Automation in Construction最新文献_第7页

Automated PPE compliance monitoring in industrial environments using deep learning-based detection and pose estimation 在工业环境中使用基于深度学习的检测和姿态估计的自动化PPE合规性监控

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-08 DOI: 10.1016/j.autcon.2025.106231

Leopoldo López , Jonay Suárez-Ramírez , Miguel Alemán-Flores , Nelson Monzón

{"title":"Automated PPE compliance monitoring in industrial environments using deep learning-based detection and pose estimation","authors":"Leopoldo López , Jonay Suárez-Ramírez , Miguel Alemán-Flores , Nelson Monzón","doi":"10.1016/j.autcon.2025.106231","DOIUrl":"10.1016/j.autcon.2025.106231","url":null,"abstract":"<div><div>This paper presents an AI framework for automated detection of personal protective equipment (PPE) compliance in complex construction and industrial environments. Ensuring health and safety standards is essential for protecting workers engaged in construction, repair, or inspection activities. The framework leverages deep learning techniques for worker detection and pose estimation to enable accurate PPE identification under challenging conditions. The framework components are replaceable, and employ the InternImage-L detector for worker detection, ViTPose for pose estimation, and YOLOv7 for PPE recognition. A duplicate removal stage, combined with pose information, ensures PPE items are accurately assigned to individual workers. The approach addresses challenges like shadows, partial occlusions, or densely grouped workers. Evaluated on diverse datasets from real-world industrial settings, the framework achieves competitive precision and recall, particularly for critical PPE like helmets and vests, demonstrating robustness for safety monitoring and proactive risk management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106231"},"PeriodicalIF":9.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923445","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 embodied and operational carbon assessment in urban sustainable development 城市可持续发展中隐含和可操作碳评估的自动化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-07 DOI: 10.1016/j.autcon.2025.106245

Siavash Ghorbany , Ming Hu , Siyuan Yao , Matthew Sisk , Chaoli Wang

引用次数: 0

Automated safety risk management guidance enhanced by retrieval-augmented large language model 基于检索增强大语言模型的自动化安全风险管理指导

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-07 DOI: 10.1016/j.autcon.2025.106255

Seungwon Baek , Chan Young Park , Wooyong Jung

引用次数: 0

3D measurement of dynamic structures using monocular camera 单目相机动态结构的三维测量

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-06 DOI: 10.1016/j.autcon.2025.106240

Ken Kobayashi, Takashi Fuse

引用次数: 0

Large language model-based agent Schema and library for automated building energy analysis and modeling 基于大型语言模型的智能体架构和库，用于自动化建筑能源分析和建模

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-05 DOI: 10.1016/j.autcon.2025.106244

Liang Zhang , Xiaoqin Fu , Yanfei Li , Jianli Chen

引用次数: 0

Assessment of construction workers' fall-from-height risk using multi-physiological data and virtual reality 基于多生理数据和虚拟现实的建筑工人高空坠落风险评估

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-05 DOI: 10.1016/j.autcon.2025.106254

Francis Xavier Duorinaah , Samuel Oluwadamilare Olatunbosun , Jeong-Hun Won , Hung-Lin Chi , Min-Koo Kim

{"title":"Assessment of construction workers' fall-from-height risk using multi-physiological data and virtual reality","authors":"Francis Xavier Duorinaah , Samuel Oluwadamilare Olatunbosun , Jeong-Hun Won , Hung-Lin Chi , Min-Koo Kim","doi":"10.1016/j.autcon.2025.106254","DOIUrl":"10.1016/j.autcon.2025.106254","url":null,"abstract":"<div><div>Efficient identification of at-risk construction workers is crucial for reducing fall-from-height (FFH) accidents. However, current methods of evaluating worker FFH risk rely on manual inspections, which are ineffective because of the complex nature of construction sites. To address this issue, this paper presents a technique for FFH risk assessment using physiological data. A virtual reality experiment with three FFH risk scenarios was conducted, during which four categories of physiological data were recorded. Using the physiological data and machine learning algorithms, FFH risk classification models were developed. Three key findings are as follows. (1) All four physiological metrics showed significant changes in response to varying FFH risk levels (2) EEG was the most effective physiological metric for FFH risk assessment, achieving a test accuracy of 0.924 (3) Combining all four physiological categories provided the highest accuracy of 0.998. The findings demonstrate the feasibility of using physiological signals for effective FFH risk assessment.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106254"},"PeriodicalIF":9.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906968","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

Digital twin framework to enhance facility management for relocatable modular buildings 数字孪生框架，加强可移动模块化建筑的设施管理

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-05 DOI: 10.1016/j.autcon.2025.106249

Truong Dang Hoang Nhat Nguyen , Dang Huy Ly , Hanbyeol Jang , Han Nguyen Ngoc Dinh , Yonghan Ahn

