Automation in Construction最新文献_第10页

Exploratory study on time-delayed excavator teleoperation in virtual lunar construction simulation: Task performance and operator behavior 虚拟月球施工模拟中的延时挖掘机远程操作探索性研究：任务性能和操作员行为

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-12 DOI: 10.1016/j.autcon.2024.105871

Miran Seo , Samraat Gupta , Youngjib Ham

{"title":"Exploratory study on time-delayed excavator teleoperation in virtual lunar construction simulation: Task performance and operator behavior","authors":"Miran Seo , Samraat Gupta , Youngjib Ham","doi":"10.1016/j.autcon.2024.105871","DOIUrl":"10.1016/j.autcon.2024.105871","url":null,"abstract":"<div><div>Building sustainable habitats on the moon has been planned for decades. However, applying fully automated construction systems is still challenging in altered environments. Teleoperation, which is the remote control of the machine, can serve as an intermediate phase before achieving fully autonomous systems. Since the teleoperation between operators on the earth-ground and robots on the lunar surface introduces inevitable communication time delays under a deep space network system, it is important to understand its impact on task performance and operator behaviors in teleoperated construction tasks. This paper develops a simulated lunar environment for excavator teleoperation systems in virtual reality to examine task performance and operator behaviors in time delay conditions. The outcomes indicate that time delays significantly degrade task performance, and the operators modify their control strategies to cope with the time delay conditions. The findings will contribute to understanding human behaviors in time-delayed teleoperation of lunar construction tasks.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105871"},"PeriodicalIF":9.6,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637448","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

Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval 检索增强生成和微调大语言模型在建筑安全管理知识检索中的性能比较

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-12 DOI: 10.1016/j.autcon.2024.105846

Jungwon Lee , Seungjun Ahn , Daeho Kim , Dongkyun Kim

{"title":"Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval","authors":"Jungwon Lee , Seungjun Ahn , Daeho Kim , Dongkyun Kim","doi":"10.1016/j.autcon.2024.105846","DOIUrl":"10.1016/j.autcon.2024.105846","url":null,"abstract":"<div><div>Construction safety standards are in unstructured formats like text and images, complicating their effective use in daily tasks. This paper compares the performance of Retrieval-Augmented Generation (RAG) and fine-tuned Large Language Model (LLM) for the construction safety knowledge retrieval. The RAG model was created by integrating GPT-4 with a knowledge graph derived from construction safety guidelines, while the fine-tuned LLM was fine-tuned using a question-answering dataset derived from the same guidelines. These models' performance is tested through case studies, using accident synopses as a query to generate preventive measurements. The responses were assessed using metrics, including cosine similarity, Euclidean distance, BLEU, and ROUGE scores. It was found that both models outperformed GPT-4, with the RAG model improving by 21.5 % and the fine-tuned LLM by 26 %. The findings highlight the relative strengths and weaknesses of the RAG and fine-tuned LLM approaches in terms of applicability and reliability for safety management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105846"},"PeriodicalIF":9.6,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637389","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

Predicting and managing risk interactions and systemic risks in infrastructure projects using machine learning 利用机器学习预测和管理基础设施项目中的风险互动和系统性风险

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-10 DOI: 10.1016/j.autcon.2024.105836

Ahmed Moussa , Mohamed Ezzeldin , Wael El-Dakhakhni

引用次数: 0

Mixed Reality-based MEP construction progress monitoring: Evaluation of methods for mesh-to-mesh comparison 基于混合现实的 MEP 施工进度监测：评估网格间比较方法

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-08 DOI: 10.1016/j.autcon.2024.105852

