Automation in Construction最新文献_第3页

Simulation-based planning of earthmoving equipment for reducing greenhouse gas (GHG) emissions 基于仿真的土方设备规划，减少温室气体排放

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-30 DOI: 10.1016/j.autcon.2024.105841

Ali Mansouri , Hosein Taghaddos , Ala Nekouvaght Tak , Amir Sadatnya , Kamyab Aghajamali

引用次数: 0

BIM-integrated semantic framework for construction waste quantification and optimisation 用于建筑垃圾量化和优化的 BIM 集成语义框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-30 DOI: 10.1016/j.autcon.2024.105842

Subarna Sivashanmugam, Sergio Rodriguez Trejo, Farzad Rahimian

{"title":"BIM-integrated semantic framework for construction waste quantification and optimisation","authors":"Subarna Sivashanmugam, Sergio Rodriguez Trejo, Farzad Rahimian","doi":"10.1016/j.autcon.2024.105842","DOIUrl":"10.1016/j.autcon.2024.105842","url":null,"abstract":"<div><div>Quantification and optimisation of Construction Waste (CW) in the design stages are vital to implementing preventive CW management measures. Previous ICT-integrated CW models are not efficiently upscaled to achieve an interoperable and automated workflow. Therefore, this paper presents a BIM-integrated semantic framework for CW quantification and optimisation from the early design stages. A CW data model using Semantic-Web-Technologies (SWT) was developed and integrated with BIM. The results proved that unified data structure, standardised and granular information, established semantic relationships between building material and CW data, and diverse measurement units proposed in the framework facilitate seamless and dynamic information flows between BIM and CW platforms. The research outcomes are critical to improving interoperability and automation across the CW assessment process, enhancing the accuracy and reliability of results, supporting timely and integrated decision-making, and easing communication and collaboration among the supply-chain members. A test-case building demonstrates the application of the framework.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105842"},"PeriodicalIF":9.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553204","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

Crack instance segmentation using splittable transformer and position coordinates 使用可分割变换器和位置坐标进行裂纹实例分割

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-28 DOI: 10.1016/j.autcon.2024.105838

Yuanlin Zhao , Wei Li , Jiangang Ding , Yansong Wang , Lili Pei , Aojia Tian

引用次数: 0

Variable-depth large neighborhood search algorithm for cable routing in distributed photovoltaic systems 分布式光伏系统中电缆布线的可变深度大邻域搜索算法

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-26 DOI: 10.1016/j.autcon.2024.105839

Andong Qiu, Zhouwang Yang

引用次数: 0

Robust localization of shear connectors in accelerated bridge construction with neural radiance field 利用神经辐射场对加速桥梁施工中的剪力连接件进行稳健定位

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-24 DOI: 10.1016/j.autcon.2024.105843

Gyumin Lee , Ali Turab Asad , Khurram Shabbir , Sung-Han Sim , Junhwa Lee

引用次数: 0

From raw to refined: Data preprocessing for construction machine learning (ML), deep learning (DL), and reinforcement learning (RL) models 从原始到精炼：构建机器学习 (ML)、深度学习 (DL) 和强化学习 (RL) 模型的数据预处理

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-24 DOI: 10.1016/j.autcon.2024.105844

SeyedeZahra Golazad , Abbas Mohammadi , Abbas Rashidi , Mohammad Ilbeigi

{"title":"From raw to refined: Data preprocessing for construction machine learning (ML), deep learning (DL), and reinforcement learning (RL) models","authors":"SeyedeZahra Golazad , Abbas Mohammadi , Abbas Rashidi , Mohammad Ilbeigi","doi":"10.1016/j.autcon.2024.105844","DOIUrl":"10.1016/j.autcon.2024.105844","url":null,"abstract":"<div><div>As the use of predictive models in construction rapidly increases, the need for preprocessing raw construction data has become more critical. This systematic review investigates data preprocessing techniques for machine learning (ML), deep learning (DL), and reinforcement learning (RL) models in the construction domain. Through a comprehensive analysis of 457 studies, the prevalence of six data types (i.e., tabular, image, video frame, time series, text, and point cloud) and their respective preprocessing methods are examined. Key findings reveal data transformation, cleaning, reduction, augmentation, and scaling as fundamental preprocessing categories, with applications varying across data types. The paper highlights knowledge gaps, including limited synthetic data adoption, lack of standardized annotation practices, absence of comprehensive preprocessing frameworks, and need for automated labeling. Furthermore, critical considerations regarding data privacy, security, sharing, and management practices are discussed. The review underscores the pivotal role of robust data preprocessing in enabling reliable predictive models.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105844"},"PeriodicalIF":9.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534959","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 evaluation of struck-by-accident alert systems for road work zone safety 道路施工区安全事故报警系统性能评估

