Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Traffic estimation in work zones using a custom regression model and data augmentation","authors":"Ali Hassandokht Mashhadi, Abbas Rashidi, Masoud Hamedi, Nikola Marković","doi":"10.1111/mice.13413","DOIUrl":"https://doi.org/10.1111/mice.13413","url":null,"abstract":"Accurately estimating traffic volumes in construction work zones is crucial for effective traffic management. However, one of the key challenges transportation agencies face is the limited coverage of continuous count station (CCS) sensors, which are often sparsely located and may not be positioned directly on roads where construction work zones are present. This spatial limitation leads to gaps in traffic data, making accurate volume estimation difficult. Addressing this, our study utilized a custom regularized model and variational autoencoders (VAE) to generate synthetic data that improves traffic volume estimations in these challenging areas. The proposed method not only bridges the data gaps between sparse CCS sensors but also outperforms several benchmark models, as measured by mean absolute percentage error, root mean square error, and mean absolute error. Moreover, the effectiveness of VAE-augmented models in enhancing the precision and accuracy of traffic volume estimations further underscores the benefits of integrating synthetic data into traffic-modeling approaches. These findings highlight the potential of the proposed approach to enhance traffic volume estimation in construction work zones and assist transportation agencies in making informed decisions for traffic management.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"5 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929495","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":"Geometry physics neural operator solver for solid mechanics","authors":"Chawit Kaewnuratchadasorn, Jiaji Wang, Chul-Woo Kim, Xiaowei Deng","doi":"10.1111/mice.13405","DOIUrl":"https://doi.org/10.1111/mice.13405","url":null,"abstract":"This study developed Geometry Physics neural Operator (GPO), a novel solver framework to approximate the partial differential equation (PDE) solutions for solid mechanics problems with irregular geometry and achieved a significant speedup in simulation time compared to numerical solvers. GPO leverages a weak form of PDEs based on the principle of least work, incorporates geometry information, and imposes exact Dirichlet boundary conditions within the network architecture to attain accurate and efficient modeling. This study focuses on applying GPO to model the behaviors of complicated bodies without any guided solutions or labeled training data. GPO adopts a modified Fourier neural operator as the backbone to achieve significantly improved convergence speed and to learn the complicated solution field of solid mechanics problems. Numerical experiments involved a two-dimensional plane with a hole and a three-dimensional building structure with Dirichlet boundary constraints. The results indicate that the geometry layer and exact boundary constraints in GPO significantly contribute to the convergence accuracy and speed, outperforming the previous benchmark in simulations of irregular geometry. The comparison results also showed that GPO can converge to solution fields faster than a commercial numerical solver in the structural examples. Furthermore, GPO demonstrates stronger performance than the solvers when the mesh size is smaller, and it achieves over 3<span data-altimg=\"/cms/asset/df3c163a-e7c6-4e48-9813-d4de41ec9066/mice13405-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"93\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13405-math-0001.png\"><mjx-semantics><mjx-mo data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"times\" data-semantic-type=\"operator\"><mjx-c></mjx-c></mjx-mo></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:10939687:media:mice13405:mice13405-math-0001\" display=\"inline\" location=\"graphic/mice13405-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mo data-semantic-=\"\" data-semantic-role=\"unknown\" data-semantic-speech=\"times\" data-semantic-type=\"operator\">×</mo>$times$</annotation></semantics></math></mjx-assistive-mml></mjx-container> and 2<span data-altimg=\"/cms/asset/67f630b9-5c45-4ee8-a436-0fef5e94744b/mice13405-math-0002.png\"></span><mjx-container ctxtmenu_counter=\"94\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13405-math-0002.png\"><mjx-semantics><mjx-mo data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"times\" data-semantic-type=\"operator\"><mjx-c></mjx-c></mjx-mo></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math ","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"73 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924496","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":"Multi-stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification","authors":"Qinghua Guo, Weihang Gao, Qingzhao Kong, Xilin Lu","doi":"10.1111/mice.13414","DOIUrl":"https://doi.org/10.1111/mice.13414","url":null,"abstract":"Suspended ceiling systems constitute a pivotal non-structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi-stage warped