Computer-Aided Civil and Infrastructure Engineering最新文献_第4页

Time–cost combined optimization in planning infrastructure construction projects under environment induced time-window constraints 环境诱导时间窗约束下基础设施建设项目规划的时间-成本联合优化

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-12-01 DOI: 10.1111/mice.70175

Serhii Naumets, Ming Lu, Kai Qi

{"title":"Time–cost combined optimization in planning infrastructure construction projects under environment induced time-window constraints","authors":"Serhii Naumets, Ming Lu, Kai Qi","doi":"10.1111/mice.70175","DOIUrl":"10.1111/mice.70175","url":null,"abstract":"To accommodate environment-induced time-window constraints, environment-sensitive activities are arranged within allowable time windows while maintaining technological precedence and other logical relationships on the project. This research advances classical time–cost trade-off (TCT) analysis by incorporating time-window constraints in project planning, creating a more complex optimization problem that becomes computationally prohibitive for real-world applications. To overcome this challenge, an integrated project planning framework combining project time and cost into a single objective function is formalized. The optimization solution employs time-window scheduling algorithms to simulate method combinations through enumerated simulation. A reward function is defined to evaluate alternatives based on their impact on project cost and duration. In addition, a sample size reduction technique is utilized to maintain computational efficiency of random sampling without sacrificing accuracy. The methodology's practical application is demonstrated through a case study of a river-crossing bridge project in remote northern Canada, which is planned to validate its effectiveness in planning real-world infrastructure projects under stringent environment-induced time-window constraints.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6678-6700"},"PeriodicalIF":9.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651186","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 spatiotemporal prediction method for the evolution of pavement distress in road networks 路网路面破损演变的时空预测方法

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-30 DOI: 10.1111/mice.70169

Ning Pan, Yuchuan Du, Ajith Kumar Parlikad

{"title":"A spatiotemporal prediction method for the evolution of pavement distress in road networks","authors":"Ning Pan, Yuchuan Du, Ajith Kumar Parlikad","doi":"10.1111/mice.70169","DOIUrl":"10.1111/mice.70169","url":null,"abstract":"Existing pavement performance prediction models often struggle to capture complex spatiotemporal dependencies in road networks due to reliance on empirical rules and scenario-based calibration. This study proposes pavement graph network (PaveGNet), a spatiotemporal graph network framework designed to model fine-grained pavement distress evolution. It constructs a multi-node graph encoding topological correlations and time-based state transitions, while integrating exogenous factors such as temperature, precipitation, traffic, and maintenance works. Experiments demonstrate that PaveGNet performs effectively in predicting fine-grained indicators of distress evolution. The prediction error for distress evolution rate was significantly reduced, from 9.005% with the baseline spatial–temporal graph convolutional network model to 2.670%. Ablation experiments were conducted to verify the contribution of temporal interdependence, spatial correlation, and external variables in the proposed PaveGNet framework. The results demonstrate that all three components play essential roles in prediction, with external variables showing the most significant impact. To further assess the modular robustness, parts of the spatial and temporal learning modules were independently replaced. The results indicate that the prediction of distress evolution rate relies heavily on the originally designed learning components. Overall, this framework provides a more realistic and scalable solution for the spatiotemporal prediction of pavement distress evolution in road networks.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6513-6532"},"PeriodicalIF":9.1,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619422","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 method for detecting construction deviations in large and complex building structures utilizing synthetic point clouds for segmentation 一种利用合成点云进行分割的大型复杂建筑结构施工偏差检测方法

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-28 DOI: 10.1111/mice.70171

Jia Zou, Xiongyao Xie, Genji Tang

{"title":"A method for detecting construction deviations in large and complex building structures utilizing synthetic point clouds for segmentation","authors":"Jia Zou, Xiongyao Xie, Genji Tang","doi":"10.1111/mice.70171","DOIUrl":"10.1111/mice.70171","url":null,"abstract":"Point cloud-based construction quality assessment and quality control (QA/QC) are playing an increasingly important role in large-scale complex building projects. However, this approach faces several challenges, such as the laborious and time-intensive process of manual point cloud segmentation, the high cost of point cloud labeling, and the lack of sufficient training data for deep learning-based automatic segmentation methods. To address these issues, this paper proposed a method for detecting construction deviations in large-scale complex building structures by utilizing synthetic point clouds for segmentation. The method automatically generated labeled synthetic point clouds with Gaussian noise using BIM and a virtual engine, significantly augmenting the limited amount of real point cloud data to train the semantic segmentation model, enabling the achievement of 94.2% overall accuracy (OA) and 81.1% mean intersection over union (M_IoU). Furthermore, a point cloud instance segmentation method according to density-based spatial clustering of applications with noise (DBSCAN) and voxel-vs-BIM was proposed to independently compare each instance object of different building components with its corresponding BIM model, assessing the construction accuracy of each component based on root mean square error metric and the level of accuracy specification. For components with an LOA3 accuracy level, further deviation analysis was conducted. Taking the structural construction deviation detection of beams, columns, and concrete thick shells in the core area of the Shanghai Grand Opera House as a case, the proposed method significantly improved the efficiency of QA/QC.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6597-6621"},"PeriodicalIF":9.1,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610936","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

