Computer-Aided Civil and Infrastructure Engineering最新文献

Prediction of the most fire‐sensitive point in building structures with differentiable agents for thermal simulators 热模拟器用可微分剂预测建筑结构最火敏感点

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-19 DOI: 10.1111/mice.13534

Yuan Xinjie, Khalid M. Mosalam

{"title":"Prediction of the most fire‐sensitive point in building structures with differentiable agents for thermal simulators","authors":"Yuan Xinjie, Khalid M. Mosalam","doi":"10.1111/mice.13534","DOIUrl":"https://doi.org/10.1111/mice.13534","url":null,"abstract":"Fire safety is crucial for ensuring the stability of building structures, yet evaluating whether a structure meets fire safety requirements is challenging. Fires can originate at any point within a structure, and simulating every potential fire scenario is both expensive and time‐consuming. To address this challenge, we propose the concept of the most fire‐sensitive point (MFSP) and an efficient machine learning framework for its identification. The MFSP is defined as the location at which a fire, if initiated, would cause the most severe detrimental impact on the building's stability, effectively representing the worst‐case fire scenario. In our framework, a graph neural network serves as an efficient and differentiable agent for conventional finite element analysis simulators by predicting the maximum interstory drift ratio under fire, which then guides the training and evaluation of the MFSP predictor. Additionally, we enhance our framework with a novel edge update mechanism and a transfer learning‐based training scheme. Evaluations on a large‐scale simulation dataset demonstrate the good performance of the proposed framework in identifying the MFSP, offering a transformative tool for optimizing fire safety assessments in structural design. All developed datasets and codes are open‐sourced online.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144319895","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}

引用次数: 0

Cover Image, Volume 40, Issue 16 封面图片，第40卷，第16期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-18 DOI: 10.1111/mice.13532

引用次数: 0

Cover Image, Volume 40, Issue 16 封面图片，第40卷，第16期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-18 DOI: 10.1111/mice.13531

引用次数: 0

Reinforcement learning–based task allocation and path‐finding in multi‐robot systems under environment uncertainty 环境不确定性下基于强化学习的多机器人系统任务分配与寻径

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-15 DOI: 10.1111/mice.13535

Songjun Huang, Chuanneng Sun, Jie Gong, Dario Pompili

{"title":"Reinforcement learning–based task allocation and path‐finding in multi‐robot systems under environment uncertainty","authors":"Songjun Huang, Chuanneng Sun, Jie Gong, Dario Pompili","doi":"10.1111/mice.13535","DOIUrl":"https://doi.org/10.1111/mice.13535","url":null,"abstract":"Autonomous robots have the potential to significantly improve the operational efficiency of multi‐robot systems (MRSs) under environment uncertainties. Achieving robust performance in these settings requires effective task allocation and adaptive path‐finding. However, conventional model‐based frameworks often rely on centralized control or global information, making them impractical when communication is intermittent or maps are unavailable. Although recent studies have shown that reinforcement learning (RL)‐based frameworks offer improved performance, problems related to synchronization and adaptability in diverse environments remain unresolved. To address these problems, this study proposes the “RL‐based Task‐Allocation and Path‐Finding under Uncertainty (RL‐TAPU)” framework. This framework incorporates an Action‐Selective Double‐Q‐Learning (ASDQ) algorithm for real‐time task allocation and a Context‐Aware Meta‐Q‐Learning (CA‐MQL) algorithm for adaptive path‐finding. Unlike previous RL‐based frameworks, RL‐TAPU is designed to operate without global maps, uses only local state information, and functions reliably under intermittent and low‐bandwidth communication conditions. The task allocator communicates only minimal information, and the path‐finding component adapts to new environments without the need for complete environmental data. Experimental results show that the RL‐TAPU framework achieves better adaptability and works more efficiently with a shorter total execution time than competitors.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"13 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289870","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}

引用次数: 0

Signed distance function–biased flow importance sampling for implicit neural compression of flow fields 流场隐式神经压缩的有符号距离函数偏流重要性采样

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-13 DOI: 10.1111/mice.13526

Omar A. Mures, Miguel Cid Montoya

{"title":"Signed distance function–biased flow importance sampling for implicit neural compression of flow fields","authors":"Omar A. Mures, Miguel Cid Montoya","doi":"10.1111/mice.13526","DOIUrl":"https://doi.org/10.1111/mice.13526","url":null,"abstract":"The rise of exascale supercomputing has motivated an increase in high‐fidelity computational fluid dynamics (CFD) simulations. The detail in these simulations, often involving shape‐dependent, time‐variant flow domains and low‐speed, complex, turbulent flows, is essential for fueling innovations in fields like wind, civil, automotive, or aerospace engineering. However, the massive amount of data these simulations produce can overwhelm storage systems and negatively affect conventional data management and postprocessing workflows, including iterative procedures such as design space exploration, optimization, and uncertainty quantification. This study proposes a novel sampling method harnessing the signed distance function (SDF) concept: SDF‐biased flow importance sampling (BiFIS) and implicit compression based on implicit neural network representations for transforming large‐size, shape‐dependent flow fields into reduced‐size shape‐agnostic images. Designed to alleviate the above‐mentioned problems, our approach achieves near‐lossless compression ratios of approximately :, reducing the size of a bridge aerodynamics forced‐vibration simulation from roughly to about while maintaining low reproduction errors, in most cases below , which is unachievable with other sampling approaches. Our approach also allows for real‐time analysis and visualization of these massive simulations and does not involve decompression preprocessing steps that yield full simulation data again. Given that image sampling is a fundamental step for any image‐based flow field prediction model, the proposed BiFIS method can significantly improve the accuracy and efficiency of such models, helping any application that relies on precise flow field predictions. The BiFIS code is available on GitHub.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"22 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288350","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}