panel detection (MWPD) method to automatically locate warped panels from two-dimensional images and quantify their deformation. First, the Deep Hough Transform (DHT) is employed to localize the runner line, after that, each detected line is expanded to a rectangular strip. Then ResNet18 classifies the strips as warped or intact. Those classified as warped will undergo Gabor and horizontal Sobel filters successively to highlight the curved edge. Subsequently, the Generalized Hough Transform (GHT) is used to locate pixel points on the curve, and fitting these points yields the pixel-level radius of curvature. Leveraging known orthogonal relationships and geometric dimensions of runners, pixel quantification is converted into physical maximum deflection. The experiments include two aspects: the first is conducted on a validation dataset to verify the localization stability, and the second is carried out on-site for quantification validation. Results demonstrate that the proposed MWPD method effectively localizes the warped panel, achieving an accuracy of 92.2% on the validation dataset. Additionally, the quantitative test has achieved an accuracy of approximately 85%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"28 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924497","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 graph attention reasoning model for prefabricated component detection","authors":"Manxu Zhou, Guanting Ye, Ka-Veng Yuen, Wenhao Yu, Qiang Jin","doi":"10.1111/mice.13373","DOIUrl":"https://doi.org/10.1111/mice.13373","url":null,"abstract":"Accurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the internal structural complexities of these components. Prefabricated composite panels, due to their complex structure—including small embedded parts and large-scale reinforcing rib—require high-precision, multiscale feature recognition. This study developed an instance segmentation model: a graph attention reasoning model (GARM) for prefabricated component detection, for the quality inspection of prefabricated concrete composite panels. First, a dataset of prefabricated concrete composite components was constructed to address the shortage of existing data and provide sufficient samples for training the segmentation network. Subsequently, after training on a self-built dataset of prefabricated concrete composite panels, ablation experiments and comparative tests were conducted. The GARM segmentation model demonstrated superior performance in terms of detection speed and model lightweighting. Its accuracy surpassed other models, with a mean average precision (mAP₅₀) of 88.7%. This study confirms the efficacy and reliability of the GARM instance segmentation model in detecting prefabricated concrete composite panels.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911799","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 2","authors":"","doi":"10.1111/mice.13410","DOIUrl":"https://doi.org/10.1111/mice.13410","url":null,"abstract":"","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"26 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908265","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 feature-based pavement image registration method for precise pavement deterioration monitoring","authors":"Zhongyu Yang, Mohsen Mohammadi, Haolin Wang, Yi-Chang (James) Tsai","doi":"10.1111/mice.13407","DOIUrl":"https://doi.org/10.1111/mice.13407","url":null,"abstract":"Over the past decade, pavement imaging systems, particularly 3D laser technology, have been widely adopted by transportation agencies for network-level pavement condition evaluations. State Highway Agencies, including Georgia Department of Transportation (DOT), Florida DOT, and Texas DOT, have been collecting pavement images for over 5 years. However, these multi-year pavement images have not been fully utilized for analyzing detailed pavement deterioration. One challenge is the accurate and efficient registration of multi-temporal pavement images. This study pioneers the use of feature-based methods to address this challenge. It evaluates various feature-based image registration methods, including both state-of-the-art and novel combinations of feature detectors and descriptors. These methods are rigorously assessed using hybrid “step-by-step” and “end-to-end” performance evaluation metrics, with a ground reference dataset containing 100 pavement image pairs featuring diverse crack types and varying year gaps. The results confirm the feasibility of using feature-based techniques to register multi-temporal pavement images. A novel combination of the AKAZE detector and the Binary Robust Independent Elementary Features (BRIEF) descriptor was identified as the best-performing method, successfully registering 96 out of 100 image pairs. This advancement enables pavement engineers to accurately monitor pavement deterioration using multi-temporal images.