Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Maintenance-oriented tunnel digital model generation via panoptic segmentation of ultra-high-resolution images","authors":"Zehao Ye, Qi Li, Giuseppe Desiderio, Mengqi Huang, Wenxi Liu, Valentina Villa, Jelena Ninić","doi":"10.1016/j.cacaie.2026.100032","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100032","url":null,"abstract":"Ultra-high-resolution (UHR) panoramic imaging enables detailed capture of tunnel surface conditions. Nevertheless, damage detection and reporting still rely on manual annotations after the inspection, as existing automated algorithms typically require downsampling (losing fine details) or patch-based processing (losing global context) to handle the massive computational load of UHR data. This trade-off restricts the effective use of such high-fidelity data, leaving comprehensive reporting reliant on manual annotation. To address this gap, a novel framework has been proposed that directly operates on UHR panoramic images for automated damage detection and 3D reconstruction employing Building Information Modelling to create digital model with annotated defects. At its core is a flexible segmentation architecture with a side network that enables context extraction from larger image patches, supporting panoptic segmentation of images over 6K resolution and accurate detection of tunnel components and five main damage types. With the Segment Anything Model 2 backbone, performance further improves, raising panoptic quality from 53.14 to 56.99. Industry Foundation Classes open data format has been expanded to support the standardized and interoperable representation of damaged tunnels, based on which an as-damaged BIM model is constructed to effectively record and visualize inspection outcomes and quantitative significance levels, thereby enhancing the efficiency of tunnel monitoring and management. The source code is publicly accessible at <ce:inter-ref xlink:href=\"https://github.com/zxy239/UHR-segmentation-for-tunnel\" xlink:type=\"simple\">https://github.com/zxy239/UHR-segmentation-for-tunnel</ce:inter-ref>.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"22 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147744163","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 optimization framework for automated design of offshore jacket structures under varied scenarios","authors":"Kang Ge, Chen Wang, Yu-Tao Guo, Xiao-Man Dong, Zhen-Zhong Hu","doi":"10.1016/j.cacaie.2026.100053","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100053","url":null,"abstract":"Conventional design methods for offshore jacket platforms rely heavily on engineers’ experience, resulting in conservative designs. This study employs a beam element-based topology optimization approach for jacket platforms, integrating multidisciplinary knowledge and simultaneously optimizing node coordinates and cross-sectional sizes. The objective is to minimize the weighted sum of volume and compliance. Given that marine loads are design-dependent, a differentiable formulation of Morison’s equation is developed. Pile-soil interaction is incorporated through a hybrid sensitivity scheme. Displacement constraints, diameter-to-thickness ratio constraints, and symmetry are considered. A Gumbel-Softmax-based strategy is employed to enable differentiable optimization of discrete standard sections for manufacturability. A member removal strategy is proposed to enable diversified design. The proposed method accommodates various multi-leg spatial frame configurations and can complete the design of a 4-leg jacket within 3-5 minutes. In addition, its effectiveness has been validated on a real engineering case.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"90 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147744165","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":"Rapid Post-Earthquake Assessment of Bridge Portfolios Using Damage-State Augmentation","authors":"Shengkui Di, Yaoyue Wang, Dong Yang, Francis T.K. Au, Yanhui Liu, Jing Zhang","doi":"10.1016/j.cacaie.2026.100061","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100061","url":null,"abstract":"Rapid portfolio-level post-earthquake bridge damage assessment is essential for restoring transportation networks and supporting emergency decisions, yet the current approaches face two persistent barriers. High-fidelity nonlinear analyses are computationally prohibitive at portfolio scale, while data-driven classifiers have been hindered by scarce and severely imbalanced damage-state labels, especially for severe damage. To address these gaps, this study presents a computational model consisting of two coupled components: a recurrent neural network surrogate for efficient response prediction and a gradient boosting-based classifier for damage-state identification. The surrogate learns the mapping from ground-motion features and bridge parameters to curvature-related response quantities, which are then used as physically meaningful features for classification. The classifier prioritizes the informative samples based on the predictive uncertainty and incorporates imbalance-mitigation strategies during training. Across five representative bridge types, the proposed model achieves test accuracies of 87.5%-96.5% for type-specific training and 83.1%-89.8% for joint cross-type training, demonstrating strong cross-type generalization.