引用次数: 0

Remote measurement of reinforcing bar spacing and length from an oblique photograph using a novel perspective correction technique 利用一种新颖的视角校正技术，从斜向照片中远程测量钢筋间距和长度

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-13 DOI: 10.1111/mice.13533

Jun Su Park, Jae Young Kang, Hyo Seon Park

{"title":"Remote measurement of reinforcing bar spacing and length from an oblique photograph using a novel perspective correction technique","authors":"Jun Su Park, Jae Young Kang, Hyo Seon Park","doi":"10.1111/mice.13533","DOIUrl":"https://doi.org/10.1111/mice.13533","url":null,"abstract":"The dimensional inspection of reinforcing bars at construction sites prior to concrete pouring is essential to ensure structural integrity. However, this process has traditionally relied on manual tape measurements, which are labor‐intensive, unsafe, and prone to human error. To address these limitations, this study introduces a novel method for remotely inspecting the spacings and lengths of reinforcing bars using a single oblique photograph and a new perspective correction technique. This method transforms oblique images into vertical images using constraints based on four specific vectors that must be perpendicular to one another, thereby eliminating the need for the four‐point correspondence required by existing methods. This improvement enhances the practicality of the proposed method. A validation experiment conducted at an apartment construction site yielded a mean absolute error of 5.12 mm in measuring the spacing and length of reinforcing bars, demonstrating field‐level accuracy in compliance with American Concrete Institute 117 and the Gagemaker's Rule from US military standard 120.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"30 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288298","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}

引用次数: 0

Cover Image, Volume 40, Issue 15 封面图片，第40卷，第15期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-08 DOI: 10.1111/mice.13529

引用次数: 0

Cover Image, Volume 40, Issue 15 封面图片，第40卷，第15期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-08 DOI: 10.1111/mice.13530

引用次数: 0

An interval prediction system for track irregularity after tamping based on multi-module machine learning and pointwise scaling approach 基于多模块机器学习和点尺度法的轨道夯实后不规则度区间预测系统

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-04 DOI: 10.1111/mice.13504

Yuxiao Zhang, Jin Shi, José Nuno Varandas, Youkang Ding

{"title":"An interval prediction system for track irregularity after tamping based on multi-module machine learning and pointwise scaling approach","authors":"Yuxiao Zhang, Jin Shi, José Nuno Varandas, Youkang Ding","doi":"10.1111/mice.13504","DOIUrl":"https://doi.org/10.1111/mice.13504","url":null,"abstract":"Predicting changes in track irregularity after tamping is important for assisting maintenance decisions and improving construction efficiency. To date, most prediction methods lack consideration for the uncertainties related to tamping effects. To fill this gap, a multi-module prediction interval system composed of feature selection, interval scaling, and intelligent predictor has been constructed. The feature selection module integrates the processes of relevance, redundancy, complementarity, and weighting. The interval scaling module assigns scaling factors to each point in a data-driven manner, offering great flexibility. Research found that the composite model has significant advantages over traditional models, improving the interval coverage probability by 5.83%–40.62%. It can accurately predict the track relative smoothness after tamping, with the R<sup>2</sup> between the measurement and the prediction of the 60 m mid-chord offset reaching 0.95. This model can serve as a reliable and feasible tool for predicting the static irregularity of ballasted tracks after tamping.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219443","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}

引用次数: 0

Differential settlements monitoring in railway transition zones using satellite-based remote sensing techniques 基于卫星遥感技术的铁路过渡区差异沉降监测

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-06-04 DOI: 10.1111/mice.13511

J. N. Varandas, Y. Zhang, J. Shi, S. Davies, A. Ferreira

{"title":"Differential settlements monitoring in railway transition zones using satellite-based remote sensing techniques","authors":"J. N. Varandas, Y. Zhang, J. Shi, S. Davies, A. Ferreira","doi":"10.1111/mice.13511","DOIUrl":"https://doi.org/10.1111/mice.13511","url":null,"abstract":"Railway track transitions are prone to uneven settlements and track geometry degradation. Traditional monitoring methods are limited in coverage, which highlights the need for novel solutions. This study proposes a method that systematically integrates the high spatial resolution of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) with the broader coverage of Small Baseline Subset (SBAS). A correction method for abnormal InSAR time series is developed, considering both consecutive phase unwrapping errors as well as outlier displacements. Model parameters are optimized through Monte Carlo analysis embedded with grid search. The proposed PS-SBAS InSAR processing method is applied to generate the track longitudinal profile of a railway transition section and is compared with track inspection data. The results show: (1) the hybrid PS-SBAS approach provides higher resolution and robustness for tracking long-term differential settlement along railway tracks. (2) There is a strong correlation between track longitudinal level and the InSAR-derived profile in the bridge approaches with high differential settlement rates. (3) InSAR can serve as a complementary method to traditional inspections, capturing the progression of differential settlement and enhancing the understanding of long-term settlement patterns and their impact on track performance.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"38 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211088","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}

引用次数: 0