Automation in Construction最新文献_第4页

Automatic clash avoidance in steel reinforcement design using explainable graph neural networks and rebar embedding learning 基于可解释图神经网络和钢筋嵌入学习的钢筋设计自动避免碰撞

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-12 DOI: 10.1016/j.autcon.2025.106161

Mingkai Li , Boyu Wang , Xingyu Tao , Zhengyi Chen , Jack C.P. Cheng , Zinan Wu

{"title":"Automatic clash avoidance in steel reinforcement design using explainable graph neural networks and rebar embedding learning","authors":"Mingkai Li , Boyu Wang , Xingyu Tao , Zhengyi Chen , Jack C.P. Cheng , Zinan Wu","doi":"10.1016/j.autcon.2025.106161","DOIUrl":"10.1016/j.autcon.2025.106161","url":null,"abstract":"<div><div>Steel reinforcement design is essential for the structural integrity and durability of reinforced concrete (RC) structures. However, rebar clashes frequently occur due to conventional design processes lacking precise bar positioning, leading to time-consuming and error-prone onsite modifications. Existing 3D analysis tools for clash detection are unsuitable for rebar design, which must comply with structural analysis and regional specifications. Therefore, this paper proposes an automatic and proactive rebar clash avoidance approach using graph neural networks (GNN) and rebar embedding learning. Vector and graph representations are introduced to model clash scenarios, while a GNN-based diagnosis framework detects clashes and classifies them as solvable or unsolvable. For unsolvable clashes, explainable GNN identifies the underlying causes, while Rebar2Vec generates optimal design alternatives to improve feasibility. Solvable clashes are resolved using multi-objective optimization, ensuring compliance with building codes. Experimental results demonstrate the approach's effectiveness in generating clash-free rebar layouts at the design stage.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106161"},"PeriodicalIF":9.6,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820761","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

Enhancing stakeholder engagement and performance evaluation in building design using BIM and VR 在建筑设计中使用BIM和VR加强利益相关者的参与和绩效评估

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-12 DOI: 10.1016/j.autcon.2025.106191

Hongyang Li , Shuying Fang , Tingting Shi , Ned Wales , Martin Skitmore

{"title":"Enhancing stakeholder engagement and performance evaluation in building design using BIM and VR","authors":"Hongyang Li , Shuying Fang , Tingting Shi , Ned Wales , Martin Skitmore","doi":"10.1016/j.autcon.2025.106191","DOIUrl":"10.1016/j.autcon.2025.106191","url":null,"abstract":"<div><div>“VR + BIM” technologies address the growing complexity of construction evaluation systems driven by digital transformation, as traditional methods struggle to meet diverse stakeholder demands. This paper develops an automated evaluation framework integrating Building Information Modeling (BIM) and Virtual Reality (VR) to enhance decision-making processes and enable immersive multi-stakeholder collaboration and data-driven assessments. To verify system effectiveness, a “green + healthy” building scenario under sustainable construction trends is selected. Analysis based on a cloud model of 18 performance indicators reveals superior performance in user experience indicators such as “light environment”, “spatial privacy” but limitations in resource management indicators like “water conservation” and “source control.” The overall system performance transitions from “average” to “relatively good,” confirming automated assessment feasibility. The research contributes a quantitative methodology for BIM-VR integration, enhancing stakeholder engagement and building performance evaluation, offering micro-level design recommendations and macro-level policy interventions for “green + healthy” buildings, advancing digital, intelligent construction practices.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106191"},"PeriodicalIF":9.6,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820680","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

Automating construction contract review using knowledge graph-enhanced large language models 使用知识图增强的大型语言模型自动化施工合同审查

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-12 DOI: 10.1016/j.autcon.2025.106179

