Computer-Aided Civil and Infrastructure Engineering最新文献_第3页

Cover Image, Volume 40, Issue 24 封面图片，第40卷，第24期

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-26 DOI: 10.1111/mice.70082

引用次数: 0

Uncertainty quantification in Bayesian physics-informed deep learning-based traffic state prediction 基于贝叶斯物理的深度学习交通状态预测中的不确定性量化

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-22 DOI: 10.1111/mice.70078

Chuan Ding, Yingjie Song, Hongliang Zhang, Ting Wang

{"title":"Uncertainty quantification in Bayesian physics-informed deep learning-based traffic state prediction","authors":"Chuan Ding, Yingjie Song, Hongliang Zhang, Ting Wang","doi":"10.1111/mice.70078","DOIUrl":"https://doi.org/10.1111/mice.70078","url":null,"abstract":"Accurate and reliable traffic state prediction (TSP) is an essential task for intelligent transportation systems. However, achieving this goal is challenging due to the high-dimensional and coupled nature of traffic feature evolution patterns, which are deeply recessive and make it difficult to effectively characterize and model TSP using purely data-driven methods. Furthermore, a significant limitation of existing TSP methods is their inability to estimate data and model uncertainty, which is crucial for understanding inherent data variations and model limitations. To address these challenges, this study proposes a novel TSP model that combines the diffusion convolutional recurrent neural network (DCRNN) with physical prior knowledge within a Bayesian framework. Specifically, DCRNN captures the spatiotemporal correlation among various sensors. Furthermore, this approach leverages Monte Carlo dropout and heteroskedasticity modeling to quantify epistemic and aleatoric uncertainties. The model's efficacy is evaluated using the Xuancheng China urban dataset and the PeMS04 US highway dataset. Empirical results show that the proposed method outperforms state-of-the-art methods in both prediction accuracy and uncertainty quantification. These findings highlight the advantages of a data-model hybrid-driven approach to achieve accurate and reliable TSP. This study effectively quantifies and mitigates both aleatoric and epistemic uncertainties, holding significant implications for the control and management of real traffic flow.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103807","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

Domain-adaptive self-supervised learning for corrosion detection and 3D building information model mapping in steel tunnels 基于领域自适应自监督学习的钢隧道腐蚀检测与三维建筑信息模型映射

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-19 DOI: 10.1111/mice.70077

Shreejan Maharjan, Shogo Inadomi, Kenta Itakura, Pang-jo Chun

{"title":"Domain-adaptive self-supervised learning for corrosion detection and 3D building information model mapping in steel tunnels","authors":"Shreejan Maharjan, Shogo Inadomi, Kenta Itakura, Pang-jo Chun","doi":"10.1111/mice.70077","DOIUrl":"https://doi.org/10.1111/mice.70077","url":null,"abstract":"Accurate detection and localization of steel corrosion in tunnel infrastructure remains a major challenge, particularly under conditions of variable lighting, limited accessibility, and visual domain shifts common in real-world inspection scenarios. This study presents a novel integrated framework that automates tunnel inspection by combining self-supervised deep learning, image-based three-dimensional reconstruction, and building information modeling (BIM)-based spatial damage localization. At the core of our approach is a Segformer-based, two-stage domain adaptation model, which leverages pseudo-labeling and confidence masking to improve generalization across visually diverse environments without requiring extensive labeled data. Unlike traditional supervised methods, our model achieves a mean intersection over union (mIoU) of 0.81 and an F1 score of 0.77, demonstrating superior robustness and generalization. Images captured via unmanned aerial vehicles and iPhones were processed to generate a dense point cloud, which was used to construct a three-dimensional (3D) BIM model of the tunnel structure. Corrosion regions were detected and precisely localized within the BIM coordinate system using a custom coordinate estimation method. The final outputs were compiled into a structured database for seamless digital asset management. Overall, the proposed framework offers a scalable, cost-effective, and highly adaptable solution that significantly reduces manual labor and inspection time, with strong potential for broader deployment in infrastructure condition monitoring and digital asset management.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"7 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089933","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

Anti-frequency long short-term memory model for stable estimation of structural response under noise conditions 噪声条件下结构响应稳定估计的抗频长短期记忆模型

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-18 DOI: 10.1111/mice.70074

S. Park, D. Y. Yun, H. S. Park

{"title":"Anti-frequency long short-term memory model for stable estimation of structural response under noise conditions","authors":"S. Park, D. Y. Yun, H. S. Park","doi":"10.1111/mice.70074","DOIUrl":"https://doi.org/10.1111/mice.70074","url":null,"abstract":"Deep learning models for structural response estimation exhibit degraded performance when the training and input data characteristics differ, particularly because of noise. This study proposes an anti-frequency long short-term memory (AF-LSTM) model designed to ensure a stable estimation regardless of noise conditions. The term “anti-frequency” is used to describe the process of suppressing predefined frequency components by setting them to zero in the frequency domain. The AF-LSTM model introduces an AF layer before the LSTM layer, which suppresses specific frequency components before learning. The AF layer transforms the input into the frequency domain, zeroes out the components within predefined noise-prone frequency bands, and converts the signal back to the time domain. This process enables LSTM to effectively learn and estimate structural responses with improved consistency, even under noise conditions. The proposed model was verified using a numerical three-degree-of-freedom system, demonstrating stable estimation performance under varying noise frequencies and amplitude ratios. Experimental validation on a three-story steel frame structure and acceleration data from a real 55-floor building with environmental noise confirmed the model's ability to estimate stable responses across non-stationary inputs.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"53 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084052","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