Adaptive stochastic deterioration modeling for evaluating pavement survey schedules in relation to performance and management cost 基于性能和管理成本的路面调查进度评估的自适应随机退化模型

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-26 DOI: 10.1111/mice.70172

Zhe Wu, Zijun Xu, Linyi Yao, Lingxiao Shangguan, Guoyang Lu, Dawei Wang

{"title":"Adaptive stochastic deterioration modeling for evaluating pavement survey schedules in relation to performance and management cost","authors":"Zhe Wu, Zijun Xu, Linyi Yao, Lingxiao Shangguan, Guoyang Lu, Dawei Wang","doi":"10.1111/mice.70172","DOIUrl":"10.1111/mice.70172","url":null,"abstract":"Pavement performance impacts transportation mobility, safety, and comfort, with timely maintenance relying on field survey data. However, frequent surveys are costly and impractical. This study examines how survey timing and frequency influence maintenance events and pavement deterioration to optimize management strategies. Using the pavement condition index as a performance indicator, historical data and maintenance records from the long-term pavement performance database were analyzed, and regional deterioration curves were derived using shifting factor methods. The proposed adaptive stochastic deterioration modeling method incorporates survey timing through sequential probabilistic events, capturing the stochastic nature of pavement deterioration. Mixture density networks predicted survey outcome distributions, while Monte Carlo simulations analyzed system behavior. Results show that longer survey intervals increase uncertainty, while uniform schedules reveal local cost minima, highlighting survey frequency ranges of over-survey and under-survey. Non-uniform schedules have been validated to offer potential for greater cost-effectiveness. These contributions provide road agencies with a new perspective for optimizing management strategies.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6701-6721"},"PeriodicalIF":9.1,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609247","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

Reinforcement response prediction of composite-concrete beams with crack patterns and deep learning 基于裂缝模式和深度学习的复合混凝土梁钢筋响应预测

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-26 DOI: 10.1111/mice.70174

Sike Wang, Yizhou Lin, Junyi Duan, Huaixiao Yan, Xingyu Wang, Xiaoli Xiong, Ying Huang, Shanyue Guan, Chengcheng Tao

{"title":"Reinforcement response prediction of composite-concrete beams with crack patterns and deep learning","authors":"Sike Wang, Yizhou Lin, Junyi Duan, Huaixiao Yan, Xingyu Wang, Xiaoli Xiong, Ying Huang, Shanyue Guan, Chengcheng Tao","doi":"10.1111/mice.70174","DOIUrl":"10.1111/mice.70174","url":null,"abstract":"Using fiber-reinforced polymer reinforcement in concrete (FRP-concrete) to form composite-concrete structures is increasingly important in civil and infrastructure applications because of its high strength, durability, and corrosion resistance. However, one of the main challenges of the FRP-concrete structure is the brittle failure mode, which might result in sudden structural collapse. It is critical to closely monitor the strain of composite reinforcement for potential structural damage. This study develops a deep learning-based framework for the reinforcement condition identification in terms of non-contact strain using the crack image data from FRP-concrete beam system. The framework contained two stages: first, using crack pattern and strain data obtained from simulation to train the Kolmogorov–Arnold networks (KANs)-based convolutional neural network strain prediction model, which added a new KAN channel in convolution to enhance performance. Subsequently, the actual crack patterns from you only look once v11 segmentation results are used to generate the input of the prediction model. A three-bending test of an FRP-concrete beam is presented to validate this method. The developed framework achieves an average R2 of 0.713, compared to the actual sensor data in reinforcement strain prediction. The results indicate that the intelligent framework has superior performance in strain prediction, addressing challenges in FRP-concrete structure applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6638-6655"},"PeriodicalIF":9.1,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609246","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

