Automation in Construction最新文献_第2页

Automated layout generation of HVAC piping from building floor plans 从建筑平面图自动生成HVAC管道布局

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-27 DOI: 10.1016/j.autcon.2025.106359

Junjie Li , Hang Guan , Peng Xu , Jiefan Gu , Liyan Dong

引用次数: 0

Multi-view stereo 3D building reconstruction with sparse depth and edge location priors 基于稀疏深度和边缘定位先验的多视点立体三维建筑重建

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-27 DOI: 10.1016/j.autcon.2025.106365

Xuan Yang , Rongrong Hou , Yuequan Bao

引用次数: 0

Electroencephalography (EEG) for psychological hazards and mental health in construction safety automation: Algorithmic Systematic Review (ASR) 建筑安全自动化中心理危害与心理健康的脑电图：算法系统评价（ASR）

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-27 DOI: 10.1016/j.autcon.2025.106346

Haytham Elmousalami , Felix Kin Peng Hui , Lu Aye

{"title":"Electroencephalography (EEG) for psychological hazards and mental health in construction safety automation: Algorithmic Systematic Review (ASR)","authors":"Haytham Elmousalami , Felix Kin Peng Hui , Lu Aye","doi":"10.1016/j.autcon.2025.106346","DOIUrl":"10.1016/j.autcon.2025.106346","url":null,"abstract":"<div><div>This algorithmic systematic review investigates the applications of electroencephalography (EEG) for recognizing psychological hazards and monitoring mental health in construction safety. As automation and wearable technologies gain traction, EEG systems provide real-time insights into workers' cognitive and emotional states, helping to identify stress, fatigue, and safety risks. Utilizing a structured search algorithm, literature from Scopus and Web of Science was filtered and analysed to create a comprehensive framework for EEG deployment in five key domains: automated psychological and cognitive assessment, hazard recognition and safety decision-making, advanced technology integration, situational awareness enhancement, and sustainability contributions. The review underscores the synergy of EEG with robotics, virtual reality, and wearable devices, enhancing safety management in construction. Challenges such as data privacy and scalability are thoroughly examined. This paper significantly advances the understanding of EEG's role in construction automation, offering future research directions to optimize EEG-based systems for a safer, more sustainable construction industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106346"},"PeriodicalIF":9.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491826","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

Lightweight instance segmentation for rapid leakage detection in shield tunnel linings under extreme low-light conditions 在极弱光照条件下盾构隧道衬砌泄漏快速检测的轻量级实例分割

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-26 DOI: 10.1016/j.autcon.2025.106368

Yuehao Chen , Binchao Xu , Ying Jiang , Zhao-Dong Xu , Xingwei Wang , Tengfei Liu , Wancheng Zhu

{"title":"Lightweight instance segmentation for rapid leakage detection in shield tunnel linings under extreme low-light conditions","authors":"Yuehao Chen , Binchao Xu , Ying Jiang , Zhao-Dong Xu , Xingwei Wang , Tengfei Liu , Wancheng Zhu","doi":"10.1016/j.autcon.2025.106368","DOIUrl":"10.1016/j.autcon.2025.106368","url":null,"abstract":"<div><div>Rapid and accurate water leakage detection and segmentation is essential for ensuring the structural safety of subway tunnels. This paper simulates the extreme low-light conditions inside the tunnel from multiple perspectives. By employing inverse operations, pseudo-RAW format data are generated, providing more original features and avoiding the complex computations associated with traditional image enhancement and denoising algorithms. A lightweight instance segmentation network is optimised and designed, incorporating a multi-stage star-shaped backbone to improve feature extraction in dark environments, and serial-parallel structured detection-segmentation heads are used to accelerate segmentation speed. Experiment results demonstrate that the optimised model, using pseudo-RAW data, achieves a segmentation precision of 84.4 % in leakage instance segmentation under low-light conditions, with a model size of only 2.7 M. The proposed method closely aligns with real-world engineering environments, providing a low-cost and efficient solution for leakage monitoring in subway shield tunnels.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106368"},"PeriodicalIF":9.6,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491823","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

Semantic BIM enrichment using a hybrid ML and rule-based framework for automated tenement compliance checking 使用混合ML和基于规则的框架进行自动房屋合规性检查的语义BIM丰富

