Computer-Aided Civil and Infrastructure Engineering最新文献_第2页

Optimization of passenger flow control and parallel bus bridging in urban rail transit based on intelligent transport infrastructure

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-20 DOI: 10.1111/mice.13460

Qingqing Zhao, Jinjin Tang, Wen-Long Shang, Chao Li, Yifei Ren, Mohammed Quddus, Washington Ochieng

{"title":"Optimization of passenger flow control and parallel bus bridging in urban rail transit based on intelligent transport infrastructure","authors":"Qingqing Zhao, Jinjin Tang, Wen-Long Shang, Chao Li, Yifei Ren, Mohammed Quddus, Washington Ochieng","doi":"10.1111/mice.13460","DOIUrl":"https://doi.org/10.1111/mice.13460","url":null,"abstract":"Passenger flow control and bus bridging are used widely in the operations and management of urban rail transit to relieve the pressure of urban rail transit passenger flow, especially in peak periods. This paper presents an optimization method based on time-varying running time in links. We first develop a mixed integer nonlinear programming model seeking to achieve the minimum total passenger travel time and operation cost. An optimization network and an algorithm are then designed to solve the model. We use the developed method to solve both a small-scale simulated case study and a real-world case study involving the Chengdu Metro. The results obtained by the designed algorithm are comparable with those obtained by the CPLEX solver but with a shorter calculation time. The results show that parallel bus bridging can effectively reduce the number of waiting passengers. A sensitivity analysis of weight suggests that the algorithm successfully balances passenger travel cost and operating cost while incorporating time-varying running times leads to more realistic and dynamic infrastructure planning. This work contributes to the development of intelligent urban rail and road infrastructure systems, promoting safer and more efficient public transport operations.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"56 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661031","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 9

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-20 DOI: 10.1111/mice.13465

引用次数: 0

Enhanced consensus control architecture for autonomous platoon utilizing multi-agent reinforcement learning

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-19 DOI: 10.1111/mice.13463

Xin Guo, Jiankun Peng, Dawei Pi, Hailong Zhang, Changcheng Wu, Chunye Ma

{"title":"Enhanced consensus control architecture for autonomous platoon utilizing multi-agent reinforcement learning","authors":"Xin Guo, Jiankun Peng, Dawei Pi, Hailong Zhang, Changcheng Wu, Chunye Ma","doi":"10.1111/mice.13463","DOIUrl":"https://doi.org/10.1111/mice.13463","url":null,"abstract":"Coordinating a platoon of connected and automated vehicles significantly improves traffic efficiency and safety. Current platoon control methods prioritize consistency and convergence performance but overlook the inherent interdependence between the platoon and the the non-connected leading vehicle. This oversight constrains the platoon's adaptability in car-following scenarios, resulting in suboptimal optimization performance. To address this issue, this paper proposed a platoon control framework based on multi-agent reinforcement learning, aiming to integrate cooperative optimization with platoon tracking behavior and internal coordination strategies. This strategy employs a bidirectional cooperative optimization mechanism to effectively decouple the platoon's tracking behavior from its internal coordination control, and then recouple it in a multi-objective optimized manner. Additionally, it leverages long short-term memory networks to accurately capture and manage the platoon's dynamic nature over time, aiming to achieve enhanced optimization outcomes. The simulation results demonstrate that the proposed method effectively improves the platoon's cooperative effect and car-following adaptability. Compared to the consensus control strategy, it reduces the average spacing error by 8.3%. Furthermore, the average length of the platoon decreases by 19.1%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"14 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660435","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

Expanding sparse point deflection measurements to spatially continuous data via optical fiber sensors in long-span suspension bridges 通过大跨度悬索桥中的光纤传感器将稀疏点挠度测量扩展为空间连续数据