Supervised parameter updating of deformation analyses for rockfill dams using prior knowledge 基于先验知识的堆石坝变形分析的监督参数更新

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-18 DOI: 10.1111/mice.70070

Zhitao Ai, Gang Ma, Guike Zhang, Jiawei Wang, Zhihong Huang, Wei Zhou, Qigui Yang

{"title":"Supervised parameter updating of deformation analyses for rockfill dams using prior knowledge","authors":"Zhitao Ai, Gang Ma, Guike Zhang, Jiawei Wang, Zhihong Huang, Wei Zhou, Qigui Yang","doi":"10.1111/mice.70070","DOIUrl":"https://doi.org/10.1111/mice.70070","url":null,"abstract":"Accurate and reliable numerical simulation is crucial for the safe construction and operation of infrastructure such as rockfill dams. Model parameter updating through inverse analysis based on monitoring data is key to improving analysis accuracy. However, existing parameter updating methods for dams often neglect parameter correlations, resulting in discrepancies between the joint distribution of updated parameters and experimental data. Besides, conventional parameter updating methods exhibit considerable randomness, resulting in non‐unique updated parameters. These factors limit the improvement of analysis accuracy and even lead to the failure of analysis convergence. Thus, this study proposes a parameter updating method for deformation analysis of rockfill dams based on surrogate‐assisted optimization. Innovatively, the multivariate distribution of experimental data of model parameters is incorporated as prior knowledge to supervise the parameter updating. Specifically, a multivariate distribution model of experimental data from 48 rockfill dams worldwide is constructed using multivariate copula function. Then the joint probability density function is integrated into the optimization process through population preselection mechanism and penalty function, guiding the updated parameters to align with the experimental joint distribution. The application to an ultra‐high rockfill dam demonstrated that this scheme effectively identified multiple optimal parameters from the constitutive model. With the supervision of prior knowledge, the updated parameters K, n, Kb, and m of the Duncan–Chang E‐B model showed strong consistency with the multivariate joint distribution derived from experimental data. This scheme improved the accuracy of the deformation analysis model by 16%, thereby providing critical support for dam safety assessment.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"123 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084289","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

Image-based large language model approach to road pavement monitoring 基于图像的道路路面监测大语言模型方法

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-18 DOI: 10.1111/mice.70075

Shuoshuo Xu, Kai Zhao, James Loney, Zili Li, Andrea Visentin

{"title":"Image-based large language model approach to road pavement monitoring","authors":"Shuoshuo Xu, Kai Zhao, James Loney, Zili Li, Andrea Visentin","doi":"10.1111/mice.70075","DOIUrl":"https://doi.org/10.1111/mice.70075","url":null,"abstract":"Accurate and rapid assessment of pavement surface condition is essential for maintaining transportation safety and minimizing vehicle wear. Manual pavement inspections are subjective and time-consuming, and machine learning methods typically require large labeled datasets. This study introduces an innovative zero-shot learning method that leverages large language models’ (LLMs) image analysis and natural-language understanding capabilities for accurate road condition assessment. Prompts were designed in alignment with the pavement surface condition index criteria to generate multiple evaluation models, which were then compared against official scores to identify an optimized configuration. Tests conducted using Google Street View imagery indicate that the optimized LLM-based model achieves a mean absolute error of 1.07 on a 0–10 scale, outperforming expert evaluations. The proposed approach enables rapid, accurate, and consistent assessments without the need for labeled data, demonstrating the transformative role of LLMs in automating infrastructure monitoring and emphasizing the importance of structured prompt engineering for reliable performance.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"4 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084053","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

A physics-informed train on synthetic and test on real method for evaluating large language model-generated safety-critical traffic scenarios 基于物理的综合训练和真实方法的测试，用于评估大型语言模型生成的安全关键交通场景

