Automation in Construction最新文献_第8页

Digital twin-enabled safety monitoring system for seamless worker-robot collaboration in construction 数字双体安全监控系统，实现施工中工人与机器人的无缝协作

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-25 DOI: 10.1016/j.autcon.2025.106147

Xiao Lin, Ziyang Guo, Xinxiang Jin, Hongling Guo

引用次数: 0

Modeling heterogeneous spatiotemporal pavement data for condition prediction and preventive maintenance in digital twin-enabled highway management 在数字双通道公路管理中，为状态预测和预防性维护建模异构时空路面数据

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-25 DOI: 10.1016/j.autcon.2025.106134

Linjun Lu, Alix Marie d'Avigneau, Yuandong Pan, Zhaojie Sun, Peihang Luo, Ioannis Brilakis

{"title":"Modeling heterogeneous spatiotemporal pavement data for condition prediction and preventive maintenance in digital twin-enabled highway management","authors":"Linjun Lu, Alix Marie d'Avigneau, Yuandong Pan, Zhaojie Sun, Peihang Luo, Ioannis Brilakis","doi":"10.1016/j.autcon.2025.106134","DOIUrl":"10.1016/j.autcon.2025.106134","url":null,"abstract":"<div><div>Pavement preventive maintenance is one of the most fundamental use cases when deploying digital twins (DTs) for highway infrastructure management. To achieve this, it is essential to accurately predict the pavement conditions in future years. This paper developed a Spatial-Temporal Graph Attention network (STGAT) that can effectively capitalize on both spatial and temporal dependencies while addressing inherent heterogeneity in pavement data for more accurate condition predictions. On top of this, a structured assessment procedure was introduced to determine the need for preventive maintenance on road sections based on the STGAT predictions. A case study on the highway network in the United Kingdom was conducted to evaluate the method's performance. The results showed that the proposed method can achieve superior accuracy for pavement condition prediction and subsequent preventive maintenance assessment compared to existing methods, thus signifying its potential to improve the effectiveness of DTs for highway infrastructure management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106134"},"PeriodicalIF":9.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681365","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

Embedded machine vision sensor with portable imaging device and high durability 嵌入式机器视觉传感器，具有便携式成像装置，高耐用性

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-24 DOI: 10.1016/j.autcon.2025.106143

Pengfei Wu , Han Yuan , Bingchuan Bai , Bo Lu , Weijie Li , Xuefeng Zhao

引用次数: 0

Safety-constrained Deep Reinforcement Learning control for human–robot collaboration in construction 基于安全约束的建筑人机协作深度强化学习控制

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-23 DOI: 10.1016/j.autcon.2025.106130

Kangkang Duan , Zhengbo Zou

引用次数: 0

Thickness optimisation in 3D printed concrete structures 3D打印混凝土结构厚度优化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-23 DOI: 10.1016/j.autcon.2025.106076

Romain Mesnil, Pedro Sarkis Rosa, Léo Demont

引用次数: 0

Automated region extraction and displacement detection for paving blocks adjacent to deep excavation using photogrammetry 基于摄影测量的深基坑附近铺装块的自动区域提取与位移检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-22 DOI: 10.1016/j.autcon.2025.106126

Jung Woo Kim , Jinman Jung , Taesik Kim

{"title":"Automated region extraction and displacement detection for paving blocks adjacent to deep excavation using photogrammetry","authors":"Jung Woo Kim , Jinman Jung , Taesik Kim","doi":"10.1016/j.autcon.2025.106126","DOIUrl":"10.1016/j.autcon.2025.106126","url":null,"abstract":"<div><div>Construction projects in urban environments often involve deep excavations, leading to ground deformations, such as settlement or uplift, which can harm nearby infrastructure. Monitoring is crucial for ensuring stability and safety because the displacement of paving blocks can indicate subsurface deformation. Traditional methods, like using settlement markers and leveling devices, only measure specific points rather than the entire surface deformation. Recent advancements in terrestrial photogrammetry offer point cloud data (PCD) to track sidewalk displacement but still require manual definition of the monitoring zone and displacement assessments. This paper focuses on automating Region Extraction and Displacement Detection (RED2) using PCD from photogrammetry. It describes the development of an algorithm that involves extracting the regions of interest and refining the surfaces. Displacement detection was then performed by identifying displacements and removing false positives. The proposed method provides an automated solution for monitoring ground deformations and enhancing safety measures for infrastructure adjacent to excavation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106126"},"PeriodicalIF":9.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681492","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

Integrating text parsing and object detection for automated monitoring of finishing works in construction projects 将文本解析与对象检测相结合，实现建筑工程整理工程的自动化监控

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-22 DOI: 10.1016/j.autcon.2025.106139