Boan Tao, Frédéric Bosché, Jiajun Li

{"title":"Mixed Reality-based MEP construction progress monitoring: Evaluation of methods for mesh-to-mesh comparison","authors":"Boan Tao, Frédéric Bosché, Jiajun Li","doi":"10.1016/j.autcon.2024.105852","DOIUrl":"10.1016/j.autcon.2024.105852","url":null,"abstract":"<div><div>Visually monitoring progress and geometric quality on site using Mixed Reality (MR) and overlaid Building Information Model (BIM model) is challenging, particularly in complex contexts like complex mechanical, electrical, and plumbing (MEP) systems. This paper proposes and evaluates four individual methods and three combined ones for automated object recognition and deviation evaluation, based on the matching and comparison of the 3D mesh captured on site by MR systems with the mesh geometry of the elements in the (as-designed) BIM model. The four individual methods include: (1) Bounding Box Occupation, (2) Point-to-Surface Distance, (3) Voxel Occupation, (4) Feature Matching. Three combined methods are Method <span><math><mrow><mn>1</mn><mo>∪</mo><mn>4</mn></mrow></math></span>, Method <span><math><mrow><mn>2</mn><mo>∪</mo><mn>4</mn></mrow></math></span> and Method <span><math><mrow><mn>3</mn><mo>∪</mo><mn>4</mn></mrow></math></span> (i.e. combining methods 1 and 4, 2 and 4, and 3 and 4, respectively). The methods are evaluated using both synthetic and real data of MEP construction works, with the Method <span><math><mrow><mn>1</mn><mo>∪</mo><mn>4</mn></mrow></math></span> yielding the best performance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105852"},"PeriodicalIF":9.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637403","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

RGB-LiDAR sensor fusion for dust de-filtering in autonomous excavation applications RGB-LiDAR 传感器融合用于自主挖掘应用中的粉尘去滤波

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-07 DOI: 10.1016/j.autcon.2024.105850

Tyler Parsons , Fattah Hanafi Sheikhha , Jaho Seo , Hanmin Lee

引用次数: 0

Robust optimization model for traceable procurement of construction materials considering contract claims 考虑合同索赔的建筑材料可追溯采购的稳健优化模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-06 DOI: 10.1016/j.autcon.2024.105847

Kaiyue Zhang , Jing Zhou , Yan Ning , Shang Gao

引用次数: 0

Self-supervised monocular depth estimation on construction sites in low-light conditions and dynamic scenes 低光照条件和动态场景下建筑工地的自监督单目深度估计

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-05 DOI: 10.1016/j.autcon.2024.105848

Jie Shen, Ziyi Huang, Lang Jiao

{"title":"Self-supervised monocular depth estimation on construction sites in low-light conditions and dynamic scenes","authors":"Jie Shen, Ziyi Huang, Lang Jiao","doi":"10.1016/j.autcon.2024.105848","DOIUrl":"10.1016/j.autcon.2024.105848","url":null,"abstract":"<div><div>Estimating construction scene depth from a single image is crucial for various downstream tasks. Self-supervised monocular depth estimation methods have recently achieved impressive results and demonstrated state-of-the-art performance. However, the low-light conditions and dynamic scenes on construction sites pose significant challenges to these methods, hindering their practical deployment. Therefore, an architecture called LLD-Depth is presented to address these challenges, including an improved ForkGAN model to generate paired low-light images from clear-day images, a new unifying learning method for accurately estimating monocular depth, motion flow, camera ego-motion, and its intrinsic parameters, as well as a training framework to estimate monocular depth under both low-light and clear-day conditions effectively. Finally, the effectiveness of monocular depth estimation in construction scenes is verified. LLD-Depth brings 16.67% and 20.17% gain in relative mean error for clear-day and low-light scenes and 2.60% and 1.80% gain in average order accuracy, achieving state-of-the-art performance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105848"},"PeriodicalIF":9.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586350","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

Rutting extraction from vehicle-borne laser point clouds 从车载激光点云中提取车辙

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-05 DOI: 10.1016/j.autcon.2024.105853

Xinjiang Ma , Dongjie Yue , Jintao Li , Ruisheng Wang , Jiayong Yu , Rufei Liu , Maolun Zhou , Yifan Wang

