Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"A Hybrid-Driven Method Based on Dynamic and Static Responses for Dynamic Girder Strain Reconstruction of Long-Span Suspension Bridges","authors":"Zaiyang Jiang, Qianen Xu, Qingfei Gao, Yang Liu","doi":"10.1016/j.cacaie.2026.100213","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100213","url":null,"abstract":"In structural health monitoring (SHM) of long-span bridges, distributed optical fiber sensing technology can provide strain information with high spatial resolution. However, due to the inherently low sampling frequency of the system, it is difficult to capture the high-frequency dynamic strain responses of bridges under complex operational environments. To address this hardware bottleneck, a hybrid-driven method based on dynamic and static responses for dynamic girder strain reconstruction of long-span suspension bridges is proposed. First, a joint state estimation framework based on variational mode decomposition (VMD) and a Kalman filter is established. This framework utilizes high-frequency acceleration as a dynamic prior while treating low-frequency deflection measurements as absolute constraints, effectively resolving the persistent issue of low-frequency drift caused by uncertain initial conditions in double integration. Second, a multi-task BP neural network is introduced to decode the complex, non-linear spatiotemporal mapping between global girder deflection and localized strain fields, thereby mapping the reconstructed dynamic deflection field to high-frequency dynamic strains at multiple cross-sections of the entire bridge.. The proposed method is validated using numerical simulation and actual monitoring data of a long-span suspension bridge. Results demonstrate that compared to conventional identification methods relying solely on acceleration integration, the proposed approach reduces the maximum root-mean-square error (RMSE) and mean absolute error (MAE) of the reconstructed dynamic strains by up to 50% and 51%, respectively. Furthermore, under Gaussian white noise interference as high as 15%, the method still maintains highly consistent strain reconstruction trends, demonstrating excellent noise robustness.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"6 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884903","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 LiDAR-Camera Fusion Framework Assisted by Geometric Anomaly Perception for Real-Time Road Subsidence Detection","authors":"Fan Gu,Hai Li,Yucheng Huang,Di Wang","doi":"10.1016/j.cacaie.2026.100220","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100220","url":null,"abstract":"","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"12 1","pages":"100220"},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932509","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":"Computer-Aided Pitting Corrosion Segmentation of Steel Surfaces Using a Geometry-Guided Hybrid Two-Stage W-Net-Transformer","authors":"Xu Jiang,Hao Qi,Xuhong Qiang,Daqian Cao,Shenpeng Sun,Feng Lin","doi":"10.1016/j.cacaie.2026.100221","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100221","url":null,"abstract":"","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"9 1","pages":"100221"},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932508","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":"AdvSerial: Physical adversarial attacks on infrastructure-mounted pedestrian detectors via semantic feature suppression","authors":"Yuanhao Huang,Yilong Ren,Jinlei Wang,Xuesong Bai,Jinchuan Zhang,Haiyang Yu","doi":"10.1016/j.cacaie.2026.100215","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100215","url":null,"abstract":"","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 1","pages":"100215"},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894873","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":"Multiscale Geographical-Temporal Machine Learning for Traffic Safety Modeling","authors":"Shaobin Yang,Pengfei Cui,Lei Han,Lishan Sun,Yang Yang,Xingchen Zhang","doi":"10.1016/j.cacaie.2026.100208","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100208","url":null,"abstract":"","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"28 1","pages":"100208"},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934106","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":"Stability-constrained operation planning for multi-support construction equipment under load and configuration variations","authors":"Xiang Li, Zhe Suo, Feifan Pan, Jixin Wang, Fei Xie","doi":"10.1016/j.cacaie.2026.100209","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100209","url":null,"abstract":"For multi-support construction equipment operating in complex environments, load and configuration variations can induce significant whole-equipment CoM fluctuations that directly affect operational stability. Existing stability analysis methods are generally unable to simultaneously accommodate multiple constraints or explicitly account for CoM fluctuations, limiting their applicability to operation planning. To address these limitations, this paper proposes a multi-constraint three-dimensional quasi-static stability analysis and operation planning framework for multi-support construction equipment. The CoM fluctuation region induced by load and configuration variations is first characterized and regularized using a minimum enclosing sphere. A three-dimensional quasi-static stability boundary is then constructed by jointly considering force and moment equilibrium, kinematic constraints, actuation constraints, support conditions, and ground conditions. Together, these formulations transform equipment stability analysis into a feasibility problem of the CoM position and enable stability to be directly incorporated into operation planning. The proposed operation planning framework is applied to support-layout evaluation for static-support equipment, and to support-point feasibility evaluation, mobile-body target position and trajectory planning for the walking transition of mobile-support equipment. Simulations and prototype experiments on flat, uneven, and sloped terrain show that a quasi-static stability boundary and a trajectory between two target positions can be generated in approximately 2 s and 3.5 s, respectively, while achieving zero boundary violations under the adopted discrete verification. These results demonstrate the effectiveness of the proposed framework, which has the potential to further enhance the practicality, robustness, and autonomy of multi-support construction equipment operating in complex environments.