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

Vision‐based adaptive cross‐domain online product recommendation for 3D design models 基于视觉的3D设计模型自适应跨域在线产品推荐

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-14 DOI: 10.1111/mice.13495

Xiaoping Zhou, Qin Si, Gen Liu, Zhen‐Zhong Hu, Yukang Wang, Haoran Li, Maozu Guo, Song Xia, Chao Tan, Qingsheng Xie

{"title":"Vision‐based adaptive cross‐domain online product recommendation for 3D design models","authors":"Xiaoping Zhou, Qin Si, Gen Liu, Zhen‐Zhong Hu, Yukang Wang, Haoran Li, Maozu Guo, Song Xia, Chao Tan, Qingsheng Xie","doi":"10.1111/mice.13495","DOIUrl":"https://doi.org/10.1111/mice.13495","url":null,"abstract":"Three‐dimensional (3D) digital design is extensively adopted in the architecture, engineering, consulting, operations, and maintenance (AECOM) industry to enhance collaboration among stakeholders. Although recommendation systems are commonly employed to facilitate purchasing in e‐commerce websites, none involves recommending online products to users from 3D building design models due to dimensional and stylistic discrepancies. This study proposes a vision‐based adaptive cross‐domain online product recommendation method, VacRed, for 3D building design models. First, a cross‐domain approach is proposed to transform design models into e‐commerce images, addressing discrepancies in dimension and style between them. Second, an adaptive mechanism is introduced to solve the issue of image quality instability caused by variations in generator weights during the training process of generative models. Third, a cross‐domain product recommendation system is developed based on deep learning to recommend the top k relevant online products for a given building design product. Finally, experiments were conducted to ascertain the effectiveness of the VacRed method. The experimental results of this method demonstrate its excellent performance, achieving a precision rate (PR) of 87.20% and a mean average precision of 83.65%. This study effectively connects two main stages in the AECOM industry, design and purchasing, and two large communities, design and e‐commerce.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"52 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946116","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

Hierarchical adaptive cross‐coupled control of traffic signals and vehicle routes in large‐scale road network 大规模道路网络中交通信号和车辆路线的分层自适应交叉耦合控制

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-14 DOI: 10.1111/mice.13508

Yizhuo Chang, Yilong Ren, Han Jiang, Daocheng Fu, Pinlong Cai, Zhiyong Cui, Aoyong Li, Haiyang Yu

{"title":"Hierarchical adaptive cross‐coupled control of traffic signals and vehicle routes in large‐scale road network","authors":"Yizhuo Chang, Yilong Ren, Han Jiang, Daocheng Fu, Pinlong Cai, Zhiyong Cui, Aoyong Li, Haiyang Yu","doi":"10.1111/mice.13508","DOIUrl":"https://doi.org/10.1111/mice.13508","url":null,"abstract":"Traffic signal timing and vehicle routing have been empirically demonstrated as the two most promising paradigms for network‐level urban road traffic management. However, mainstream studies based on Wardrop's theory continues to treat these two modules separately without achieving effective coupling. Optimization‐based methods face the challenge of increasing computational complexity as urban scales continue to expand, constrained to small‐scale road networks. To address the above challenges, this paper proposes HAC3, a hierarchical adaptive cross‐coupled control method for network‐wide traffic management. HAC3 utilizes a rolling horizon architecture, comprising a fast update stage and a slow update stage. The core of the slow update stage is a spatiotemporal superposition vehicle route planning (SSP) module, which assigns the optimal route to each connected vehicle (CV) based on the road network state and the traffic signal timing of each intersection, and clarifies priority in right‐of‐way allocation to avoid falling into local optimal. The fast update stage is used for multi‐intersection adaptive traffic signal control (TSC), taking the intersection state and vehicle routes as inputs to optimize the signal timing scheme. Through the asynchronous cross‐coupling optimization of the two stages, the road network efficiency can be improved while ensuring equilibrium. Experimental results show that HAC3 achieves superior convergence performance on both synthetic and real‐world road network data sets, outperforming baseline methods and proving its scalability to large‐scale road networks. Plug‐and‐play experiments indicate the proposed HAC3 framework can integrate with other mainstream signal control models.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946357","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

Multi-task graph-based model for metro flow prediction under dynamic urban conditions 动态城市条件下基于多任务图的地铁流量预测模型

