Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Self‐supervised domain adaptive approach for extrapolated crack segmentation with fine‐tuned inpainting generative model","authors":"Seungbo Shim","doi":"10.1111/mice.13517","DOIUrl":"https://doi.org/10.1111/mice.13517","url":null,"abstract":"The number and proportion of aging infrastructures are increasing, thereby necessitating accurate inspection to ensure safety and structural stability. While computer vision and deep learning have been widely applied to concrete cracks, domain shift issues often result in the poor performance of pretrained models at new sites. To address this, a self‐supervised domain adaptation method using generative artificial intelligence based on inpainting is proposed. This approach generates site‐specific crack images and labels by fine‐tuning Stable Diffusion model with DreamBooth. The resulting data set is then used to train a crack detection neural network using self‐supervised learning. Evaluations across two target domain data sets and eight models show average F1‐score improvements of 25.82% and 17.83%. A comprehensive tunnel ceiling field test further demonstrates the effectiveness of the method. By enhancing real‐world crack detection capabilities, this approach supports better structural safety management.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"56 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136756","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 14","authors":"","doi":"10.1111/mice.13519","DOIUrl":"https://doi.org/10.1111/mice.13519","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Spatially aware Markov chain-based deterioration prediction of bridge components using a Graph Transformer</i> by Shogo Inadomi et al., https://doi.org/10.1111/mice.13497.\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 14","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108853","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 14","authors":"","doi":"10.1111/mice.13518","DOIUrl":"https://doi.org/10.1111/mice.13518","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion</i> by Zhiyi Tang et al., https://doi.org/10.1111/mice.13377.\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 14","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108852","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":"Long short‐term memory‐based real‐time prediction models for freezing depth and thawing time in unbound pavement layers","authors":"Y. Ma, S. Park, A. Bae, K. Kwon, H. Choi","doi":"10.1111/mice.13516","DOIUrl":"https://doi.org/10.1111/mice.13516","url":null,"abstract":"The prediction of freezing depth and thawing time of unbound pavement layers in cold regions is a critical task in pavement design and management. This study developed long short‐term memory (LSTM)‐based encoder–decoder models to accurately predict freezing depth and thawing time, with air temperature as the sole input variable. The models, which aim to offer a 14‐day advance prediction of the thawing time for effective pavement management, utilized data from the Long‐Term Pavement Performance program's database, provided by the Federal Highway Administration in United States. This database contains extensive records on air temperature and freezing states. The LSTM models were trained using data collected from four regions in North America with severely cold winters (Quebec, Minnesota, Ontario, and Maine) and subsequently validated using data from both severely cold (South Dakota and Vermont) and mild (Idaho and Wyoming) winter regions. During the validation phase, the models demonstrated strong performance in the severely cold regions, with predicted freezing depths deviating from the measured values by only 0.05 to 0.20 m and thawing date predictions differing by just 1 to 3 days. However, in the mild winter regions, the models showed less accuracy, with freezing depth differences ranging from 0.10 to 0.40 m and thawing date delays of 3–6 days. Compared to existing analytical and empirical models, the LSTM prediction models developed in this study provide enhanced convenience while maintaining a satisfactory level of accuracy.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"162 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144104279","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":"Skill-abstracting continual reinforcement learning for safe, efficient, and comfortable autonomous driving through vehicle–cloud collaboration","authors":"Jing Chen, Cong Zhao, Kun Gao, Yuxiong Ji, Yuchuan Du","doi":"10.1111/mice.13503","DOIUrl":"https://doi.org/10.1111/mice.13503","url":null,"abstract":"Safe, efficient, and comfortable autonomous driving is essential for high-quality transport service in an open road environment. However, most existing driving strategy learning approaches for autonomous driving struggle with varying driving environments, only working properly under certain scenarios. Therefore, this study proposes a novel hierarchical continual reinforcement learning (RL) framework to abstract various driving patterns as skills and support driving strategy adaptation based on vehicle-cloud collaboration. The proposed framework leverages skill abstracting in the cloud to learn driving skills from massive demonstrations and store them as deep RL models, mitigating catastrophic forgetting and data imbalance for driving strategy adaptation. Connected autonomous vehicles’ (CAVs) driving strategies are sent to the cloud and continually updated by integrating abstracted driving skills and interactions with parallel environments in the cloud. Then, CAVs receive updated driving strategies from the cloud to interact with the real-time environment. In the experiment, high-fidelity and stochastic environments are created using real-world pavement and traffic data. Experimental results showcase the proposed hierarchical continual RL framework exhibits a 34.04% reduction in potentially hazardous events and a 9.04% improvement in vertical comfort, compared to a classical RL baseline, demonstrating superior driving performance and strong generalization capabilities in varying driving environments. Overall, the proposed framework reinvigorates streaming driving data, prevailing motion planning models, and cloud computation resources for life-long driving strategy learning.