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-26 DOI: 10.1016/j.autcon.2025.106369

Ankan Karmakar, Venkata Santosh Kumar Delhi

{"title":"Semantic BIM enrichment using a hybrid ML and rule-based framework for automated tenement compliance checking","authors":"Ankan Karmakar, Venkata Santosh Kumar Delhi","doi":"10.1016/j.autcon.2025.106369","DOIUrl":"10.1016/j.autcon.2025.106369","url":null,"abstract":"<div><div>Semantic enrichment enhances BIM models by extracting structured information, improving their applicability for Automated Code Compliance Checking. Rule-based methods rely on well-defined conditions but struggle with tasks like space classification, where explicit checking rules are unavailable. Meanwhile, ML-based classification introduces adaptability but faces liability challenges due to misclassifications. This paper proposes a hybrid framework integrating ML for space classification and rule-based inferencing for tenement identification. The approach ensures that ML automates preprocessing, improving classification through meticulous feature engineering, while rule-based reasoning guarantees logical consistency during verification. Validated using real-world datasets from residential projects in Mumbai, India as a case, the ML-based space classification component achieves an F1-score of 0.85 and accuracy of 0.86, demonstrating its effectiveness. The deterministic tenement identification process delivers error-free results for various dwelling configurations, making it highly suitable for verification workflows. This study advances scalable BIM-based compliance systems by refining semantic enrichment methodologies for future applications.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106369"},"PeriodicalIF":9.6,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491824","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 analysis of construction safety accident videos using a large multimodal model and graph retrieval-augmented generation 使用大型多模态模型和图形检索增强生成的建筑安全事故视频自动分析

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-26 DOI: 10.1016/j.autcon.2025.106363

Miyoung Uhm , Jaehee Kim , Ghang Lee

引用次数: 0

Automated extraction of geometric information from LiDAR point clouds on curved ramps 弯曲坡道激光雷达点云几何信息的自动提取

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-24 DOI: 10.1016/j.autcon.2025.106358

Yuchen Wang , Yuhang Liu , Pieter Pauwels , Zheng Li , Bin Yu

{"title":"Automated extraction of geometric information from LiDAR point clouds on curved ramps","authors":"Yuchen Wang , Yuhang Liu , Pieter Pauwels , Zheng Li , Bin Yu","doi":"10.1016/j.autcon.2025.106358","DOIUrl":"10.1016/j.autcon.2025.106358","url":null,"abstract":"<div><div>Several approaches have been implemented to extract road geometric information from point clouds originating from different LiDAR systems. However, they are unsuitable for scenarios lacking trajectory data and involving road widening and complex alignment combinations, particularly in the case of curved ramps. This article proposes an automated framework to process discrete LiDAR point clouds and extract geometric information for these ramps. The framework primarily contributes in three key areas: 1) A node identification method is proposed to accurately segment the horizontal and vertical alignments, especially for fluctuating curvature and varying longitudinal grade; 2) By determining road axis points using road markings and boundaries, the framework supports road widening and all types of ramp cross sections; 3) Cross sections are extracted without slicing and rotating, allowing width calculation within each section. Test results show that the framework achieves geometric extraction accuracies between 90.79 % and 100 %, demonstrating its effectiveness for curved ramps.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106358"},"PeriodicalIF":9.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366439","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

Rapid integration strategy for oblique photogrammetry terrain and highway BIM models in large-scale scenarios 大尺度场景下倾斜摄影测量地形与公路BIM模型的快速集成策略

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-23 DOI: 10.1016/j.autcon.2025.106354