Juseok Oh , Sungkook Hong , Byungjoo Choi , Youngjib Ham , Hyunsoo Kim

{"title":"Integrating text parsing and object detection for automated monitoring of finishing works in construction projects","authors":"Juseok Oh , Sungkook Hong , Byungjoo Choi , Youngjib Ham , Hyunsoo Kim","doi":"10.1016/j.autcon.2025.106139","DOIUrl":"10.1016/j.autcon.2025.106139","url":null,"abstract":"<div><div>Construction process monitoring traditionally relies on manual inspections and document cross-referencing, leading to inefficiencies in project management. Despite advances enabling computer vision-based monitoring and automated document analysis, integrating these technologies remains challenging, particularly in connecting field data with work documentation. This paper proposes an automated monitoring system integrating computer vision-based field data with text-based work instructions. The system employs YOLOv5 object detection models to analyze construction site images and architectural drawings, while utilizing text parsing techniques to extract information from work instructions. Validation using thirty apartment units demonstrated effectiveness in monitoring finishing works, particularly masonry and tiling applications. Results showed consistent performance in establishing automated connections between work instructions, drawings, and site conditions, reducing manual verification requirements while maintaining high accuracy. The successful implementation in finishing works demonstrates potential scalability for broader construction applications with varying complexity levels.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106139"},"PeriodicalIF":9.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681491","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

Damage assessment of modular integrated construction during transport and assembly using a hybrid CNN–Gated recurrent unit model 基于混合cnn门控循环单元模型的模块化集成结构在运输和装配过程中的损伤评估

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-22 DOI: 10.1016/j.autcon.2025.106136

Husnain Arshad , Tarek Zayed , Beenish Bakhtawar , Anthony Chen , Heng Li

{"title":"Damage assessment of modular integrated construction during transport and assembly using a hybrid CNN–Gated recurrent unit model","authors":"Husnain Arshad , Tarek Zayed , Beenish Bakhtawar , Anthony Chen , Heng Li","doi":"10.1016/j.autcon.2025.106136","DOIUrl":"10.1016/j.autcon.2025.106136","url":null,"abstract":"<div><div>Modular integrated construction (MiC) offers improved sustainability and automation. Nevertheless, its performance is impeded by extensive logistics operations, including multimode transportation, recurring loading-unloading, stacking, and assembly. Such rigorous operations may cause inadvertent underlying damage to module structure, leading to supply chain disruptions, safety hazards and structural deterioration. A robust real-time damage prediction can mitigate such issues. Thus, this paper develops a hybrid deep learning model for MiC module damage prediction, integrating convolutional and sequential neural networks. The developed hybrid CNN-GRU model establishes correlations between module motion during logistic operations and corresponding structural variations. The multivariate training and testing data of MiC operations is collected using a multi-sensing IoT system. The model is validated for damage scenarios to assess damage level and location, demonstrating a 96 % (R<sup>2</sup>) accuracy. The model provides practical considerations through a robust, automated damage prediction to enhance the safety, productivity and proactive maintenance of MiC modules.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106136"},"PeriodicalIF":9.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681490","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

Common data environment for digital twins from building to city levels 从建筑物到城市级别的数字孪生的通用数据环境

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-21 DOI: 10.1016/j.autcon.2025.106131

Jiayi Yan , Qiuchen Lu , Nan Li , Long Chen , Michael Pitt

{"title":"Common data environment for digital twins from building to city levels","authors":"Jiayi Yan , Qiuchen Lu , Nan Li , Long Chen , Michael Pitt","doi":"10.1016/j.autcon.2025.106131","DOIUrl":"10.1016/j.autcon.2025.106131","url":null,"abstract":"<div><div>Digital twin (DT) technology is pivotal for advancing sustainable, liveable, and resilient smart cities. As DTs scale from building to infrastructure and city levels, data management remains a key challenge due to increasing data heterogeneity. This paper addresses this gap by defining a common data environment (CDE) that connects physical and virtual spaces with three enablers: data sources, data management with functional components (FCs), and data consumers. A systematic literature review (SLR) of 264 papers (from 14,532) analyses these enablers, identifying knowledge gaps and future directions. A prospective DT data ecosystem model is proposed to support city-level DTs (CDT) and federated sub-DTs, integrating informational, technological, functional, organisational, and user-centred features. The paper highlights the immaturity of current data environments in managing heterogeneous data for comprehensive DT applications. It provides state-of-the-art insights and practical recommendations to researchers, practitioners, and policymakers to enhance data management in diverse smart city scenarios.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106131"},"PeriodicalIF":9.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672843","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

Corrigendum to “Estimating track geometry irregularities from in-service train accelerations using deep learning” [Automation in Construction 173 (2025) 106114] “使用深度学习估计在役列车加速度的轨道几何不规则性”[建筑自动化173(2025)106114]的勘误表

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-21 DOI: 10.1016/j.autcon.2025.106138

Zihao Jin, Wei Zhang, Zhen-Yu Yin, Ning Zhang, Xueyu Geng

引用次数: 0