images in a single imaging procedure. Subsequently, a cross‐modal defect detection network is developed to explore complementary semantic and structural information from 2D and 3D images hierarchically, enhancing defect identification capability. Finally, considering the physical curvature changes of the railhead, a partition projection‐based 3D measurement method is established to provide accurate quantitative measurements for defect depth, width, and length. This study collects 2045 operational rail surface images with visible defects and establishes a standard 2D&amp;3D defect detection dataset to validate model performance. Experimental results show that this technology achieves improvements of 7.26% and 9.17% in maximum F1‐score and recall, compared to prevalent SAINet. The defect depth measurement accuracy reached 0.18 mm. Extensive experiments on publicly available non‐service rail surface defect datasets also demonstrate the effectiveness of the proposed method.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568599","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}

{"title":"Dual‐reference approach for vision‐based structural displacement measurement using time‐varying Kalman filter","authors":"Geonyeol Jeon, Kevin Han, Hyungchul Yoon","doi":"10.1111/mice.70006","DOIUrl":"https://doi.org/10.1111/mice.70006","url":null,"abstract":"Accurate displacement measurement is essential for structural health monitoring (SHM) to ensure infrastructure safety. Most previous vision‐based displacement measurement methods either rely on static reference frames or lack dynamic error feedback, leading to performance degradation under real‐world conditions. To address these challenges, this study proposes the dual‐reference Kanade–Lucas–Tomasi (DR‐KLT) method, which improves vision‐based displacement measurements by dynamically integrating both the initial reference frame and the previous reference frame in the KLT tracker. The proposed method estimates the reliability of tracking by analyzing performance indicators such as corner tendency, bi‐directional error, number of feature points, and optical flow magnitude. These estimates are incorporated into a time‐varying Kalman filter for accurate displacement estimation. Validation through simulations, lab‐scale, and on‐site experiments demonstrate the method's robustness and superior accuracy compared to single‐reference approaches. The results confirm that the DR‐KLT approach effectively mitigates the limitations of conventional KLT‐based tracking under unstable conditions such as occlusion or lighting variation, making it a reliable tool for real‐world SHM applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"103 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568606","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}

{"title":"Cover Image, Volume 40, Issue 17","authors":"","doi":"10.1111/mice.70002","DOIUrl":"https://doi.org/10.1111/mice.70002","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Signed distance function-biased flow importance sampling for implicit neural compression of flow fields</i> by Omar A. Mures et al., https://doi.org/10.1111/mice.13526.\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 17","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537117","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}

{"title":"Cover Image, Volume 40, Issue 17","authors":"","doi":"10.1111/mice.70005","DOIUrl":"https://doi.org/10.1111/mice.70005","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Semi-supervised pipe video temporal defect interval localization</i> by Zhu Huang et al., https://doi.org/10.1111/mice.13403.\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 17","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537116","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}

{"title":"Advanced low-light image transformation for accurate nighttime pavement distress detection","authors":"Yuanyuan Hu, Hancheng Zhang, Yue Hou, Pengfei Liu","doi":"10.1111/mice.70001","DOIUrl":"https://doi.org/10.1111/mice.70001","url":null,"abstract":"Pavement distress detection is critical for road safety and infrastructure longevity. Although nighttime inspections offer advantages such as reduced traffic and enhanced operational efficiency, challenges like low visibility and noise hinder their effectiveness. This paper presents IllumiShiftNet, a novel model that transforms low-light images into high-quality, daylight-like representations for pavement distress detection. By employing unpaired image translation techniques, aligned nighttime–daytime datasets are generated for supervised training. The model integrates a lightEnhance generator, multiscale feature discriminators, and distress-focused loss function, ensuring accurate reconstruction of critical pavement details. Experimental results show that IllumiShiftNet achieves a state-of-the-art peak signal-to-noise ratio of 28.5 and a structural similarity index measure of 0.78, enabling detection algorithms trained on daytime data to perform effectively on nighttime imagery. The model demonstrates robust performance across varying illuminance levels, adverse weather conditions, and diverse road types while maintaining real-time processing capabilities. These results establish IllumiShiftNet as a practical solution for nighttime pavement monitoring.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"10 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479437","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}