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"134 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905283","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":"Automatic steel girder inspection system for high-speed railway bridge using hybrid learning framework","authors":"Tao Xu, Yunpeng Wu, Yong Qin, Sihui Long, Zhen Yang, Fengxiang Guo","doi":"10.1111/mice.13409","DOIUrl":"https://doi.org/10.1111/mice.13409","url":null,"abstract":"The steel girder of high-speed railway bridges requires regular inspections to ensure bridge stability and provide a safe environment for railway operations. Unmanned aerial vehicle (UAV)-based inspection has great potential to become an efficient solution by offering superior aerial perspectives and mitigating safety concerns. Unfortunately, classic convolutional neural network (CNN) models suffer from limited detection accuracy or redundant model parameters, and existing CNN-based bridge inspection systems are only designed for a single visual task (e.g., bolt detection or rust parsing only). This paper develops a novel bi-task girder inspection network (i.e., BGInet) to recognize different types of surface defects on girder from UAV imagery. First, the network assembles an advanced detection branch that integrates the sparse attention module, extended efficient linear aggregation network, and RepConv to solve the small object with scarce samples and complete efficient bolt defect identification. Then, an innovative U-shape saliency parsing branch is integrated into this system to supplement the detection branch and parse the rust regions. Smoothly, a pixel-to-real-world mapping model utilizing critical UAV flight parameters is also developed and assembled to measure rust areas. Finally, extensive experiments conducted on the UAV-based bridge girder dataset show our method achieves better detection accuracy over the current advanced models yet remains a reasonably high inference speed. The superior performance illustrates the system can effectively turn UAV imagery into useful information.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"143 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887241","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":"Automatic classification of near-fault pulse-like ground motions","authors":"Hongwu Yang, Yingmin Li, Weihao Pan, Lei Hu, Shuyan Ji","doi":"10.1111/mice.13408","DOIUrl":"https://doi.org/10.1111/mice.13408","url":null,"abstract":"This study presents an automated, quantitative classification method for near-fault pulse-like ground motions, distinguishing between forward-directivity and fling-step (FS) motions. The method introduces two novel parameters—the pulse velocity ratio and pulse area ratio—which transform the classification standard from a qualitative to a quantitative framework. Combined with an enhanced pulse extraction technique that captures permanent displacement characteristics, these parameters significantly improve classification efficiency and repeatability. This automated approach overcomes the limitations of manual classification, providing reproducible results. The identified FS ground motions can be applied to the dynamic analysis of cross-fault structures, enhancing the reliability of seismic hazard assessments.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"29 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884772","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":"Infrared thermography and 3D pavement surface unevenness measurement algorithm for damage assessment of concrete bridge decks","authors":"Mikiko Yamashita, Koichi Kawanishi, Kenji Hashizume, Pang-jo Chun","doi":"10.1111/mice.13406","DOIUrl":"https://doi.org/10.1111/mice.13406","url":null,"abstract":"Deterioration of the concrete deck surface, including disintegration and delamination between the deck slab and pavement, presents significant challenges in bridge maintenance due to its hidden nature and the risk it poses to the deck's durability as damage progresses. Early detection is critical for preventing issues such as pothole formation and ensuring long-term durability. However, traditional methods require core sampling, which often delays detection until damage is extensive. This study proposes a nondestructive approach combining infrared thermography (IRT) and laser-based surface profiling to improve early detection of subsurface damage. IRT captures temperature variations on the pavement surface, detecting horizontal voids and moisture, while laser profiling refines the detection of deeper, progressive damage. By integrating these two methods, the technique offers a comprehensive assessment that single-method approaches cannot provide. Field validation demonstrates that this method enables precise evaluation of bridge deck conditions, contributing to safer and more efficient bridge maintenance.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"57 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884771","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 1","authors":"","doi":"10.1111/mice.13404","DOIUrl":"https://doi.org/10.1111/mice.13404","url":null,"abstract":"<b>The cover image</b> is based on the article <i>Deep neural network based time–frequency decomposition for structural seismic responses training with synthetic samples</i> by Ranting Cui et al., https://doi.org/10.1111/mice.13242.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874203","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}