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-17 DOI: 10.1111/mice.70071

Mo Jia, Qixiu Cheng, Chengkun Tao, Yetao Hu, Qi Hong, Wenzhe Cheng, Zhiyuan Liu

{"title":"A physics-informed train on synthetic and test on real method for evaluating large language model-generated safety-critical traffic scenarios","authors":"Mo Jia, Qixiu Cheng, Chengkun Tao, Yetao Hu, Qi Hong, Wenzhe Cheng, Zhiyuan Liu","doi":"10.1111/mice.70071","DOIUrl":"https://doi.org/10.1111/mice.70071","url":null,"abstract":"Corner cases, which are rare and high-risk scenarios such as safety-critical behaviors in autonomous vehicle operations, present significant modeling challenges due to their low occurrence probability and limited data availability. Large language models (LLMs) offer new potential for synthesizing such scenarios, but existing evaluation metrics are inadequate because corner case data typically lack one-to-one mapping to real samples and have extremely limited instances. To address this, we propose a two-stage evaluation framework, that is, a physics-informed train on synthetic and test on real (PI-TSTR) framework. Using safety-critical car-following (CF) scenarios as an example, we design a prompting and interpolation strategy to guide LLMs in generating physically feasible synthetic follower trajectories from real leading vehicle inputs. We then evaluate the generated data by training several CF models, that is, extended S-shaped three-parameter (ES3) model, Gipps model, optimal velocity model (OVM), improved full velocity difference model (IFVDM), intelligent driver model (IDM), and testing their performances on real-world data. The CF models trained on LLM-generated trajectories show strong generalization to real scenarios, validating the quality of the synthetic data. This framework provides a physics-grounded approach for evaluating LLM-generated data in safety-critical, data-scarce domains.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"316 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084086","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

A Double‐T model for rutting performance prediction integrating data augmentation and periodic patterns 车辙性能预测的双T模型集成数据增量和周期模式

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-16 DOI: 10.1111/mice.70066

Xingyi Zhu, Yanan Wu, Chao Wang, Yicong Hu, Luca Rosafalco, Stefano Mariani

引用次数: 0

A central difference attention multi‐modal segmentation network integrating continual learning and graph convolution diffusion algorithms for complex road crack segmentation 结合持续学习和图卷积扩散算法的中心差分注意多模态分割网络用于复杂道路裂缝分割

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-16 DOI: 10.1111/mice.70076

Gang Wang, HuaJun Huang, JunHui Wang, YanFeng Wang, YaBing Yi, GuFeng Gong, Guoxiong Zhou

{"title":"A central difference attention multi‐modal segmentation network integrating continual learning and graph convolution diffusion algorithms for complex road crack segmentation","authors":"Gang Wang, HuaJun Huang, JunHui Wang, YanFeng Wang, YaBing Yi, GuFeng Gong, Guoxiong Zhou","doi":"10.1111/mice.70076","DOIUrl":"https://doi.org/10.1111/mice.70076","url":null,"abstract":"The presence of vehicles and traffic signs in complex scenarios poses significant challenges for road crack detection. To address these challenges, this paper integrates image and text information and proposes a new cross‐modal road crack detection model, CDGC‐TNet. The model uses a classic encoder–decoder structure for image feature extraction and BERT‐VisTrans text feature extractor for text feature extraction. First, the centered difference attention module is employed to deal with complex background interference. Second, the graph diffusion depth propagation algorithm is used to address the issue of fine cracks in segmentation problems. Finally, we employ a continuous learning mechanism based on flexible memory fusion to address catastrophic forgetting in the model. Through experimental validation on multiple public datasets, CDGC‐TNet outperforms 10 existing advanced crack segmentation networks in all metrics, demonstrating excellent performance and good generalization ability. Tests in real‐world road scenarios further prove the effectiveness of the proposed method, which can provide an efficient and reliable auxiliary tool for road safety detection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072130","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

Undersea immersed tube tunnel docking positioning via active cooperative target photogrammetry 基于主动协同目标摄影测量的海底沉管隧道对接定位

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-09-13 DOI: 10.1111/mice.70064

Huachuan Ma, Qingquan Li, Lin Tian, Zhipeng Chen, Xuhong Suo, Dejin Zhang

{"title":"Undersea immersed tube tunnel docking positioning via active cooperative target photogrammetry","authors":"Huachuan Ma, Qingquan Li, Lin Tian, Zhipeng Chen, Xuhong Suo, Dejin Zhang","doi":"10.1111/mice.70064","DOIUrl":"https://doi.org/10.1111/mice.70064","url":null,"abstract":"Deep water, distant sea, unmanned is the inevitable trend of the development of marine engineering, the underwater positioning system for the accuracy, real‐time, and environmental adaptability of the aspects of the increasingly high requirements. The mainstream underwater positioning methods face limitations such as multipath effects, cost, water depth, and water quality, making it difficult to meet diverse needs. This study presents a novel underwater photogrammetry solution based on an active cooperative target that combines optical hardware with intelligent algorithms to achieve millimeter‐level positioning in complex marine environments. Specifically, the system designs and optimizes the hardware configuration, including binocular vision camera, LED array target, and auxiliary optics, through multi‐parameter association to ensure the continuity and stability of positioning. At the algorithmic level, a multilevel image processing module is established through spatiotemporal distribution analysis, expected template matching, physical light intensity modeling, and geometric configuration constraints, which effectively overcomes the dynamic occlusion, scattering degradation and feature extraction errors of cooperative targets. In a standard test cell, the system achieves an angular accuracy of 0.24° and a ranging accuracy of 0.72 mm. A number of positioning systems have been developed to assist in the docking of submarine immersed tube tunnels, and the absolute positioning error is still better than 5 mm even under dynamic high turbidity conditions, which proves its effectiveness.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"37 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072131","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