{"title":"Cover Image, Volume 40, Issue 17","authors":"","doi":"10.1111/mice.70005","DOIUrl":"https://doi.org/10.1111/mice.70005","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Semi-supervised pipe video temporal defect interval localization</i> by Zhu Huang et al., https://doi.org/10.1111/mice.13403.\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 17","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537116","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":"Advanced low-light image transformation for accurate nighttime pavement distress detection","authors":"Yuanyuan Hu, Hancheng Zhang, Yue Hou, Pengfei Liu","doi":"10.1111/mice.70001","DOIUrl":"https://doi.org/10.1111/mice.70001","url":null,"abstract":"Pavement distress detection is critical for road safety and infrastructure longevity. Although nighttime inspections offer advantages such as reduced traffic and enhanced operational efficiency, challenges like low visibility and noise hinder their effectiveness. This paper presents IllumiShiftNet, a novel model that transforms low-light images into high-quality, daylight-like representations for pavement distress detection. By employing unpaired image translation techniques, aligned nighttime–daytime datasets are generated for supervised training. The model integrates a lightEnhance generator, multiscale feature discriminators, and distress-focused loss function, ensuring accurate reconstruction of critical pavement details. Experimental results show that IllumiShiftNet achieves a state-of-the-art peak signal-to-noise ratio of 28.5 and a structural similarity index measure of 0.78, enabling detection algorithms trained on daytime data to perform effectively on nighttime imagery. The model demonstrates robust performance across varying illuminance levels, adverse weather conditions, and diverse road types while maintaining real-time processing capabilities. These results establish IllumiShiftNet as a practical solution for nighttime pavement monitoring.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"10 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479437","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 step toward a micromechanics-informed neural network for predicting asphalt mixture stiffness","authors":"Kumar Anupam, Mohammadjavad Berangi, Juan Camilo Camargo, Cor Kasbergen, Sandra Erkens","doi":"10.1111/mice.70000","DOIUrl":"10.1111/mice.70000","url":null,"abstract":"<p>Asphalt mixtures show complex mechanical behavior due to their heterogeneous structure. Traditionally, the mechanical characterization of asphalt mixture is done through laboratory testing or micromechanical modeling. While laboratory tests and micromechanical models provide reliable measurements and physical interpretability, they are often resource-intensive and demand extensive calibration. Recent advances in machine learning address some of the above issues by enabling accurate predictions, though often lacking physical interpretability and stability. Hence, this study aims to present a novel micromechanics-infused neural network (MINN) framework for predicting asphalt mixture stiffness. The framework embeds micromechanical principles derived from the modified Hirsch model into the neural network's loss function, allowing the model to learn from experimental data while adhering to micromechanics-based constraints. In this study, feature selection is performed using BorutaShap, and Bayesian optimization is applied for hyperparameter tuning. Results show that MINN improves prediction accuracy, interpretability, and robustness.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 23","pages":"3624-3651"},"PeriodicalIF":9.1,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144370709","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":"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":"10.1111/mice.13534","url":null,"abstract":"<p>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.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 18","pages":"2584-2611"},"PeriodicalIF":8.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13534","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cover Image, Volume 40, Issue 16","authors":"","doi":"10.1111/mice.13532","DOIUrl":"https://doi.org/10.1111/mice.13532","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>3D Data Generation of Manholes from Single Panoramic Inspection Images</i> by Mizuki Tabata et al., https://doi.org/10.1111/mice.13496.\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 16","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308723","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 16","authors":"","doi":"10.1111/mice.13531","DOIUrl":"https://doi.org/10.1111/mice.13531","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>A K-Net-based deep learning framework for automatic Rock Quality Designation (RQD) estimation</i> by Sihao Yu et al., https://doi.org/10.1111/mice.13386.\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 16","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308719","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}
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":"10.1111/mice.13535","url":null,"abstract":"<p>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 “<b>RL</b>-based <b>T</b>ask-<b>A</b>llocation and <b>P</b>ath-Finding under <b>U</b>ncertainty (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.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 22","pages":"3408-3429"},"PeriodicalIF":9.1,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13535","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"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":"10.1111/mice.13526","url":null,"abstract":"<p>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 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>17000</mn>\u0000 </mrow>\u0000 <annotation>$hskip.001pt 17000$</annotation>\u0000 </semantics></math>:<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation>$hskip.001pt 1$</annotation>\u0000 </semantics></math>, reducing the size of a bridge aerodynamics forced-vibration simulation from roughly <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>600</mn>\u0000 <mspace></mspace>\u0000 <mi>GB</mi>\u0000 </mrow>\u0000 <annotation>$600 ,mathrm{GB}$</annotation>\u0000 </semantics></math> to about <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>36</mn>\u0000 <mspace></mspace>\u0000 <mi>MB</mi>\u0000 </mrow>\u0000 <annotation>$36 ,mathrm{MB}$</annotation>\u0000 </semantics></math> while maintaining low reproduction errors, in most cases below <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.5</mn>\u0000 <mspace></mspace>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$0.5 ,%$</annotation>\u0000 </semantics></math>, 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 ","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 17","pages":"2434-2463"},"PeriodicalIF":8.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13526","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"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":"10.1111/mice.13533","url":null,"abstract":"<p>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.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 22","pages":"3451-3465"},"PeriodicalIF":9.1,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13533","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cover Image, Volume 40, Issue 15","authors":"","doi":"10.1111/mice.13529","DOIUrl":"https://doi.org/10.1111/mice.13529","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Aeroelastic force prediction via temporal fusion transformers</i> by Miguel Cid Montoya et al., https://doi.org/10.1111/mice.13381.\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 15","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237319","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}