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884905","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 differentiable physics solver for pavement mechanical responses","authors":"Dayong Jiang, Dongsheng Wang, Zhongshi Pei, Wenyi Zhou, Zhaojie Sun, Decheng Feng, Junyan Yi","doi":"10.1016/j.cacaie.2026.100212","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100212","url":null,"abstract":"Engineering systems increasingly rely on data-driven optimization and closed-loop decision-making. Accordingly, computational models in engineering are no longer only tools for forward analysis, but also need to support parameter updating, design optimization, and feedback decision-making. In contrast, existing pavement mechanics computation still mainly serves response prediction under given parameter conditions and lacks a dedicated differentiable physics solver that directly provides stable gradient information. To address this gap, this study develops <mml:math altimg=\"si22.svg\"><mml:mi>∂</mml:mi></mml:math>Pave, a differentiable physics solver for pavement mechanical response computation. <mml:math altimg=\"si22.svg\"><mml:mi>∂</mml:mi></mml:math>Pave builds on a spectral element forward solution framework for layered pavement systems under moving loads. The solver connects load, material, geometric, and evaluation parameters to explicit stages of an end-to-end differentiable spectral element method solution path. Within this unified physical framework, <mml:math altimg=\"si22.svg\"><mml:mi>∂</mml:mi></mml:math>Pave returns selected mechanical responses at specified points in the layered pavement coordinate system together with their gradients with respect to specified input parameters. This study verifies the computed gradients through examples involving multiple response quantities. The results show that the automatic differentiation gradients from <mml:math altimg=\"si22.svg\"><mml:mi>∂</mml:mi></mml:math>Pave are generally consistent with finite difference results. In multi-parameter joint differentiation tasks, the efficiency advantage of <mml:math altimg=\"si22.svg\"><mml:mi>∂</mml:mi></mml:math>Pave over parameter-by-parameter finite differences increases with the number of differentiated parameters. This study further illustrates the use of the same response-gradient interface in two gradient-driven pavement engineering workflows, namely pavement structural design optimization and measurement-point layout optimization for pavement inspection. The two examples use task-specific variables, objectives, constraints, and decision procedures outside the physics solver. They demonstrate that <mml:math altimg=\"si22.svg\"><mml:mi>∂</mml:mi></mml:math>Pave can serve as a reusable physics-based response-gradient provider for different pavement mechanics task formulations.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"52 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884949","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 Computer-Aided Probabilistic Framework for Full-Field Seismic Response Assessment of Concrete Dams","authors":"Jingbo Hao,Mengxi Zhang,Yunguo Cheng,Jingyi Zhang,Chensen Ding,Mingchao Li","doi":"10.1016/j.cacaie.2026.100222","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100222","url":null,"abstract":"","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"77 1","pages":"100222"},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932507","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":"Physically consistent and domain-adaptive vehicle state prediction via kolmogorov–arnold networks with a differentiable dynamics prior","authors":"Dingrui Xue,Wanlong Qi,Zhipeng Zhang,Yuqing Lin,Kang Sun,Xingye Han,Bo Li,Jiancheng Liu,Cheng Zhang,Tuo Wang,Kexin Wang","doi":"10.1016/j.cacaie.2026.100216","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100216","url":null,"abstract":"","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"28 1","pages":"100216"},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894874","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":"ASPECT: Adaptive spatio-temporal prediction of evolving crack traces","authors":"Rui Zhang, Gang Zheng, Bing Tang, Yu Diao, Xinxin Song","doi":"10.1016/j.cacaie.2026.100214","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100214","url":null,"abstract":"Automated crack analysis remains largely reactive: most methods delineate damage only after it has been observed, whereas maintenance planning would benefit from evidence about how visible crack patterns may evolve between inspections. Forecasting future crack traces is difficult because crack pixels are sparse and thin, small discontinuities can change structural interpretation, and uncertainty increases with prediction horizon. We introduce the Crack Predictor benchmark and ASPECT (Adaptive Spatio-temporal Prediction of Evolving Crack Traces), a non-autoregressive framework that predicts four future crack masks directly from 16 observed RGB frames. ASPECT employs a compact StarNet spatial backbone augmented with Haar Wavelet Downsampling for frequency-aware encoding, ConvLSTM for temporal modeling on a compressed two-dimensional feature grid, and a Geometry-adaptive Decoding Head for reconstructing thin, irregular crack masks. On the validation split, ASPECT achieves 90.33% mIoU, 94.66% mF1, and 80.91% crack-class IoU with 0.32 M trainable parameters. It also ranks first in topology sensitivity and endpoint precision, recall, and F1, reaching an endpoint F1 of 66.56%. Across the four prediction horizons, ASPECT retains the highest Crack F1 among the evaluated methods. These results establish ASPECT as a compact, topology-aware framework for short-horizon crack forecasting on the controlled multi-temporal benchmark.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"43 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884902","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}