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

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

A methodology for incident detection in sectorized water distribution networks based on pressure and flow data 基于压力和流量数据的分区配水网络事故检测方法

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-29 DOI: 10.1111/mice.13493

Alicia Robles-Velasco, Luis Onieva, José Guadix, Pablo Cortés

引用次数: 0

Cover Image, Volume 40, Issue 12 封面图片，第40卷，第12期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-28 DOI: 10.1111/mice.13498

引用次数: 0

An integrated framework for multiple traffic anomalies detection on highways using vehicle trajectories 基于车辆轨迹的高速公路多重交通异常检测集成框架

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-28 DOI: 10.1111/mice.13494

Zhiyuan Liu, Anfeng Jiang, Zhirui Wang, Zhen Zhou, Lue Fang, Qixiu Cheng, Ziyuan Gu

{"title":"An integrated framework for multiple traffic anomalies detection on highways using vehicle trajectories","authors":"Zhiyuan Liu, Anfeng Jiang, Zhirui Wang, Zhen Zhou, Lue Fang, Qixiu Cheng, Ziyuan Gu","doi":"10.1111/mice.13494","DOIUrl":"https://doi.org/10.1111/mice.13494","url":null,"abstract":"Fast and accurate identification of traffic anomalies on highways is of utmost importance. This study presents an integrated framework for multiple traffic anomaly detection on highways using vehicle trajectories. The framework addresses both macroscopic congestion patterns and microscopic driving behaviors, offering a comprehensive solution that simultaneously detects multiple anomalies within a unified framework. The developed framework comprises three main components: data acquisition and preprocessing, vehicle trajectory recognition, and traffic anomaly detection. The former two components are responsible for acquiring real-time vehicle trajectories on highways. With such trajectory information and the continuously monitored short-term traffic state, the latter component seeks to simultaneously detect all the traffic anomalies via a tailored sub-algorithm for each of them. For macroscopic anomaly detection, an algorithm for detecting stop-and-go waves by constructing localized shockwaves is proposed to capture the propagation of traffic congestion waves even in limited field-of-view scenarios. For microscopic anomaly detection, a dynamic background traffic state updating mechanism is introduced, allowing the framework to adaptively integrate historical traffic data and environmental factors. Additionally, a double-layer stacking framework based on unsupervised methods is designed to integrate diverse feature types and addressing perspective distortions. The developed framework is tested in experiments on both simulation and real-world data on highways. The results confirm its effectiveness in the simultaneous detection of multiple traffic anomalies within an integrated framework.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"94 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884948","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