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-24 DOI: 10.1016/j.autcon.2024.105837

Qishen Ye, Yihai Fang, Nan Zheng

{"title":"Performance evaluation of struck-by-accident alert systems for road work zone safety","authors":"Qishen Ye, Yihai Fang, Nan Zheng","doi":"10.1016/j.autcon.2024.105837","DOIUrl":"10.1016/j.autcon.2024.105837","url":null,"abstract":"<div><div>Road work zones pose significant safety risks to both vehicles passing by and the construction workers moving within the work zones. Over recent years, significant research efforts have been dedicated to work zone safety, particularly by leveraging emerging technologies. This paper aims to review the literature on performance evaluation of safety technologies designed to mitigate struck-by hazards. This review identified 57 relevant publications focusing on technology evaluation, which were critically reviewed using the Four-component Cyber-Physical System (CPS) hierarchy and the Adapted Layer of Protection Analysis (ALOPA) framework. The CPS hierarchy-based review unveiled the focused components under evaluation, the relationship among these components, the methodologies employed, and the key performance results. The extent and completeness of the evaluation methods were examined through the ALOPA framework. The findings of this research highlight emerging trends that explore the impact of human factors on accident avoidance outcomes in risk-free virtual environments and suggest several prospective considerations as per ALOPA that can guide future research towards performance-based evaluations and design optimisations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105837"},"PeriodicalIF":9.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534960","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

Digital twins in bridge engineering for streamlined maintenance and enhanced sustainability 桥梁工程中的数字双胞胎简化维护工作并提高可持续性

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-23 DOI: 10.1016/j.autcon.2024.105834

M. Franciosi, M. Kasser, M. Viviani

引用次数: 0

Topology-aware mamba for crack segmentation in structures 用于结构裂缝分割的拓扑感知 Mamba

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-23 DOI: 10.1016/j.autcon.2024.105845

Xin Zuo , Yu Sheng , Jifeng Shen , Yongwei Shan

引用次数: 0

Virtual in-situ modeling between digital twin and BIM for advanced building operations and maintenance 数字孪生和 BIM 之间的虚拟原位建模，用于先进的楼宇运营和维护

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-23 DOI: 10.1016/j.autcon.2024.105823

Sungmin Yoon , Jeyoon Lee , Jiteng Li , Peng Wang

{"title":"Virtual in-situ modeling between digital twin and BIM for advanced building operations and maintenance","authors":"Sungmin Yoon , Jeyoon Lee , Jiteng Li , Peng Wang","doi":"10.1016/j.autcon.2024.105823","DOIUrl":"10.1016/j.autcon.2024.105823","url":null,"abstract":"<div><div>A virtual model that mathematically represents operational behaviors is essential for implementing the concepts of digital twins (DTs) and building information modeling (BIM) to achieve intelligent, optimal building operations. However, current research lacks an approach to reliably construct virtual models. This paper introduces a concept named virtual in-situ modeling (VIM), designed to comprehensively represent building behaviors. The VIM framework is based on five key aspects: modeling environments, model types, modeling sources, modeling approaches, and model fusion techniques. VIM bridges BIM and DT, enabling virtual modeling during the operational phase and enhancing both BIM-based DT and DT-enhanced BIM. Case studies conducted using real building operations demonstrate the effectiveness of VIM, achieving a highly accuracy (RMSE of 0.24 °C). Additionally, the VIM-assisted fault detection and diagnosis (FDD) provided early detection and diagnostic estimation, outperforming FDD without the virtual model. This paper highlights the potential of VIM for advanced building operations and maintenance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105823"},"PeriodicalIF":9.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534956","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