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-12 DOI: 10.1111/mice.13505

Lu Zhao, Linmu Zou, Zijia Wang, Taoran Song, Paul Schonfeld, Feng Chen, Rui Li, Pengcheng Li

{"title":"Multi-task graph-based model for metro flow prediction under dynamic urban conditions","authors":"Lu Zhao, Linmu Zou, Zijia Wang, Taoran Song, Paul Schonfeld, Feng Chen, Rui Li, Pengcheng Li","doi":"10.1111/mice.13505","DOIUrl":"https://doi.org/10.1111/mice.13505","url":null,"abstract":"Accurately predicting metro commuter flows under changing urban conditions is essential for guiding infrastructure investments and service planning. However, existing methods show limited adaptability to evolving urban conditions. To address this, we propose an adaptive graph sharing embedding cascade interaction network (AGSECIN), which establishes a dynamic mapping relationship between changing urban conditions and commuter flows, enabling accurate predictions of metro inflows, outflows, and origin-destination (OD) flows simultaneously. A graph attention network is built on the long-term graph to capture the spatiotemporal evolving patterns of urban conditions. Then, an adaptive supply–demand sharing embedding network is designed to model the interaction between origin supply and destination demand. Finally, an adaptive feature interaction layer is developed to uncover the complex high-order relations among passenger flows and urban conditions. Experimental results on real-world Beijing datasets demonstrate the superior performance of AGSECIN, compared to contemporary models. Ablation experiments confirm the robustness of our model.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"51 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940449","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

Automated form‐finding method of spoke cable net structures using physics‐constrained neural network 基于物理约束神经网络的轮辐索网结构自动找形方法

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-12 DOI: 10.1111/mice.13491

Xuanzhi Li, Yue Liu, Suduo Xue, Tafsirojjaman Tafsirojjaman

{"title":"Automated form‐finding method of spoke cable net structures using physics‐constrained neural network","authors":"Xuanzhi Li, Yue Liu, Suduo Xue, Tafsirojjaman Tafsirojjaman","doi":"10.1111/mice.13491","DOIUrl":"https://doi.org/10.1111/mice.13491","url":null,"abstract":"The spoke cable‐net structure is a typical flexible tensile structure that relies solely on cables as load‐bearing components. Its unique topological characteristics, composed of ring cables and radial cables, determine that the main challenge in its form‐finding lies in controlling the spatial configuration of the inner ring. Existing computational methods primarily rely on numerical iteration based on empirical trial and error, which makes it difficult to effectively address the multi‐variable coupling problem between the prestress distribution and the geometric configuration of the ring cables. Accordingly, this paper aims to establish a deep learning‐based autonomous form‐finding framework driven by geometric constraints and physical equations to achieve the simultaneous intelligent solution of prestress distribution and spatial configuration. The effectiveness and versatility of the proposed method are validated through case studies with various regular and irregular geometric forms. To enhance the precision of form‐finding for structures with intricate geometries, a dual‐optimizer strategy integrating the adaptive moment estimation and limited‐memory Broyden Fletcher Goldfarb Shanno algorithms is implemented. For a spoke cable‐net structure spanning 100 m, the intelligent form‐finding accuracy can be maintained within 1 cm, ensuring a satisfactory form‐finding outcome. The proposed deep neural network (DNN) method automatically correlates cable force distribution with geometric configuration, offering a novel computational approach and solution pathway for the automated shape determination and configuration design of flexible cable structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939988","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

Resource leveling strategy integrating soft logic and crew interruption in repetitive projects 在重复项目中集成软逻辑和人员中断的资源均衡策略

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-10 DOI: 10.1111/mice.13483

Zongyu Yao, Lihui Zhang, Jing Luo, Shaokun Wei

{"title":"Resource leveling strategy integrating soft logic and crew interruption in repetitive projects","authors":"Zongyu Yao, Lihui Zhang, Jing Luo, Shaokun Wei","doi":"10.1111/mice.13483","DOIUrl":"https://doi.org/10.1111/mice.13483","url":null,"abstract":"In construction projects, resource fluctuations not only decrease work efficiency but also lead to high costs. However, current resource leveling strategies exhibit limitations in crew‐based scheduling mechanisms, constraining the flexibility of resource allocation. This study addresses the resource leveling problem of repetitive projects, focusing on leveling resource utilization from the perspective of crews. First, the resource leveling strategy integrating crew interruptions and soft logic is elaborated, along with a discussion of its advantages. Then, a mixed integer linear programming (MILP) model is constructed to minimize the total deviation in resource utilization. Further, the MILP model is then transformed into a constraint programming model using the optimization programming language and designing a branch‐and‐search algorithm. Finally, three actual construction projects are used to compare the integrated strategy with six different resource leveling strategies. The results show that, compared to the known optimal strategy, the proposed strategy reduces the average resource peak by 17% and the average total resource deviation by 47%. This research provides an effective strategy for project managers to enhance project stability.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"54 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932518","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 13 封面图片，第40卷，第13期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-09 DOI: 10.1111/mice.13509

