Computer-Aided Civil and Infrastructure Engineering最新文献

{"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, Yangtao Li, Yixiang Fang, Yiming Wang, Yang Yu, Yang Wei, Liqun Xu, Yijun Chen","doi":"10.1111/mice.13513","DOIUrl":"https://doi.org/10.1111/mice.13513","url":null,"abstract":"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.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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, Kazuaki Watanabe, Junichiro Tamamatsu","doi":"10.1111/mice.13496","DOIUrl":"https://doi.org/10.1111/mice.13496","url":null,"abstract":"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.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"77 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision‐based adaptive cross‐domain online product recommendation for 3D design models","authors":"Xiaoping Zhou, Qin Si, Gen Liu, Zhen‐Zhong Hu, Yukang Wang, Haoran Li, Maozu Guo, Song Xia, Chao Tan, Qingsheng Xie","doi":"10.1111/mice.13495","DOIUrl":"https://doi.org/10.1111/mice.13495","url":null,"abstract":"Three‐dimensional (3D) digital design is extensively adopted in the architecture, engineering, consulting, operations, and maintenance (AECOM) industry to enhance collaboration among stakeholders. Although recommendation systems are commonly employed to facilitate purchasing in e‐commerce websites, none involves recommending online products to users from 3D building design models due to dimensional and stylistic discrepancies. This study proposes a vision‐based adaptive cross‐domain online product recommendation method, VacRed, for 3D building design models. First, a cross‐domain approach is proposed to transform design models into e‐commerce images, addressing discrepancies in dimension and style between them. Second, an adaptive mechanism is introduced to solve the issue of image quality instability caused by variations in generator weights during the training process of generative models. Third, a cross‐domain product recommendation system is developed based on deep learning to recommend the top <jats:italic>k</jats:italic> relevant online products for a given building design product. Finally, experiments were conducted to ascertain the effectiveness of the VacRed method. The experimental results of this method demonstrate its excellent performance, achieving a precision rate (<jats:italic>PR</jats:italic>) of 87.20% and a mean average precision of 83.65%. This study effectively connects two main stages in the AECOM industry, design and purchasing, and two large communities, design and e‐commerce.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"52 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946116","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":"Hierarchical adaptive cross‐coupled control of traffic signals and vehicle routes in large‐scale road network","authors":"Yizhuo Chang, Yilong Ren, Han Jiang, Daocheng Fu, Pinlong Cai, Zhiyong Cui, Aoyong Li, Haiyang Yu","doi":"10.1111/mice.13508","DOIUrl":"https://doi.org/10.1111/mice.13508","url":null,"abstract":"Traffic signal timing and vehicle routing have been empirically demonstrated as the two most promising paradigms for network‐level urban road traffic management. However, mainstream studies based on Wardrop's theory continues to treat these two modules separately without achieving effective coupling. Optimization‐based methods face the challenge of increasing computational complexity as urban scales continue to expand, constrained to small‐scale road networks. To address the above challenges, this paper proposes HAC3, a hierarchical adaptive cross‐coupled control method for network‐wide traffic management. HAC3 utilizes a rolling horizon architecture, comprising a fast update stage and a slow update stage. The core of the slow update stage is a spatiotemporal superposition vehicle route planning (SSP) module, which assigns the optimal route to each connected vehicle (CV) based on the road network state and the traffic signal timing of each intersection, and clarifies priority in right‐of‐way allocation to avoid falling into local optimal. The fast update stage is used for multi‐intersection adaptive traffic signal control (TSC), taking the intersection state and vehicle routes as inputs to optimize the signal timing scheme. Through the asynchronous cross‐coupling optimization of the two stages, the road network efficiency can be improved while ensuring equilibrium. Experimental results show that HAC3 achieves superior convergence performance on both synthetic and real‐world road network data sets, outperforming baseline methods and proving its scalability to large‐scale road networks. Plug‐and‐play experiments indicate the proposed HAC3 framework can integrate with other mainstream signal control models.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946357","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":"Multi-task graph-based model for metro flow prediction under dynamic urban conditions","authors":"Lu Zhao, Linmu Zou, Zijia Wang, Taoran Song, Paul Schonfeld, Feng Chen, Rui Li, Pengcheng Li","doi":"10.1111/mice.13505","DOIUrl":"https://doi.org/10.1111/mice.13505","url":null,"abstract":"Accurately predicting metro commuter flows under changing urban conditions is essential for guiding infrastructure investments and service planning. However, existing methods show limited adaptability to evolving urban conditions. To address this, we propose an adaptive graph sharing embedding cascade interaction network (AGSECIN), which establishes a dynamic mapping relationship between changing urban conditions and commuter flows, enabling accurate predictions of metro inflows, outflows, and origin-destination (OD) flows simultaneously. A graph attention network is built on the long-term graph to capture the spatiotemporal evolving patterns of urban conditions. Then, an adaptive supply–demand sharing embedding network is designed to model the interaction between origin supply and destination demand. Finally, an adaptive feature interaction layer is developed to uncover the complex high-order relations among passenger flows and urban conditions. Experimental results on real-world Beijing datasets demonstrate the superior performance of AGSECIN, compared to contemporary models. Ablation experiments confirm the robustness of our model.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"51 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940449","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}