Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Network models for temporal data reconstruction for dam health monitoring","authors":"Yongjiang Chen, Kui Wang, Mingjie Zhao, JianFeng Liu","doi":"10.1111/mice.13431","DOIUrl":"https://doi.org/10.1111/mice.13431","url":null,"abstract":"The reconstruction of monitoring data reconstruction is an important step in the process of structural health monitoring. Monitoring data reconstruction involves generating values that are close to the true or expected values, and then using the generated values to replace the anomalous data or fill in the missing data. Deep learning models can be used to reconstruct dam monitoring data, but current models suffer from the inabilities to reconstruct data when the dataset is significantly incomplete, and the reconstruction accuracy and speed have needs for improvement. To this end, this paper proposes a dam temporal reconstruction nets (DTRN) based on generative adversarial nets, which is used to accurately reconstruct dam monitoring data for cases of incomplete datasets. To improve the accuracy of the reconstruction values, this paper embeds a gated recurrent unit network based on a sequence-to-sequence model into DTRN to extract the temporal features of the dam monitoring data. In addition, given that random matrices with different distributions lead to different reconstruction results, maximum probability reconstruction based on multiple filling is adopted. Finally, several experiments show that (1) DTRN is not only applicable to the reconstruction of various types of dam monitoring data (e.g., dam displacement monitoring data, dam seepage pressure monitoring data, seam gauge monitoring data, etc.) but also can be applied to other relatively smooth time series data. (2) The average root mean square error of DTRN (0.0618) indicates that its accuracy is 92.3%, 57.5%, and 71.99% higher than that of generative adversarial imputation nets (GAIN), timing GAIN (TGAIN), and dam monitoring data reconstruction network (DMDRN), respectively. (3) The average elapsed time of DTRN (522.6 s) is 68.45% and 48.10% shorter than that of TGAIN and DMDRN, respectively.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"128 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401936","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":"Integrated column generation for volunteer-based delivery assignment and route optimization","authors":"Asya Atik, Kuangying Li, Leila Hajibabai, Ali Hajbabaie","doi":"10.1111/mice.13439","DOIUrl":"https://doi.org/10.1111/mice.13439","url":null,"abstract":"This study develops an integrated delivery assignment and route planning strategy for food banking operations, considering food supply and demand constraints, food item restrictions, and vehicle capacity constraints. A mixed-integer linear model is formulated to maximize the total demand served and minimize the total travel cost imposed on delivery volunteers. An integrated solution algorithm is developed that includes Lagrangian relaxation and column generation. The algorithm decomposes the problem into assignment and routing components and solves each iteratively. The proposed methodology is applied to a case study in Wake County, NC. A series of sensitivity analyses are conducted to draw insights. The numerical results demonstrate the proposed methodology's capacity to solve complex problems in food delivery operations efficiently.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"2 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393705","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 interactive cross-multi-feature fusion approach for salient object detection in crack segmentation","authors":"Jian Liu,&nbsp;Pei Niu,&nbsp;Lei Kou,&nbsp;Yalin Zhang,&nbsp;Honglei Chang,&nbsp;Feng Guo","doi":"10.1111/mice.13437","DOIUrl":"10.1111/mice.13437","url":null,"abstract":"<p>Salient object detection (SOD) is a crucial preprocessing technique in visual computing, which identifies the salient regions in an image by simulating the human visual perception system. It achieves remarkable results in tasks such as image quality assessment, editing, and object recognition. However, due to the particularity of pavement crack detection in terms of scale and feature requirements, the SOD model is rarely applied in pavement surface crack detection at present. In order to break the existing dilemma, this paper proposes a new SOD model (iU2Net) specialized for crack detection, which is based on the encoder–decoder structure of U2Net and incorporates the developed interactive cross-multi-feature fusion module (ICMFM). Compared with the existing models, the main contributions of iU2Net are reflected in two aspects. On the one hand, current models are difficult to comprehensively extract the complex features of cracks while iU2Net achieves a breakthrough in feature extraction by efficiently aggregating multiscale crack features and accurately reconstructing them through its unique architecture. On the other hand, iU2Net focuses on infrastructure crack detection, breaking the limitation of independent processing of traditional feature channels and facilitating information exchange. To validate the model's effectiveness, comprehensive experiments are conducted on a public benchmark dataset. iU2Net is compared with eight existing SOD models (EGNet, PoolNet, MINet, F3Net, U2Net, SegNet, BASNet, and DeepCrack). Training and detection performance is evaluated using average mean absolute error (AveMAE), maximum F1 score (MaxF1), mean F1 score (MeanF1), precision–recall curves, and visualizations. Experimental the results indicate that iU2Net exceeds the behavior of other networks during both the training and testing phases, with MaxF1 and MeanF1 achieving maximum values of 0.912 and 0.730, respectively; and AveMAE of 0.048, which is only 0.005 higher than the minimum value, which demonstrates its effectiveness for pavement surface crack detection and indicating potential future applications involving interactive feature fusion.