{"title":"Digital twin framework to enhance facility management for relocatable modular buildings","authors":"Truong Dang Hoang Nhat Nguyen , Dang Huy Ly , Hanbyeol Jang , Han Nguyen Ngoc Dinh , Yonghan Ahn","doi":"10.1016/j.autcon.2025.106249","DOIUrl":"10.1016/j.autcon.2025.106249","url":null,"abstract":"<div><div>Relocatable modular buildings (RMBs) are gaining recognition for their innovation, efficiency, and sustainability, especially in addressing urgent needs during natural disasters or pandemics. Their inherent flexibility, mobility, and scalability enable rapid deployment and adaptation to changing needs. However, managing multiple modular units and their relocation and reconfiguration over time presents significant challenges. This paper proposes a digital twin framework that integrates building information modeling (BIM), the internet of things (IoT), and geographic information systems (GIS) to enhance facility management for RMBs. Through a detailed case study of a relocatable modular school system, the framework's application is illustrated, showcasing its ability to streamline lifecycle management of modular construction, from initial design and fabrication to deployment, operation, and reconfiguration. The results highlight the potential of digital twin technology in fostering more sustainable, efficient, and resilient modular building environments, contributing to the evolution of smart construction practices and adaptive architecture</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106249"},"PeriodicalIF":9.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903915","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

Synchronization in prefabricated off-site fit-out with graduation intelligent manufacturing system 预制非现场装配与毕业智能制造系统同步

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-05 DOI: 10.1016/j.autcon.2025.106243

Haoran Ding , Ray Y. Zhong , George Q. Huang

{"title":"Synchronization in prefabricated off-site fit-out with graduation intelligent manufacturing system","authors":"Haoran Ding , Ray Y. Zhong , George Q. Huang","doi":"10.1016/j.autcon.2025.106243","DOIUrl":"10.1016/j.autcon.2025.106243","url":null,"abstract":"<div><div>Prefabricated Off-site Fit-out (POF) plays a critical but often overlooked role in Modular Prefabricated Construction (MPC). Due to fragmented and asymmetric information, inconsistent production and intralogistics operations, and production uncertainty, the POF faces significant synchronization challenges. To address these challenges, this paper proposes the Graduation Intelligent Manufacturing System (GiMS), inspired by the university graduation ceremonies. GiMS creates a smart environment that supports POF real-time decision-making under uncertainties. A ticket-based synchronization mechanism is used to coordinate production and intralogistics operations dynamically. Numerical analysis verified that GiMS significantly improves synchronization performance and reduces earliness and tardiness across various uncertainty scenarios. The experiment results confirmed its effectiveness in optimizing POF processes. This paper presents a synchronization framework explicitly designed for POF, providing practical insights for applying smart manufacturing solutions in modular construction environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106243"},"PeriodicalIF":9.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903916","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

Prompts to layouts: Hybrid graph neural network and agent-based model for generative architectural design 布局提示：生成式建筑设计的混合图神经网络和基于代理的模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-05 DOI: 10.1016/j.autcon.2025.106253

Yangpeng Xin, Ying Zhou, Yuanyuan Liu

引用次数: 0

Transfer learning for intelligent design of lightweight Strain-Hardening Ultra-High-Performance Concrete (SH-UHPC) 轻量化应变硬化超高性能混凝土智能设计的迁移学习

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-03 DOI: 10.1016/j.autcon.2025.106241

Yi-Xin Zhang , Qiao Zhang , Ling-Yu Xu , Wei Hou , You-Shui Miao , Yang Liu , Bo-Tao Huang

{"title":"Transfer learning for intelligent design of lightweight Strain-Hardening Ultra-High-Performance Concrete (SH-UHPC)","authors":"Yi-Xin Zhang , Qiao Zhang , Ling-Yu Xu , Wei Hou , You-Shui Miao , Yang Liu , Bo-Tao Huang","doi":"10.1016/j.autcon.2025.106241","DOIUrl":"10.1016/j.autcon.2025.106241","url":null,"abstract":"<div><div>The design of lightweight Ultra-High-Performance Concrete (UHPC) requires pursuing superior material efficiency, which involves striking a delicate balance between ultra-high compressive strength and reduced material density. This paper compiled a comprehensive dataset of 176 ordinary UHPC and 72 lightweight UHPC and proposed a framework that integrates both transfer learning and Bayesian Optimization-enhanced Extreme Gradient Boosting (BO-XGBoost) for the design of lightweight UHPC. The BO-XGBoost model was pre-trained through hyperparameter tuning, laying a solid foundation for predicting material efficiency. Transfer learning was incorporated to address data limitations in lightweight UHPC while capturing its unique properties. The framework achieved 98.2 % accuracy in forward prediction and 94.7 % in reverse design. Notably, a lightweight UHPC using local materials was successfully developed and exhibited strain-hardening behavior based on the proposed approach, pushing the performance envelope of existing UHPC materials. This approach provided a solution for the design of lightweight strain-hardening UHPC towards superior material efficiency.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106241"},"PeriodicalIF":9.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898673","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