Computer-Aided Civil and Infrastructure Engineering最新文献_第3页

Cover Image, Volume 40, Issue 31 封面图片，第40卷，第31期

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-19 DOI: 10.1111/mice.70184

引用次数: 0

Cover Image, Volume 40, Issue 31 封面图片，第40卷，第31期

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-19 DOI: 10.1111/mice.70187

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Cover Image, Volume 40, Issue 30 封面图像，第40卷，第30期

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-09 DOI: 10.1111/mice.70183

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Cover Image, Volume 40, Issue 30 封面图像，第40卷，第30期

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-09 DOI: 10.1111/mice.70181

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Hierarchical analysis of spreading dynamics in complex systems 复杂系统扩展动力学的层次分析

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-08 DOI: 10.1111/mice.70165

Aparimit Kasliwal, Abdullah Alhadlaq, Ariel Salgado, Auroop R. Ganguly, Marta C. González

{"title":"Hierarchical analysis of spreading dynamics in complex systems","authors":"Aparimit Kasliwal, Abdullah Alhadlaq, Ariel Salgado, Auroop R. Ganguly, Marta C. González","doi":"10.1111/mice.70165","DOIUrl":"10.1111/mice.70165","url":null,"abstract":"Modeling spreading dynamics on spatial networks is crucial to addressing challenges related to traffic congestion, epidemic outbreaks, efficient information dissemination, and technology adoption. Existing approaches include domain-specific agent-based simulations, which offer detailed dynamics but often involve extensive parameterization, and simplified differential equation models, which provide analytical tractability but may abstract away spatial heterogeneity in propagation patterns. As a step toward addressing this trade-off, this work presents a hierarchical multiscale framework that approximates spreading dynamics across different spatial scales under certain simplifying assumptions. Applied to the Susceptible-Infected-Recovered (SIR) model, the approach ensures consistency in dynamics across scales through multiscale regularization, linking parameters at finer scales to those obtained at coarser scales. This approach constrains the parameter search space, and enables faster convergence of the model fitting process compared to the non-regularized model. Using hierarchical modeling, the spatial dependencies critical for understanding system-level behavior are captured while mitigating the computational challenges posed by parameter proliferation at finer scales. Considering traffic congestion and COVID-19 spread as case studies, the calibrated fine-scale model is employed to analyze the effects of perturbations and to identify critical regions and connections that disproportionately influence system dynamics. This facilitates targeted intervention strategies and provides a tool for studying and managing spreading processes in spatially distributed sociotechnical systems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6223-6241"},"PeriodicalIF":9.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703914","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 hierarchical framework for three-dimensional pavement crack detection on point clouds with multi-scale abnormal region filtering and multimodal interaction fusion 基于多尺度异常区域滤波和多模态相互作用融合的点云路面三维裂缝分层检测框架

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-03 DOI: 10.1111/mice.70167

Jiayv Jing, Ling Ding, Xu Yang, Hang Cheng, Yazhen Qiu, Hainian Wang, Rauno Heikkilä

{"title":"A hierarchical framework for three-dimensional pavement crack detection on point clouds with multi-scale abnormal region filtering and multimodal interaction fusion","authors":"Jiayv Jing, Ling Ding, Xu Yang, Hang Cheng, Yazhen Qiu, Hainian Wang, Rauno Heikkilä","doi":"10.1111/mice.70167","DOIUrl":"10.1111/mice.70167","url":null,"abstract":"Early crack detection enables timely maintenance actions, which in turn help extend pavement life and reduce maintenance costs. Traditional 2D detection lacks detail, while 3D detection faces accuracy and efficiency challenges. This paper proposes a hierarchical crack detection framework—F2CrackDet-PCD (crack detection based on point cloud data with filtering and fusion). The framework adopts a pre-filtering and fine segmentation strategy (multi-scale anomaly region filtering [MARF]). First, the MARF uses point cloud characteristics to quickly identify potential crack regions. Then, an orthogonal projection converts 3D data into RGB, depth, and normal images, which are combined by MIF-CrackNet (multimodal interaction fusion) to enhance detection accuracy and robustness. Two datasets were developed: RoadScan-2228, capturing realistic road scenes, and CrackNet-1187, emphasizing densely cracked pavement. Experimental results show that the MARF achieves a recall of about 98% on both datasets. F2CrackDet-PCD achieves F1-scores of 75.0 on RoadScan-2228 and 78.2 on CrackNet-1187. F2CrackDet-PCD provides a solution of lane-level 3D point cloud crack detection for large-scale road detection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6577-6596"},"PeriodicalIF":9.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664568","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

Cover Image, Volume 40, Issue 29 封面图片，第40卷，第29期

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-03 DOI: 10.1111/mice.70178

引用次数: 0

Cover Image, Volume 40, Issue 29 封面图片，第40卷，第29期

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-03 DOI: 10.1111/mice.70180

引用次数: 0

Efficient and physically consistent generation of 2D concrete mesostructures via a regressor-guided diffusion framework 通过回归量引导扩散框架高效和物理一致地生成二维混凝土细观结构

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-02 DOI: 10.1111/mice.70176

Juntong Zhang, Xin Ruan, Airong Chen, Hongzhou Zeng, Yue Li

{"title":"Efficient and physically consistent generation of 2D concrete mesostructures via a regressor-guided diffusion framework","authors":"Juntong Zhang, Xin Ruan, Airong Chen, Hongzhou Zeng, Yue Li","doi":"10.1111/mice.70176","DOIUrl":"10.1111/mice.70176","url":null,"abstract":"Concrete mesostructure modeling is critical for simulation-driven material design, as it directly influences the accuracy of mechanical and durability analyses. However, conventional methods such as computed tomography–based reconstruction and aggregate sequential placement suffer from low efficiency and limited controllability. To address these challenges, this study proposes a regressor-guided conditional diffusion framework for 2D concrete mesostructure generation. By transforming continuous physical parameters into dynamic gradient fields, a separately trained regressor guides the denoising process to ensure strong compliance with aggregate gradation and particle size constraints. Experimental results demonstrate that this method significantly outperforms traditional conditional embedding models in terms of constraint accuracy, reducing aggregate volume fraction and particle size distribution errors by 12.6% and 31.6%, respectively. Additionally, the proposed framework achieves over two orders of magnitude greater efficiency than existing numerical techniques. Its modular design and fine-grained control capabilities establish a scalable, physically consistent solution for high-fidelity mesostructure generation in concrete and beyond.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6622-6637"},"PeriodicalIF":9.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658313","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

Traffic signal optimization for emissions mitigation in urban road networks with contraflow left-turn lanes 基于交通信号优化的城市反流左转弯道路网络排放控制

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-01 DOI: 10.1111/mice.70177

Xiao Chen, Yunqing Jia

引用次数: 0