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"128 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088155","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":"Modeling car-following behaviors using a driving style–based Bayesian model averaging Copula framework in mixed traffic flow","authors":"Shubo Wu, Yue Zhang, Yajie Zou, Yuanchang Xie, Yangyang Wang","doi":"10.1111/mice.13514","DOIUrl":"https://doi.org/10.1111/mice.13514","url":null,"abstract":"As a fundamental driving behavior, the accurate modeling of car-following (CF) dynamics is essential for improving traffic flow and advancing autonomous driving technologies. Due to the stochastic nature of CF behaviors, the CF model parameters often exhibit heterogeneity (multimodal trends), distribution uncertainty, and parameter correlations. Most studies have examined correlations among CF model parameters, assuming deterministic marginal distributions, and investigated heterogeneity through driving behavior indicators. However, distribution uncertainty and multimodal trends in CF model parameter characteristics remain insufficiently explored. To address this challenge, this study proposes a driving style–based Bayesian model averaging Copula (DS-BMAC) framework that simultaneously accounts for heterogeneity, distribution uncertainty, and parameter correlations in CF behavior modeling. Using the intelligent driver model (IDM) as a representative example, its parameters are calibrated using CF trajectory data extracted from the Waymo open motion data set. Based on these calibrated IDM parameters, a multivariate Gaussian mixture model is employed to categorize three distinct driving styles, capturing heterogeneity. Subsequently, a Bayesian model average Copula approach is applied to address distribution uncertainty and parameter correlations. Deterministic and multivehicle ring road simulations were conducted to assess the effectiveness of the proposed DS-BMAC framework. The results demonstrate that the DS-BMAC framework provides a precise characterization of CF model parameters and effectively reproduces microscopic CF behaviors compared to other approaches. Additionally, the DS-BMAC framework offers a realistic representation of traffic flow dynamics. The research findings are valuable for understanding mixed traffic flow dynamics and for developing CF decision-making models for autonomous vehicles and advanced driver-assistance systems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"2 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088160","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 effective ship detection approach combining lightweight networks with supervised simulation-to-reality domain adaptation","authors":"Ruixuan Liao, Yiming Zhang, Hao Wang, Linjun Lu, Zhengyi Chen, Xiaoyou Wang, Wenqiang Zuo","doi":"10.1111/mice.13501","DOIUrl":"https://doi.org/10.1111/mice.13501","url":null,"abstract":"Computer vision-based ship detection using extensively labeled images is crucial for visual maritime surveillance. However, such data collection is labor-intensive and time-demanding, which hinders the practical application of newly built ship inspection systems. Additionally, well-trained detectors are usually deployed on resource-constrained edge devices, highlighting the lowered complexity of deep neural networks. This study proposes a simulation-to-reality (Sim2Real) domain adaptation framework that alleviates the annotation burden and improves ship detection efficiency by a lightweight adaptive detector. Specifically, a proxy virtual environment is established to generate synthetic images. An automated annotation method is introduced for data labeling, creating a large-scale synthetic ship detection dataset termed SSDShips. The dataset comprises 4800 images, 23,317 annotated instances, six ship categories, and various scenarios. A novel multi-level fusion lightweight (MFL) network is developed based on the you only look once version 8 (YOLOv8) framework, referred to as MFL-YOLOv8. MFL-YOLOv8 is pre-trained on the SSDShips and fine-tuned using both realistic and pseudo-realistic data through a hybrid transfer learning strategy to minimize cross-domain discrepancies. The results show that MFL-YOLOv8 reduces model parameters by 20.5% and giga floating-point operations per second by 66.0%, while improving detection performance, compared to the vanilla YOLOv8. Sim2Real adaptation boosts the model generalization in practical situations, reaching mean average precision mAP@0.5 and mAP@0.5:0.95 scores of 98.8% and 81.8%, respectively. It also shrinks the size of real-world labeling by 66.4%, achieving superior detection effectiveness and efficiency, compared to existing ship detection methods within the specific domain. Deployed on the NVIDIA Jetson Orin Nano, the proposed method demonstrates reliable performance in edge-oriented ship detection. The SSDShips dataset is available at https://github.com/congliaoxueCV/SSDShips.