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 8","pages":"1080-1099"},"PeriodicalIF":8.5,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375474","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 5","authors":"","doi":"10.1111/mice.13433","DOIUrl":"https://doi.org/10.1111/mice.13433","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Attention-optimized 3D segmentation and reconstruction system for sewer pipelines employing multi-]view images</i> by Wang Niannian et al., https://doi.org/10.1111/mice.13241.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 5","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111878","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":"The expressway network design problem for multiple urban subregions based on the macroscopic fundamental diagram","authors":"Yunran Di, Weihua Zhang, Haotian Shi, Heng Ding, Jinbiao Huo, Bin Ran","doi":"10.1111/mice.13435","DOIUrl":"https://doi.org/10.1111/mice.13435","url":null,"abstract":"With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The method employs the macroscopic fundamental diagram to model arterial dynamics and the cell transmission model to capture expressway dynamics. A stochastic user equilibrium model is further established for route choice, and a decision model is developed to minimize total time spent. Simulations show that new expressways alleviate network congestion, with significant effects in the initial stages. Moreover, route guidance strategies and driver compliance also influence the schemes.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083148","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 corrosion surface quantification in steel transmission towers using UAV photogrammetry and deep convolutional neural networks","authors":"Pierclaudio Savino, Fabio Graglia, Gabriele Scozza, Vincenzo Di Pietra","doi":"10.1111/mice.13434","DOIUrl":"https://doi.org/10.1111/mice.13434","url":null,"abstract":"Corrosion in steel transmission towers poses a challenge to structural integrity and safety, requiring efficient detection methods. Traditional visual inspections are unsustainable due to the complexity and volume of structures. Their manual, qualitative, and subjective nature often leads to inconsistencies in maintenance planning. This study proposes a deep learning-based approach for semantic segmentation of corroded areas on steel towers. Using the DeepLabv3+ model, the network was trained and validated on 999 field photographs. MobileNetV2, serving as the feature extractor, was chosen for its optimal balance between accuracy and computational efficiency, achieving a validation accuracy of 90.8% and a loss of 0.23. The trained network was applied to real-world inspections using orthomosaics derived from photogrammetric reconstructions of the South-East tower at the Torino Eremo broadcasting center. These photogrammetric products not only enabled precise segmentation of corroded areas but also provided the foundation for corrosion quantification with metrical accuracy, a critical advantage for maintenance planning. Unlike traditional image segmentation methods, which lack a spatial reference and precise scaling, the photogrammetric approach ensures that the corrosion extent and distribution are quantified in exact physical dimensions, enhancing the reliability of the analysis. The results show that deep learning-based inspections can automate detection, providing reliable data and reducing reliance on manual inspections, enhancing efficiency, safety, and accuracy.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"25 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124406","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":"Autonomous construction framework for crane control with enhanced soft actor–critic algorithm and real-time progress monitoring","authors":"Yifei Xiao, T. Y. Yang, Fan Xie","doi":"10.1111/mice.13427","DOIUrl":"https://doi.org/10.1111/mice.13427","url":null,"abstract":"With the shortage of skilled labors, there is an increasing demand for automation in the construction industry. This study presents an autonomous construction framework for crane control with enhanced soft actor–critic (SAC-E) algorithm and real-time progress monitoring. SAC-E is a novel reinforcement learning algorithm with superior learning speed and training stability for lifting path planning. In addition, robotic kinematics and a control algorithm are implemented to ensure that the crane can autonomously execute the lifting path. Last, novel hardware communication interfaces between robot operating system and building information modeling (BIM) are developed for real-time construction progress monitoring. The performance of the proposed framework was demonstrated using a robotized mobile crane to stack concrete retaining blocks. The results show that the proposed framework can be effectively used to execute the retaining block construction using the robotized mobile crane with real-time construction update in the BIM platform.