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"133 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147744164","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":"Online Closed-loop Traffic Signal Control Scheme Updating Using Learning-Based Turning Ratio Prediction","authors":"Jiarong Yao, Yun Lu, Yicheng Zhang, Rong Su, Wei Ma","doi":"10.1016/j.cacaie.2026.100048","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100048","url":null,"abstract":"Dynamic optimization strategies for large-scale network signal control typically require real-time traffic state to deal with variable demand patterns, yet a decentralized control strategy to control the network through the combination of intersection-level control is mostly adopted, which limits the solution to local optimum to some degree. On the other hand, though centralized fixed-time controller aims at global optimum, its essence of an open-loop one-shot control depends more on prediction accuracy of traffic state, meanwhile facing a dilemma between computation efficiency and solution quality. To fill this gap, this study proposed a closed-loop online updating control strategy for large-scale network, which keeps updating the parameters of a Cycle-based Adaptive Traffic Light Control (CATLC) model through learning-based turning ratio prediction. A model predictive control strategy is further used to develop a multi-cycle extended control model considering the demand distribution of traffic progression in a larger temporal-spatial scale. Simulation evaluation on a 56-intersection network in Singapore showed that the proposed method outperformed five baselines covering fixed-time control, actuated control and adaptive control methods. Specifically, an improvement 19% in total waiting time over the Sydney Coordinated Adaptive Traffic System (SCATS) scheme is obtained, while an superiority of 22% in average number of stops over a Deep Q Network-(DQN)-based method is obtained. The proposed GRU-based turning ratio predictor obtained an accuracy of about 84%, outperforming existing turning ratio predictors. Sensitivity analysis was also conducted regarding the control step size and the solution algorithm parameters, and results showed that a control step of 4 cycles was the optimal to realize global optimum in the case study, while the solution quality is more sensitive to population number than iteration number. With satisfactory solution quality and computation cost shown in case study, the method shows great prospect in real-time traffic control for large-scale roadway networks in practice.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"133 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147744173","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 Surrogate Model for Predicting Hysteresis Model Parameters for Earthquake Simulation of Elevated Transportation Infrastructure","authors":"Miguel A. Gómez, Matthew J. DeJong","doi":"10.1016/j.cacaie.2026.100046","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100046","url":null,"abstract":"Quick estimation of hysteresis model parameters is a necessary step when simulating the earthquake response of large inventories of structures. To this end, this paper introduces a Gaussian process (GP) based workflow aimed at predicting nonlinear model parameters to simulate the hysteresis behavior of reinforced concrete column plastic hinges. The workflow is presented through an application using a modified Bouc-Wen model. The GP is trained using a dataset of 290 experimental cyclic tests, ranging from columns with stable flexural failure modes to tests failing in shear with significant pinching and strength decay during cycling. Each test is characterized by a set of physical dimensionless predictors and a set of best-fit BW model parameters, obtained from calibrations performed with the Transitional Markov Chain Monte Carlo (TMCMC) algorithm. The BW model was enhanced with a new pinching initiation rule to better cover the multiple hysteresis shapes present in the database. Two GP surrogate models were then trained to map the non-dimensional parameters to the BW model parameters, one for flexure-dominated and another for shear-influenced failure mode. The model performance is evaluated through comparison of the hysteresis curves obtained using the GP-predicted BW model parameters, the curves resulting from the direct calibration procedure, and the real data observed in the experiments with a cross-validation procedure, holding out points in the dataset for testing. Overall, the GP surrogate model exhibits similar performance, in terms of mean average error in the hysteresis curves, as the direct calibration results from the TMCMC algorithm. Better performance is achieved for flexural failure modes than shear failure modes, due to the high variability of the pinching parameters across the dataset, and sparser data when it comes to shear-influenced tests. A sample application is developed to show how the GP surrogate model can be used to rapidly generate medium-fidelity non-linear structural models to simulate the response of cantilever concrete columns from a few easy-to-compute geometric parameters as inputs, and how the responses compare to a higher-fidelity FEM model. The introduced workflow and the presented model were developed with the specific aim of enabling medium-fidelity simulation of structural responses for a large, elevated transportation infrastructure inventory through time-history analyses, for which quick model creation and efficient simulations are needed.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"100 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147744170","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":"Imaging the Internal Structure of the Existing Bridge Pile Foundation Using Reverse Time Migration","authors":"Huihui Zhang, Tian Chen, Xiaolin Zhang, Muyu Liu, Peimin Zhu","doi":"10.1016/j.cacaie.2026.100043","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100043","url":null,"abstract":"This paper proposes a novel three-dimensional (3D) computational imaging framework based on elastic wave observations for assessing the internal integrity of in-service bridge pile foundations. While conventional detection methods rely on invasive borehole measurements and ray-based tomography, our approach introduces a high-fidelity imaging paradigm by integrating the Spectral Element Method (SEM) for elastic wavefield simulation and extrapolation with Reverse Time Migration (RTM) for structural reconstruction. The primary methodological contribution lies in the development of a full-wavefield imaging strategy that effectively manages the complex elastic wave scattering, reflections, and diffractions induced by the bridge superstructure (e.g., piers and decks). By leveraging the high-order accuracy of SEM in complex geometries, the proposed computational model captures the complete physics of elastic wave propagation in the pile-soil-superstructure system. Numerical experiments demonstrate that this SEM-RTM framework can reconstruct high-resolution 3D images