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"30 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088161","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":"A lightweight physics-data-driven method for real-time prediction of subgrade settlements induced by shield tunneling","authors":"Guankai Wang, Yao Shan, Weifan Lin, Zhiyao Tian, Shunhua Zhou, Giovanni S. Alberti, Bettina Detmann, Tong Zhou, Jiahui Chen","doi":"10.1111/mice.13512","DOIUrl":"https://doi.org/10.1111/mice.13512","url":null,"abstract":"Real-time prediction of subgrade settlement caused by shield tunneling is crucial in engineering applications. However, data-driven methods are prone to overfitting, while physical methods rely on certain assumptions, making it difficult to select satisfactory parameters. Although there are currently physics-data-driven methods, they typically require extensive iterative calculations with physical models, which makes them unavailable for real-time prediction. This paper introduces a lightweight physics-data-driven method for predicting subgrade settlement caused by shield tunneling. The core concept involves using a single calculation of the physical model to provide a weak constraint. A deep learning network is then designed to capture spatiotemporal correlations based on ConvLSTM. By iteratively incorporating real-time data, the learning of physical constraints is further enhanced. This method combines the predictive power of data-driven method with the reasonable constraints of physical laws, validated a good performance in a practical project. The results demonstrate that this method meets real-time prediction requirements in engineering, achieving an coefficient of determination of 0.980, a root mean square error of 0.22 mm, and a mean absolute error of 0.15 mm. Furthermore, it outperforms both physical and data-driven models and demonstrates good generalization performance. This study provides effective guidance for engineering practices.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"25 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088165","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":"Environmental-aware deformation prediction of water-related concrete structures using deep learning","authors":"Hao Gu,&nbsp;Yangtao Li,&nbsp;Yixiang Fang,&nbsp;Yiming Wang,&nbsp;Yang Yu,&nbsp;Yang Wei,&nbsp;Liqun Xu,&nbsp;Yijun Chen","doi":"10.1111/mice.13513","DOIUrl":"10.1111/mice.13513","url":null,"abstract":"<p>Accurate long-term deformation prediction is essential to ensure the structural security and ongoing stability of large water-related concrete structures like ultra-high arch dams. Traditional statistical regression and shallow machine learning approaches, due to their algorithmic constraints, often fail to comprehensively capture the complex temporal and spatial dependencies inherent in high-dimensional prototypical monitoring data, thereby limiting their predictive accuracy and robustness. To address these challenges, this study proposes a multi-point deformation forecasting model that incorporates both spatial and temporal correlations between environmental factors and deformation, utilizing advanced deep learning (DL) techniques. Specifically, we employ a Transformer-based convolutional long short-term memory (ConvLSTM) model to capture the spatiotemporal dependencies across numerous temperature and deformation monitoring sequences. Furthermore, the multi-objective bayesian optimization algorithm is utilized to ascertain the optimal model architecture and hyperparameters, concurrently maximizing the regression coefficient and minimizing the root mean square error (RMSE). The effectiveness of the proposed DL-based model for high-arch dam deformation prediction is validated using data from multiple monitoring points of ultra-high arch dams. Experimental results demonstrate that the TransformerConvLSTM method significantly outperforms other models at five monitoring points. Quantitatively, it consistently achieves lower RMSE and high correlation coefficient values, indicating its superior ability to provide accurate predictions with minimal error.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 15","pages":"2130-2151"},"PeriodicalIF":8.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144104278","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":"3D data generation of manholes from single panoramic inspection images","authors":"Mizuki Tabata,&nbsp;Kazuaki Watanabe,&nbsp;Junichiro Tamamatsu","doi":"10.1111/mice.13496","DOIUrl":"10.1111/mice.13496","url":null,"abstract":"<p>Infrastructure facilities require proper maintenance, including diagnosing structural durability and determining appropriate repair methods. Structural analysis is widely used to assess structural conditions, necessitating three-dimensional (3D) data that accurately reflect the locations of deterioration. Therefore, we investigate a method to generate 3D data of manholes from single-shot panoramic inspection images, focusing on accurately mapping the condition of wall surfaces, the primary targets of manhole inspections, including their deterioration such as rebar corrosion. However, some areas of the wall are occasionally occluded by internal objects such as cables or adjacent walls, making accurate layout estimation and 3D reconstruction difficult. To address this issue, we propose a model that incorporates 3D data generated from design drawings as additional input, along with a post-processing method to adjust the estimated layout based on geometry in the drawings. The evaluation results show that our method improves 3D IoU accuracy, especially under occluded conditions. Moreover, with a mapping error of 14.3 cm in the reconstructed 3D data, our approach demonstrates its practical potential for use in the structural analysis of most manholes.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 16","pages":"2383-2396"},"PeriodicalIF":8.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066119","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}