Jiaheng Li , Feng Han , Long Shi , Zelong Liu , Chengxiang Wang , Yikun Fan

{"title":"Rapid integration strategy for oblique photogrammetry terrain and highway BIM models in large-scale scenarios","authors":"Jiaheng Li , Feng Han , Long Shi , Zelong Liu , Chengxiang Wang , Yikun Fan","doi":"10.1016/j.autcon.2025.106354","DOIUrl":"10.1016/j.autcon.2025.106354","url":null,"abstract":"<div><div>The integration of Oblique Photogrammetric Digital Models (OPDMs) with Highway Building Information Modeling (HwyBIM) models to construct Three-Dimensional (3D) scenes is vital in the digital twin technology used for linear infrastructure, especially in geological hazard simulation and monitoring. However, owing to large data volumes and complex geometric features, no efficient method currently exists for rapid OPDM integration processing. This paper proposes a fast extraction method for Donflicting Tile Models (CTMs) based on the Scan Line Filling (SLF) algorithm to reduce redundant data loading during integration. An improved Surface-Volume Boolean Operation Algorithm (F-V BOA) is developed to process individual Tile Models (TMs), along with a 3D Model Boundary (3D-MB)-driven automatic TM integration plugin. The proposed approach is validated through a real-world highway case study and compared with conventional approaches, demonstrating its effectiveness and efficiency. The proposed approach can significantly contributes to OPDM integration in large-scale linear infrastructure projects. Future work will incorporate CPU/GPU parallel computing for further optimization.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106354"},"PeriodicalIF":9.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364652","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 excavation performance and situational awareness in construction teleoperation using electro-tactile feedback 利用电触觉反馈提高施工遥操作的挖掘性能和态势感知

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-23 DOI: 10.1016/j.autcon.2025.106366

Jin Sol Lee , Youngjib Ham , Hangue Park , Semyoung Oh

{"title":"Enhancing excavation performance and situational awareness in construction teleoperation using electro-tactile feedback","authors":"Jin Sol Lee , Youngjib Ham , Hangue Park , Semyoung Oh","doi":"10.1016/j.autcon.2025.106366","DOIUrl":"10.1016/j.autcon.2025.106366","url":null,"abstract":"<div><div>Construction teleoperators, who perform hazardous tasks remotely, often experience limited situational awareness and cognitive overload due to overreliance on visual displays. These challenges are exacerbated in high-pressure situations, increasing the risk of fatal accidents and degrading task performance, especially for novice operators. In this paper, an electro-tactile feedback interface was introduced to address these issues, and its impact on task performance (e.g., collision, productivity) and cognitive performance (e.g., workload, situational awareness) are investigated across different levels of operational difficulty. A virtual environment simulating real-world excavation constraints was used to assess its effectiveness and usability with sixty-two participants. The results indicate that electro-tactile feedback not only reduces workload and enhances risk perception of remote hazards, but also improves control performance in excavation tasks, particularly under cognitively demanding conditions. These findings support the integration of electro-tactile feedback into construction teleoperation interfaces, contributing to safer and more efficient remote operations in challenging environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106366"},"PeriodicalIF":9.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364649","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

Multi-source data-driven intelligent analysis and decision optimization for high-density pedestrian flows in urban public spaces 城市公共空间高密度人流的多源数据驱动智能分析与决策优化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-23 DOI: 10.1016/j.autcon.2025.106367

Shiqi Zeng , Xiangsheng Chen , Dong Su , Haofeng Gong

{"title":"Multi-source data-driven intelligent analysis and decision optimization for high-density pedestrian flows in urban public spaces","authors":"Shiqi Zeng , Xiangsheng Chen , Dong Su , Haofeng Gong","doi":"10.1016/j.autcon.2025.106367","DOIUrl":"10.1016/j.autcon.2025.106367","url":null,"abstract":"<div><div>Managing high-density pedestrian flows in urban public spaces via Information Technologies (IT) is crucial for safety and efficiency. Despite advancements in sensing, AI-driven prediction, and control, a critical gap persists: lacking the systematic integration needed for robust automated crowd management systems, an issue intensified by AI/IoT growth. To address this challenge, a comprehensive review of the literature from 2014 to 2024 has been conducted, analyzing and synthesizing IT-driven decision support approaches for automated crowd management. The field is organized around three core technological pillars: (1) multi-source data fusion architectures for comprehensive real-time monitoring; (2) intelligent prediction systems using deep learning for accurate forecasting and anomaly detection; and (3) advanced decision optimization platforms enabling dynamic, multi-objective control strategies. In addition, the review explores key emerging trends such as edge computing, digital twins, and human-machine collaboration. The findings offer theoretical insights, practical guidelines, an overview of persistent challenges, and strategic directions for future research in intelligent crowd management within the broader context of smart cities and resilient infrastructure.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106367"},"PeriodicalIF":9.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364653","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