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"14 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056574","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":"Vehicle wheel load positioning method based on multiple projective planes","authors":"Kai Sun, Xu Jiang, Xuhong Qiang","doi":"10.1111/mice.13432","DOIUrl":"https://doi.org/10.1111/mice.13432","url":null,"abstract":"Computer vision-based vehicle load monitoring methods could obtain spatiotemporal data of vehicle loads, which is important for bridge monitoring and operation. However, during the process of vehicle detection and tracking, current research usually focuses on the vehicle as a whole, and there is a lack of research on the accurate positioning of vehicle wheel loads. For the fatigue analysis of orthotropic steel deck, stress at the structural details belongs to the typical third-class system, and related research requires accurate wheel load position. Based on the principle of camera imaging, this study proposes an innovative vehicle wheel load location method based on vehicle license plate detection and multiple projective planes, and the accurate positioning of the vehicle center is achieved by the projective relationship matrix of different planes. Then, accurate measurement of the lateral wheelbase is achieved through secondary detection and projective transformation. Further, accurate wheel load tracking for fatigue research is achieved by the multi-objective tracking algorithm. Based on theoretical analysis and practical application results, the effectiveness and accuracy of this method have been verified. Different from traditional positioning methods based on vehicle detection boxes and 3D reconstruction boxes, the proposed method has higher accuracy and will play a fundamental role in the use of vehicle load spatiotemporal data for more accurate analysis such as fatigue research.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"31 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050875","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":"Reinforcement learning-based trajectory planning for continuous digging of excavator working devices in trenching tasks","authors":"X. Tan, W. Wei, C. Liu, K. Cheng, Y. Wang, Z. Yao, Q. Huang","doi":"10.1111/mice.13428","DOIUrl":"https://doi.org/10.1111/mice.13428","url":null,"abstract":"This paper addresses the challenge of real-time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is established, resulting in optimal trajectories, the sensitive parameters, and the corresponding variation ranges. Second, an RL model is built, and the optimization results obtained offline are used as training samples. The RL-based method can be applied for continuous digging tasks, which is beneficial for improving the overall efficiency of the autonomous operation of the excavator. Finally, simulation experiments were conducted in four distinct conditions. The results demonstrate that the proposed method effectively accomplishes excavation tasks, with trajectory generation completed within 0.5 s. Comprehensive performance metrics remained below 0.14, and the excavation rate exceeded 92%, surpassing or matching the performance of the optimization-based method and PINN-based method. Moreover, the proposed method produced consistently balanced trajectory performance across all sub-tasks. These results underline the method's effectiveness in achieving real-time, multi-objective, and continuous trajectory planning for autonomous excavators.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050874","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":"Modeling the collective behavior of pedestrians with the spontaneous loose leader–follower structure in public spaces","authors":"Jie Xu, Dengyu Xu, Jing Wu, Xiaowei Shi","doi":"10.1111/mice.13429","DOIUrl":"https://doi.org/10.1111/mice.13429","url":null,"abstract":"Gaining insights into pedestrian flow patterns in public spaces can greatly benefit decision-making processes related to infrastructure planning. Interestingly, even pedestrians are unfamiliar with one another, they often follow others, drawing on positive information and engaging in a spontaneous collective behavior of pedestrians. To model this collective behavior, this paper proposed a social force-based technique characterized by a loosely defined leader–follower structure. First, a complex field-based phase transfer entropy (PTE) method was applied to measure the difference in information flow between pedestrians. Setting the detecting threshold with the 3 sigma principle, the radial basis function (RBF) was utilized to identify the leader in the collective. Integrating the PTE, RBF, and social force model (SFM), a comprehensive model (PTE-RBF-SFM) was developed to simulate collective behavior. Some bidirectional pedestrian flow data, collected from Fairground Düsseldorf, were used to validate the model in a real-world setting. The results showed that the proposed model provided more realistic trajectories than benchmark models, and the spontaneous leader–follower structure was found to change over time and stable with time interval prolonging.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"34 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044282","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}