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-18 DOI: 10.1111/mice.13459

Qianen Xu, Xinteng Ma, Yang Liu

{"title":"Expanding sparse point deflection measurements to spatially continuous data via optical fiber sensors in long-span suspension bridges","authors":"Qianen Xu, Xinteng Ma, Yang Liu","doi":"10.1111/mice.13459","DOIUrl":"https://doi.org/10.1111/mice.13459","url":null,"abstract":"In structural health monitoring, only the deflection of key sections of the bridge can be monitored; the spatial continuous deflection of the main girder cannot be identified. To solve this problem, a method for expanding sparse point deflection measurements to spatially continuous data via optical fiber sensors in long-span suspension bridges is proposed. First, the distributed fiber-optic sensors are arranged longitudinally along the bridge to obtain the strain data of high-density measurement points on the main girder. Second, the influences of ambient temperature and cable system on the main girder strain of the suspension bridge are eliminated by using multiple types of sensors, and a transformation model from strain to deflection of the main girder based on an inverse finite element method is established. Then, by using thin-walled bar torsion analysis and deflection data obtained from point sensors, a method for expanding the deflection data of high-density measurement points on long-span suspension bridges that combines data interpolation and particle swarm optimization is proposed. The proposed method can extend the deflection monitoring data at key sections to the spatial continuous position of the main girder, thus effectively identifying the deflection of high-density measurement points on the main girder. Finally, a numerical simulation and monitoring data of a real bridge are used to evaluate the effectiveness of the proposed method, and the results show that the deflection identification results of the proposed method are more accurate than the conjugate beam method and the inverse finite element method without considering the main girder torsion.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"27 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640561","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

Origin–destination prediction via knowledge-enhanced hybrid learning

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-18 DOI: 10.1111/mice.13458

Zeren Xing, Edward Chung, Yiyang Wang, Azusa Toriumi, Takashi Oguchi, Yuehui Wu

{"title":"Origin–destination prediction via knowledge-enhanced hybrid learning","authors":"Zeren Xing, Edward Chung, Yiyang Wang, Azusa Toriumi, Takashi Oguchi, Yuehui Wu","doi":"10.1111/mice.13458","DOIUrl":"https://doi.org/10.1111/mice.13458","url":null,"abstract":"This paper proposes a novel origin–destination (OD) prediction (ODP) model, namely, knowledge-enhanced hybrid spatial–temporal graph neural networks (KE-H-GNN). KE-H-GNN integrates a deep learning predictive model with traffic engineering domain knowledge and a multi-linear regression (MLR) module for incorporating external factors. Leveraging insights from the gravity model, we propose two meaningful region partitioning strategies for reducing data dimension: election districts and K-means clustering. The aggregated OD matrices and graph inputs are processed using an long short-term memory network to capture temporal correlations and a multi-graph input graph convolutional network module to capture spatial correlations. The model also employs a global–local attention module, inspired by traffic flow theory, to capture nonlinear spatial features. Finally, an MLR module was designed to quantify the relationship between OD matrices and external factors. Experiments on real-world datasets from New York and Tokyo demonstrate that KE-H-GNN outperforms all the baseline models while maintaining interpretability. Additionally, the MLR module outperformed the concatenation method for integrating external factors, regarding both performance and transparency. Moreover, the election district-based partitioning approach proved more effective and simpler for practical applications. The proposed KE-H-GNN offers an effective and interpretable solution for ODP that can be practically applied in real-world scenarios.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"125 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640791","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 vision-based weigh-in-motion approach for vehicle load tracking and identification 基于视觉的运动称重法用于车辆载荷跟踪和识别

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-16 DOI: 10.1111/mice.13461

Phat Tai Lam, Jaehyuk Lee, Yunwoo Lee, Xuan Tinh Nguyen, Van Vy, Kevin Han, Hyungchul Yoon