Detection and localization of pipeline leakage of water distribution networks based on graph convolutional networks 基于图卷积网络的配水管网泄漏检测与定位

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-26 DOI: 10.1111/mice.70173

Zhengxuan Li, Yimei Tian, Sen Peng

{"title":"Detection and localization of pipeline leakage of water distribution networks based on graph convolutional networks","authors":"Zhengxuan Li, Yimei Tian, Sen Peng","doi":"10.1111/mice.70173","DOIUrl":"10.1111/mice.70173","url":null,"abstract":"A leak in a water distribution network (WDN) creates a localized pressure depression whose effect diffuses along the network topology through headloss coupling. This spatial diffusion is well approximated by graph Laplacian smoothing, so we use a graph convolutional network as a learnable surrogate of hydraulic information propagation on the graph. The method casts single-snapshot leakage analysis as graph-level classification of normal versus abnormal states using topology-aware convolution. Localization then leverages gradient-based node importance—sensitivities of the leak logit to node features—mapped to pipes via linear interpolation to produce leakage-risk contour maps. The framework couples pressure and demand features, accommodates partial monitoring by varying sensor density, and evaluates noise robustness by injecting Gaussian noise into inputs. In simulated WDNs, the approach maintains high accuracy across sensor-density settings and exhibits a predictable trend under increasing noise, where false negative rate rises faster than false positive rate as signal-to-noise ratio decreases. On the L-Town benchmark, we use real pressure measurements and nodal demand data consistent with metered consumption (generally <10% deviation), providing a realistic testbed. A simplified network representation accelerates convergence while preserving localization fidelity, enabling millisecond-level inference suitable for operational deployment. Together, these results support a physically grounded, computationally efficient pathway for leak detection and localization in smart water networks.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6656-6677"},"PeriodicalIF":9.1,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609245","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 vortex-induced vibration warning method based on ensemble-learning-embedded neural network 基于集成学习嵌入神经网络的涡激振动预警方法

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-25 DOI: 10.1111/mice.70160

Fei Yan, Yan Geng, Yue Liu, Ning Chen, Xuanzhi Li, Yue Wang, Angelo Aloisio

{"title":"A vortex-induced vibration warning method based on ensemble-learning-embedded neural network","authors":"Fei Yan, Yan Geng, Yue Liu, Ning Chen, Xuanzhi Li, Yue Wang, Angelo Aloisio","doi":"10.1111/mice.70160","DOIUrl":"10.1111/mice.70160","url":null,"abstract":"An ensemble-learning-embedded neural network for vortex-induced vibration (VIV) early warning was proposed. The new model consists of a core module based on ensemble learning and peripheral modules. The core module identifies abstract features of VIV, while the peripheral modules handle feature extraction and weight control. The core module of the new model is trained entirely using augmented datasets. Consequently, compared to other models with equivalent parameter counts, the new model can be trained using significantly fewer datasets. Displacement records from three VIV events at a cable-stayed bridge under construction were used to train and test the model. The new model demonstrated superior performance during testing. After retraining with data from a single VIV event at another cable-stayed bridge in the construction phase, the new model successfully achieved VIV early warning for the new bridge. The new model demonstrates significant potential for providing early warning of VIV due to its lower data requirements.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 31","pages":"6419-6436"},"PeriodicalIF":9.1,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599047","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

An explainable intelligent system for multi-performance shield tunnel tail grout optimization 一种可解释的多性能盾构隧道尾浆优化智能系统

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-25 DOI: 10.1111/mice.70149

Jiaxin Liang, Wei Liu, Jingyi Gong, Cheng Chen, Xiaoqiang Dong, Chunqing Fu

{"title":"An explainable intelligent system for multi-performance shield tunnel tail grout optimization","authors":"Jiaxin Liang, Wei Liu, Jingyi Gong, Cheng Chen, Xiaoqiang Dong, Chunqing Fu","doi":"10.1111/mice.70149","DOIUrl":"10.1111/mice.70149","url":null,"abstract":"Shield tunnel tail grouting fills the annular gap between excavated soil and tunnel lining, supporting soil stability and controlling ground settlements during construction. However, existing grout performance prediction methods are limited by labor-intensive empirical testing, insufficient datasets, and inadequate modeling of liquid-to-solid phase transitions, resulting in consolidation deformation, ground loss, suboptimal formulations, and increased settlements. To address these issues, this study develops an explainable intelligent system for multi-performance grout optimization, integrating innovative experimental testing with advanced machine learning. A unique database is constructed from liquid-state (e.g., density, bleeding rate, fluidity) and solid-state (e.g., unconfined compressive strength (UCS), compressed deformation) properties, augmented via a physics-constrained generative adversarial network for realistic datasets. Four algorithms (artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR)) are ensemble-optimized using Bayesian techniques and K-fold cross-validation, achieving high predictive accuracy. SHapley Additive exPlanations (SHAP) analysis enhances interpretability, identifying water–binder ratio as dominant for liquid properties and cement–fly ash ratio for strength. Laboratory experiments and field applications validate the system, highlighting its efficiency in grout optimization, accurate prediction of consolidation-induced settlements, and improved deformation control, thereby enabling better settlement management, protection of adjacent structures, and informed decision-making in shield tunneling projects.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 30","pages":"6165-6183"},"PeriodicalIF":9.1,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599048","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