引用次数: 0

Cover Image, Volume 40, Issue 13 封面图片，第40卷，第13期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-09 DOI: 10.1111/mice.13510

引用次数: 0

Zero-shot framework for construction equipment task monitoring 施工设备任务监控的零射击框架

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-07 DOI: 10.1111/mice.13506

Jaewon Jeoung, Seunghoon Jung, Taehoon Hong

引用次数: 0

Excavator 3D pose estimation from point cloud with self-supervised deep learning 基于自监督深度学习的点云挖掘机三维姿态估计

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-03 DOI: 10.1111/mice.13500

Mingyu Zhang, Wenkang Guo, Jiawen Zhang, Shuai Han, Heng Li, Hongzhe Yue

{"title":"Excavator 3D pose estimation from point cloud with self-supervised deep learning","authors":"Mingyu Zhang, Wenkang Guo, Jiawen Zhang, Shuai Han, Heng Li, Hongzhe Yue","doi":"10.1111/mice.13500","DOIUrl":"https://doi.org/10.1111/mice.13500","url":null,"abstract":"Pose estimation of excavators is a fundamental yet challenging task with significant implications for intelligent construction. Traditional methods based on cameras or sensors are often limited by their ability to perceive spatial structures. To address this, 3D light detection and ranging has emerged as a promising paradigm for excavator pose estimation. However, these methods face significant challenges: (1) accurate 3D pose annotations are labor-intensive and costly, and (2) excavators exhibit complex kinematics and geometric structures, further complicating pose estimation. In this study, a novel framework is proposed for full-body excavator pose estimation directly from 3D point clouds, without relying on manual 3D annotations. The excavator pose is parameterized using pose parameters of geometric primitives under kinematic constraints. A unified deep network is designed to predict pose parameters from point clouds. The network is initially pre-trained on synthetic data to provide parameter initialization and then fine-tuned using real-world data. To facilitate label-free training, the self-supervised loss functions are designed by exploiting the geometric and kinematic consistency between point clouds and excavators. Experimental results on real-world construction sites demonstrate the effectiveness and robustness of the proposed method, achieving an average pose estimation accuracy of 0.26 m. The method also exhibits promising performance across various excavator operational scenarios, highlighting its potential for real-world applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"22 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901793","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

Spatially aware Markov chain-based deterioration prediction of bridge components using a Graph Transformer 基于空间感知马尔可夫链的桥梁构件劣化预测

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-05-03 DOI: 10.1111/mice.13497

Shogo Inadomi, Pang-jo Chun

{"title":"Spatially aware Markov chain-based deterioration prediction of bridge components using a Graph Transformer","authors":"Shogo Inadomi, Pang-jo Chun","doi":"10.1111/mice.13497","DOIUrl":"10.1111/mice.13497","url":null,"abstract":"<p>This study proposes a Markov chain-based deterioration prediction framework that incorporates spatial relationships between structural components. Despite spatial clustering and propagation of damage, conventional research has left spatial dependencies underexplored. This study constructs graph representations that reflect component adjacency and employs a Graph Transformer to capture both local and distant dependencies. Synthetic datasets confirm the advantage of introducing spatial positioning in settings with probabilistic transitions and various component topologies. The model is also tested on a semi-automatically generated Tokyo girder bridge dataset. It improves precision sixfold over the percentage prediction method, surpasses a graph neural network, and outperforms a Transformer model without spatial information by five points on the real dataset and eight on a synthetic dataset. Attention weight analysis reveals that the model captures spatial dependencies and aligns with empirical deterioration mechanisms, offering interpretability. The proposed approach enables detailed element-level deterioration predictions, enhancing maintenance planning and safety.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 14","pages":"1932-1955"},"PeriodicalIF":8.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901801","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