Computer-Aided Civil and Infrastructure Engineering最新文献

The cover image is based on the article Emergency response vehicle routing allowing lane straddling in congested traffic conditions under connected and autonomous vehicle environment by Jiyoung Kim et al., https://doi.org/10.1111/mice.70168.

The cover image is based on the article A method for detecting construction deviations in large and complex building structures utilizing synthetic point clouds for segmentation by Jia Zou et al., https://doi.org/10.1111/mice.70171.

The cover image is based on the article Large language model for post-earthquake structural damage assessment of buildings by Yongqing Jiang et al., https://doi.org/10.1111/mice.70010.

The cover image is based on the article Reinforcement response prediction of composite-concrete beams with crack patterns and deep learning by Sike Wang et al., https://doi.org/10.1111/mice.70174.

The cover image is based on the article Progressive development of cracks in biochar-cement composites through multiscale analysis by Muduo Li et al., https://doi.org/10.1111/mice.70090.

The cover image is based on the article 4D spatial-temporal information infrastructure for digital twin environments using Spatial IDs by Kenji Nakamura et al., https://doi.org/10.1111/mice.70146.

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.

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.

The cover image is based on the article Hierarchical adaptive cross-coupled control of traffic signals and vehicle routes in large-scale road network by Yizhuo Chang et al., https://doi.org/10.1111/mice.13508.

The cover image is based on the article Efficient unsupervised domain adaptation for crack segmentation with interpretable Fourier– Morphology blending and Uncertainty-guided selftraining by Saheli Bhattacharya et al., https://doi.org/10.1111/mice.70127.