{"title":"A vision-based weigh-in-motion approach for vehicle load tracking and identification","authors":"Phat Tai Lam, Jaehyuk Lee, Yunwoo Lee, Xuan Tinh Nguyen, Van Vy, Kevin Han, Hyungchul Yoon","doi":"10.1111/mice.13461","DOIUrl":"https://doi.org/10.1111/mice.13461","url":null,"abstract":"With the rapid increase in the number of vehicles, accurately identifying vehicle loads is crucial for maintaining and operating transportation infrastructure systems. Existing load identification methods typically rely on collecting vehicle load data from weigh-in-motion (WIM) systems when vehicles pass over them. However, cumbersome installation, high costs, and regular maintenance are the main obstacles that prevent WIM from being widely used in practice. This study introduces the visual WIM (V-WIM) framework, a vision-based approach for tracking and identifying moving loads. The V-WIM framework consists of two main components, the vehicle weight estimation and the vehicle tracking and location estimation. Vehicle weight is estimated using tire deformation parameters extracted from tire images through object detection and optical character recognition techniques. A deep learning-based YOLOv8 algorithm is employed as a vehicle detector, combined with the ByteTrack algorithm for tracking vehicle location. The vehicle weight and its corresponding location are then integrated to enable simultaneous vehicle weight estimation and tracking. The performance of the proposed framework was evaluated through two component validation tests and one on-site validation test, demonstrating its capability to overcome the limitations of existing methods.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"89 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631374","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 crack detection method based on structure perception for drop brackets and swivel clevises in catenary system

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-14 DOI: 10.1111/mice.13464

Dongkai Zhang, Lifan Sun, Ferrante Neri, Zhumu Fu, Long Yu, Jian Wang, Yajie Yu

{"title":"A crack detection method based on structure perception for drop brackets and swivel clevises in catenary system","authors":"Dongkai Zhang, Lifan Sun, Ferrante Neri, Zhumu Fu, Long Yu, Jian Wang, Yajie Yu","doi":"10.1111/mice.13464","DOIUrl":"https://doi.org/10.1111/mice.13464","url":null,"abstract":"Drop brackets (DB) and swivel clevises (SC) are critical components of railway catenary systems, playing a key role in maintaining cantilever stability. The condition of these components significantly impacts the safe operation of the catenary, necessitating periodic inspections to detect defects. This task is typically performed by onboard cameras using computer vision. However, traditional image processing methods often focus on shallow features, making it difficult to handle the interference from complex structures of components. While deep learning methods have strong capabilities in capturing semantic features, the lack of crack samples makes reliable crack identification challenging. Therefore, a joint approach for crack detection based on structural perception is proposed. The approach integrates three main components: object structure perception, stick structure perception, and crack defect detection. A multistream catenary components segmentation network (MCSnet) is employed to extract structural features of the DB and SC. Subsequently, an adaptive stick perception method (ASPM) is applied to identify potential crack candidates based on stick structure. The combined structural features enable effective detection of crack defects. Experimental results validate the effectiveness of the proposed approach.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"69 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618923","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

Uncertainty-aware fuzzy knowledge embedding method for generalized structural performance prediction

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-14 DOI: 10.1111/mice.13457

Xiang-Yu Wang, Xin-Rui Ma, Shi-Zhi Chen

{"title":"Uncertainty-aware fuzzy knowledge embedding method for generalized structural performance prediction","authors":"Xiang-Yu Wang, Xin-Rui Ma, Shi-Zhi Chen","doi":"10.1111/mice.13457","DOIUrl":"https://doi.org/10.1111/mice.13457","url":null,"abstract":"Structural performance prediction for structures and their components is a critical issue for ensuring the safety of civil engineering structures. Thus, enhancing the reliability of the prediction models with better generalization capability and quantifying the uncertainties of their predictions is significant. However, existing mechanism-driven and data-driven prediction models for reliable engineering applications incorporate complicated modifications on models and are sensitive to the precision of relevant prior knowledge. Focusing on these issues, a novel and concise data-driven approach, named “R2CU” (stands for transforming regression to classification with uncertainty-aware), is proposed to introduce the relative <i>fuzzy prior knowledge</i> into the data-driven prediction models. To enhance generalization capacity, the conventional regression task is transformed into a classification task based on the fuzzy prior knowledge and the experimental data. Then the aleatoric and epistemic uncertainty of the prediction is estimated to provide the confidence interval, which reflects the prediction's trustworthiness. A validation case study based on shear capacity prediction of reinforced concrete (RC) deep beams is carried out. The result proved that the model's generalization capability for extrapolating has been effectively enhanced with the proposed approach (the prediction precision was raised 80%). Meanwhile, the uncertainties within the model's prediction are rationally estimated, which made the proposed approach a practical alternative for structural performance prediction.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"23 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618918","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