Chunmo Zheng , Saika Wong , Xing Su , Yinqiu Tang , Ahsan Nawaz , Mohamad Kassem

{"title":"Automating construction contract review using knowledge graph-enhanced large language models","authors":"Chunmo Zheng , Saika Wong , Xing Su , Yinqiu Tang , Ahsan Nawaz , Mohamad Kassem","doi":"10.1016/j.autcon.2025.106179","DOIUrl":"10.1016/j.autcon.2025.106179","url":null,"abstract":"<div><div>An effective and efficient review of construction contracts is essential for minimizing construction projects losses, but current methods are time-consuming and error-prone. Studies using methods based on Natural Language Processing (NLP) exist, but their scope is often limited to text classification or segmented label prediction. This paper investigates whether integrating Large Language Models (LLMs) and Knowledge Graphs (KGs) can enhance the accuracy and interpretability of automated contract risk identification. A tuning-free approach is proposed that integrates LLMs with a Nested Contract Knowledge Graph (NCKG) using a Graph Retrieval-Augmented Generation (GraphRAG) framework for contract knowledge retrieval and reasoning. Tested on international EPC contracts, the method achieves more accurate risk evaluation and interpretable risk summaries than baseline models. These findings demonstrate the potential of combining LLMs and KGs for reliable reasoning in tasks that are knowledge-intensive and specialized, such as contract review.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106179"},"PeriodicalIF":9.6,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820681","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

Temporal defect point localization in pipe CCTV Videos with Transformers 变形管CCTV视频的时间缺陷点定位

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-12 DOI: 10.1016/j.autcon.2025.106160

Zhu Huang , Gang Pan , Chao Kang , YaoZhi Lv

{"title":"Temporal defect point localization in pipe CCTV Videos with Transformers","authors":"Zhu Huang , Gang Pan , Chao Kang , YaoZhi Lv","doi":"10.1016/j.autcon.2025.106160","DOIUrl":"10.1016/j.autcon.2025.106160","url":null,"abstract":"<div><div>During the inspection and maintenance of underground pipe systems, technicians often spend considerable time searching for subtle defects in inspection videos captured under varying pipe conditions using Closed-Circuit Television (CCTV). The lack of feature extractors tailored for pipe images, combined with the complexity of pipe CCTV videos, poses substantial challenges to the performance and applicability of conventional frame-by-frame, image-based localization algorithms. To address these challenges, this paper introduces PipeTR, a transformer-driven, end-to-end network, offering enhanced insights into pipe CCTV video analysis by shifting from a frame-based to a video-based approach. The development of PipeTR aims to assist technicians by automating the most time-consuming step of the assessment, thereby improving both efficiency and accuracy. Experiments demonstrate that PipeTR outperforms other image-based, frame-by-frame analysis methods on real-world CCTV pipe inspection video datasets, achieving an average F1 score of 43.04%, which is a 5.35% improvement over the current state-of-the-art methods.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106160"},"PeriodicalIF":9.6,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820760","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

Enhancing bridge inspection data quality using machine learning 利用机器学习提高桥梁检测数据质量

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-12 DOI: 10.1016/j.autcon.2025.106182

Chenhong Zhang , Xiaoming Lei , Ye Xia

引用次数: 0

Performance prediction and sensitivity analysis of tunnel boring machine in various geological conditions using an ensemble extreme learning machine 基于集合极值学习机的不同地质条件下隧道掘进机性能预测及敏感性分析

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-09 DOI: 10.1016/j.autcon.2025.106169

Lianhui Jia , Lijie Jiang , Yongliang Wen , Jiulin Wu , Heng Wang

{"title":"Performance prediction and sensitivity analysis of tunnel boring machine in various geological conditions using an ensemble extreme learning machine","authors":"Lianhui Jia , Lijie Jiang , Yongliang Wen , Jiulin Wu , Heng Wang","doi":"10.1016/j.autcon.2025.106169","DOIUrl":"10.1016/j.autcon.2025.106169","url":null,"abstract":"<div><div>The selection of data modelling methods in the data-driven performance prediction of tunnel boring machines is a challenge since each method has its own advantages and disadvantages compared with each other. Extreme learning machine (ELM) exhibits the benefits of fast learning speed, better scalability, and generalization performance, and is easy to convert between neural networks-based and kernel function-based methods. Thus, this paper proposes an ensemble extreme learning machine model for the performance prediction of tunnel boring machines, aiming to take respective advantage of different ELM models. The proposed model is validated through six in-situ datasets of a tunnel boring machine with different geological conditions, showing that it can produce accurate dynamic and statistical performance prediction results (average error of 3.12 %). The sensitivity analysis results show that the sensitivities are mainly distributed on the parameters of driving system and chamber system when the excavation face is occupied by a single geological layer.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106169"},"PeriodicalIF":9.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808025","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