of pile defects using only non-invasive, superstructure-based sensor data. The results show that the method significantly outperforms traditional PST and cross-hole tomography in terms of spatial resolution and its ability to image multiple piles (pile group) simultaneously within a single measurement. This work provides a robust computational tool for the non-destructive visualization and quantitative evaluation of deep foundation systems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"100 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147744166","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 Interpretable Framework for Tornado Loss Estimation Using Parametric Surrogate Models and Dual-Objective Adaptive Smart Sampling","authors":"Mohamad Habibnia, John W. van de Lindt","doi":"10.1016/j.cacaie.2026.100047","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100047","url":null,"abstract":"Tornadoes pose a significant threat to communities due to their sudden nature, often causing widespread destruction, economic losses, and fatalities. Although the existing tornado damage models provide a rigorous damage assessment methodology, they are limited to a few archetypes and cannot capture the diverse loss patterns across different building geometric configurations. Additionally, these models lack the generalizability needed for use by practitioners and various stakeholders. This study addresses these limitations by developing parametric loss models for one-story residential buildings with gable and hip roofs, demonstrating a methodology that can be generalized to other building configurations. This study employs a previously developed physics-informed probabilistic methodology along with ASCE7-22 tornado guidelines and probabilistic approaches to generate loss curves for buildings with various attributes. To provide vulnerability models in the simplest and most practical form, mathematically interpretable representations of the loss curves were targeted. A representative subset of building configurations was selected using a dual-objective adaptive smart sampling method, ensuring efficient coverage of input and output variable spaces. Various statistical formulations served as surrogates, with linear regressions established between the building properties and the surrogate parameters. While all models showed acceptable accuracy, scaled sigmoid and scaled Weibull CDF formulations were identified as the best representation for vulnerability models of residential buildings with gable and hip roofs, respectively. The result is a simple and concise framework that is applicable to a wide range of stakeholders in the built environment.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"69 1","pages":"100047"},"PeriodicalIF":11.775,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147752958","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":"Automated Design Optimization Framework for Geometrically Nonlinear and Dynamic Structures based on A Differentiable Finite Particle Method","authors":"Yafeng Wang, Xian Xu, Ying Yu, Yaozhi Luo","doi":"10.1016/j.cacaie.2026.100052","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100052","url":null,"abstract":"Structural design optimization accounting for geometric nonlinearity and dynamic effects remains challenging. Conventional finite element method (FEM)–based approaches often suffer from convergence difficulties and unreliable sensitivity evaluation when large deformations or instability phenomena occur. Moreover, the need for problem-specific formulations and different numerical schemes for static versus dynamic or linear versus nonlinear analyses further complicates gradient-based optimization. To address these issues, this study develops a finite particle method (FPM)–based optimization framework that provides a unified formulation for static and dynamic analyses, naturally accommodating large deformations and instability behaviors within a single explicit time-stepping scheme. Automatic differentiation (AD) is embedded directly into the FPM time-marching process, enabling fully automated and stable sensitivity evaluation without manual derivation or solver-specific adjoint formulations. Benchmark examples involving global buckling, snap-through instability, and dynamic loading demonstrate the effectiveness of the proposed approach. For optimization problems involving snap-through instability where FEM-based methods fail to converge or yield unreliable sensitivities, the proposed framework successfully guides the optimization and reduces structural compliance by over 98%, effectively preventing instability. For large-scale spatial trusses with up to 10,000 degrees of freedom under dynamic loading, computational time is reduced by more than 95% compared with FEM-based optimization. These results highlight the potential of the proposed framework for the optimization of structures subject to large deformations, instability, and dynamic excitations, providing clear numerical and practical advantages.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"6 1","pages":"100052"},"PeriodicalIF":11.775,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147752623","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 31","authors":"","doi":"10.1111/mice.70188","DOIUrl":"10.1111/mice.70188","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Emergency response vehicle routing allowing lane straddling in congested traffic conditions under connected and autonomous vehicle environment</i> by Jiyoung Kim et al., https://doi.org/10.1111/mice.70168.\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 31","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778089","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 31","authors":"","doi":"10.1111/mice.70185","DOIUrl":"10.1111/mice.70185","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>A method for detecting construction deviations in large and complex building structures utilizing synthetic point clouds for segmentation</i> by Jia Zou et al., https://doi.org/10.1111/mice.70171.\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 31","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777826","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}