Short-term prediction of railway track degradation using ensemble deep learning

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-14 DOI: 10.1111/mice.13462

Yong Zhuang, Yuanjie Tang, Yingchen Qiu, Rengkui Liu

{"title":"Short-term prediction of railway track degradation using ensemble deep learning","authors":"Yong Zhuang, Yuanjie Tang, Yingchen Qiu, Rengkui Liu","doi":"10.1111/mice.13462","DOIUrl":"10.1111/mice.13462","url":null,"abstract":"<p>Short-term prediction of track degradation facilitates flexible and efficient maintenance, thereby meeting the railway system's escalating demands for track safety and smoothness. However, the track condition evolution presents challenges to accurate prediction, with diverse influential factors resulting in heterogeneous degradation patterns across space and time. In a short-term context, time series derived from historical records are length-limited, with sparse sampling points complicating feature identification. Actual activities, particularly minor repairs, lack strict periodicity, leading to irregular spans in continuous degradation curves, yielding nonuniform samples. This study leverages dynamic inspection and influential factors to propose an ensemble learning using the Transformer model. The outer framework employs unsupervised learning to group the sections based on specific time periods and track lengths. It assigns fuzzy logic categories to these groups to capture differentiated patterns and guides the division of samples into fuzzy subsets and assigns them to the learners corresponding to each cluster. The loosely coupled structure aids task decomposition and enhances local performance. The inner model refines the Transformer design for a new scenario, introducing a prediction objective transformation based on the interdependencies among multidimensional indicators to strengthen feature extraction. The prediction performance is evaluated using over 2 years of records from 560 km railway lines, offering insights for improving onsite track management.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 10","pages":"1314-1343"},"PeriodicalIF":8.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618922","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}

引用次数: 0

A lightweight binocular vision-supported framework for 3D structural dynamic response monitoring

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-03-13 DOI: 10.1111/mice.13452

Yujie Ruan, Tao Huang, Cheng Yuan, Gang Zong, Qingzhao Kong

{"title":"A lightweight binocular vision-supported framework for 3D structural dynamic response monitoring","authors":"Yujie Ruan, Tao Huang, Cheng Yuan, Gang Zong, Qingzhao Kong","doi":"10.1111/mice.13452","DOIUrl":"https://doi.org/10.1111/mice.13452","url":null,"abstract":"Current three-dimensional (3D) displacement measurement algorithms exhibit practical limitations, such as computational inefficiency, redundant point cloud data storage, reliance on preset targets, and restrictions to unidirectional measurements. This research aims to address computation efficiency and accuracy issues in binocular camera-based 3D structural displacement measurement by proposing a lightweight binocular vision-supported framework for structural 3D dynamic response monitoring. Through the optimization of sub-algorithms and code structures, this framework enhances both measurement accuracy and computational efficiency. The research incorporates a hybrid feature point processing algorithm and a lightweight tracking algorithm, which improve the accuracy of feature point recognition and tracking, enhance the adaptability and flexibility of the monitoring process, and increase tracking efficiency and overall system performance. These improvements make the framework more applicable to various civil engineering scenarios. Experimental validation on a full-scale three-story structure shows that the framework enables effective, target-free, 3D dynamic monitoring. Compared with reference displacement sensors, the framework achieves a relative root mean squared error of 14.6%, closely matching the accuracy of traditional methods that utilize accelerometers. The framework processes 1000 frames at 9.2 frames per second, offering a novel solution for contactless structural dynamic response monitoring in civil engineering applications, such as residential buildings and bridges, within a reasonable distance.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608527","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