Analysis of masonry work activity recognition accuracy using a spatiotemporal graph convolutional network across different camera angles 基于时空图卷积网络的不同摄像机角度砌体工作活动识别精度分析

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-09 DOI: 10.1016/j.autcon.2025.106178

Sangyoon Yun , Sungkook Hong , Sungjoo Hwang , Dongmin Lee , Hyunsoo Kim

{"title":"Analysis of masonry work activity recognition accuracy using a spatiotemporal graph convolutional network across different camera angles","authors":"Sangyoon Yun , Sungkook Hong , Sungjoo Hwang , Dongmin Lee , Hyunsoo Kim","doi":"10.1016/j.autcon.2025.106178","DOIUrl":"10.1016/j.autcon.2025.106178","url":null,"abstract":"<div><div>Human activity recognition (HAR) in construction has gained attention for its potential to improve safety and productivity. While HAR research has shifted toward vision-based approaches, many studies typically use data from a specific angle, limiting understanding of how camera angles affect accuracy. This paper addresses this gap by using AlphaPose and Spatial-Temporal Graph Convolutional Network (ST-GCN) algorithms to analyze the impact of various camera angles on HAR accuracy in masonry work. Data was collected from seven angles (0° to 180°), with the frontal view only used for training. Results showed consistently high recognition accuracy (>80 %) for side views, while accuracy decreased as the camera shifted toward rear views, especially from directly behind due to occlusion. By quantifying HAR accuracy across angles, this study provides baseline data for predicting performance from various camera positions, improving camera placement strategies and enhancing monitoring system effectiveness on construction sites.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106178"},"PeriodicalIF":9.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808028","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

Bridge management with AI, UAVs, and BIM 桥梁管理与人工智能，无人机和BIM

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-09 DOI: 10.1016/j.autcon.2025.106170

Pablo Araya-Santelices , Zacarías Grande , Edison Atencio , José Antonio Lozano-Galant

引用次数: 0

Video-based evaluation of bolt loosening in steel bridges using multi-frame spatiotemporal feature correlation 基于多帧时空特征关联的钢桥螺栓松动视频评价

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-09 DOI: 10.1016/j.autcon.2025.106173

Baoxian Wang , Tao Wu , Weigang Zhao , Yilin Wu

引用次数: 0

Data-driven safety management of worker-equipment interactions using visual relationship detection and semantic analysis 使用视觉关系检测和语义分析的工人-设备交互的数据驱动安全管理

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-09 DOI: 10.1016/j.autcon.2025.106181

Liu Yipeng , Wang Junwu , Mehran Eskandari Torbaghan

{"title":"Data-driven safety management of worker-equipment interactions using visual relationship detection and semantic analysis","authors":"Liu Yipeng , Wang Junwu , Mehran Eskandari Torbaghan","doi":"10.1016/j.autcon.2025.106181","DOIUrl":"10.1016/j.autcon.2025.106181","url":null,"abstract":"<div><div>Existing technologies struggle to accurately identify interactions between workers and equipment, as well as the deep semantics of complex construction scenes. To address these limitations, this paper proposes an automated construction site safety management system designed to enhance scene understanding and identify safety hazards while focusing on hazard-area and personal protective equipment (PPE) interaction. The system transforms image information into worker-centric triplets and generates precise textual descriptions through semantic enhancement, enabling effective scene analysis. By comparing the generated descriptions with predefined hazard statements, the system identifies potential risks. Experimental results demonstrate a 9.6 % improvement in recall for Ng-mR@K metrics (K = 20, 50, 100). Additionally, the system successfully filters over 90 % of invalid relationships, achieving 83.7 % accuracy in semantic similarity matching, significantly enhancing detection precision and semantic understanding. By advancing from object detection to a structured image-to-triplet-to-text framework, this paper offers an efficient and reliable solution for automated construction site safety management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106181"},"PeriodicalIF":9.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808024","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