{"title":"Rutting extraction from vehicle-borne laser point clouds","authors":"Xinjiang Ma , Dongjie Yue , Jintao Li , Ruisheng Wang , Jiayong Yu , Rufei Liu , Maolun Zhou , Yifan Wang","doi":"10.1016/j.autcon.2024.105853","DOIUrl":"10.1016/j.autcon.2024.105853","url":null,"abstract":"<div><div>Rutting is a type of structural road damage that seriously affects traffic safety, and rutting conditions are typically analyzed only from a two-dimensional cross-sectional perspective. Rutting detection currently lacks directional features and trends along the traveling direction. To address this issue, this paper develops a rutting extraction methodology from vehicle-borne laser point clouds to reflect the actual rutting conditions. The proposed method locates rutting points from cross-sectional data and further integrates the spatial correlation information of continuous cross sections to accurately extract dangerous rutting regions and longitudinal feature lines. Comprehensive experiments show that the Recall and Precision of rutting extraction are higher than 85 % and 90 % respectively, while also exhibiting higher robustness compared to other methods. These results demonstrate the effectiveness and accuracy of the proposed method for rutting extraction in large-scale road scenes. Future research will focus on deep learning-based road damage monitoring and provide valuable references for traffic management, road maintenance, and safety.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105853"},"PeriodicalIF":9.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586349","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 reinforcement of 3D-printed engineered cementitious composite beams 自动加固 3D 打印工程水泥基复合梁

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-02 DOI: 10.1016/j.autcon.2024.105851

Manfang Lin , Lingzhi Li , Fangming Jiang , Yao Ding , Fan Yu , Fangyuan Dong , Kequan Yu

{"title":"Automated reinforcement of 3D-printed engineered cementitious composite beams","authors":"Manfang Lin , Lingzhi Li , Fangming Jiang , Yao Ding , Fan Yu , Fangyuan Dong , Kequan Yu","doi":"10.1016/j.autcon.2024.105851","DOIUrl":"10.1016/j.autcon.2024.105851","url":null,"abstract":"<div><div>The advancement of emerging 3D concrete printing (3DCP) has been hindered by two significant challenges: the weak tensile properties of conventional concrete and the difficulty of simultaneously placing reinforcement during printing. In this paper, engineered cementitious composites (ECC) with superior tensile properties along with an in-process reinforcement technique through laying CFRP meshes between ECC layers were strategically composited. Four-point bending tests were performed on 3DP-ECC beams reinforced with different layers and configurations of CFRP mesh. Experimental results demonstrated that CFRP meshes can deform collaboratively with ECC, and enhance the load bearing capacity of 3DP-ECC beams to 1.22–2.01 times compared to that of unreinforced beam, while moderately decrease the deformation capacity of printed beams. A theoretical model for predicting the load bearing capacity and bending moment-curvature relationship of 3DP-ECC beams was further proposed. This paper validated the feasibility and effectiveness of CFRP mesh in reinforcing 3DP-ECC beams for efficient 3DCP construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105851"},"PeriodicalIF":9.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573399","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

Decision support for railway track facility management using OpenBIM 利用 OpenBIM 为铁路轨道设施管理提供决策支持

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-31 DOI: 10.1016/j.autcon.2024.105840

Zeru Liu , Jung In Kim , Wi Sung Yoo

{"title":"Decision support for railway track facility management using OpenBIM","authors":"Zeru Liu , Jung In Kim , Wi Sung Yoo","doi":"10.1016/j.autcon.2024.105840","DOIUrl":"10.1016/j.autcon.2024.105840","url":null,"abstract":"<div><div>Despite rapid advancements in track condition assessment technologies, current railway track facility management (FM) often results in cost-ineffectiveness as well as maintenance- and operation-inefficient outcomes. However, the challenges in current practice and the requirements for enhancing track FM decision-making processes have not been identified in a comprehensive and structured manner by any existing study. To address this gap, case studies and interviews were conducted to identify the challenges, along with the necessary information and functions. Based on these findings, a conceptual decision-support framework for railway track FM, utilizing openBIM, was proposed. This framework addresses data integration, track condition diagnosis, root cause identification considering the interrelationships among multiple components, long-term deterioration prediction, and FM plan optimization. A focus group interview was also conducted, and existing studies were examined to validate the proposed framework, which was found to support informed decision-making for railway track FM, thereby enhancing predictive maintenance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105840"},"PeriodicalIF":9.6,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561215","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