Knowledge-guided generative adversarial networks for intelligent design of internal supporting structures in foundation pits 基于知识导向的生成对抗网络的基坑内部支护结构智能设计

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-24 DOI: 10.1111/mice.70156

Fan Zhang, Kaiqiang Wang, Qing Sun, Wanpeng Song, Cheng Fang, Yuqing Gao

{"title":"Knowledge-guided generative adversarial networks for intelligent design of internal supporting structures in foundation pits","authors":"Fan Zhang, Kaiqiang Wang, Qing Sun, Wanpeng Song, Cheng Fang, Yuqing Gao","doi":"10.1111/mice.70156","DOIUrl":"10.1111/mice.70156","url":null,"abstract":"This study presents SupportGAN, a knowledge-guided framework based on a two-stage generative adversarial network, for preliminary conceptual plan-view layout of corner- and cross-supporting structures in foundation pits. Design drawings of support structures collected from authoritative design institutes were semantically processed and expanded through a structural knowledge-guided augmentation (SKGA) approach. The SupportGAN model was then trained with multiple hyperparameter configurations to achieve optimal performance. Additionally, SupportGAN was evaluated and compared with two mainstream GAN models (pix2pix and pix2pixHD), demonstrating its superior capabilities. The design results generated by SupportGAN were evaluated using visual assessment and quantitative metrics. A feasibility assessment was also performed to confirm the economic and mechanical viability of the generated layouts: A pixel-count proxy showed a 6.90% material gap versus engineer designs, and results of the finite element (FE) analysis on two cases indicated comparable structural performance (force difference <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≤</mo>\u0000 <mn>12.74</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$le 12.74 %$</annotation>\u0000 </semantics></math>). The results indicate that SupportGAN's outputs exhibit significant similarities to those of expert engineers across various aspects, demonstrating its potential to aid designers in the preliminary conceptual layout design of corner- and cross-supporting structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 30","pages":"6149-6164"},"PeriodicalIF":9.1,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145583030","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

Enhancing logit stochastic user equilibrium convergence in large-scale networks via Barzilai–Borwein step size optimization 基于Barzilai-Borwein步长优化的大规模网络logit随机用户均衡收敛

IF 9.1 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-11-24 DOI: 10.1111/mice.70148

Zelin Wang, Yicheng Zhang, Yuting Ding, Qian Liu, Chengqi Liu, Qixiu Cheng

{"title":"Enhancing logit stochastic user equilibrium convergence in large-scale networks via Barzilai–Borwein step size optimization","authors":"Zelin Wang, Yicheng Zhang, Yuting Ding, Qian Liu, Chengqi Liu, Qixiu Cheng","doi":"10.1111/mice.70148","DOIUrl":"10.1111/mice.70148","url":null,"abstract":"Traffic assignment serves as an important component in modeling flow distribution across infrastructure networks and supporting intelligent traffic management and urban planning. Fast algorithms for solving the stochastic user equilibrium (SUE) model are essential for enhancing computational performance and scalability of traffic assignment models applied to complex infrastructure networks. We augment the gradient projection (GP) algorithm for the SUE models through Barzilai–Borwein (BB) step size adaptation. For further optimizing computational performance, we explore iteration strategies within the GP algorithm: Jacobi parallelization, Gauss–Seidel sequential updating, and successive over-relaxation (SOR) with dynamic relaxation. Global convergence of these iterative methods in solving the SUE problems is theoretically established, with convergence conditions for the SOR derived. The results demonstrate the BB step size's superior performance, compared to alternative step size methods, across all network scales, and it achieves the best stability when demand factors and the dispersion parameter increase.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 30","pages":"5908-5926"},"PeriodicalIF":9.1,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593110","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