Automation in Construction最新文献_第6页

Corrigendum to “Neural radiance fields for construction site scene representation and progress evaluation with BIM” [Automation in Construction, Volume 172 (2025) 106013]

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-12 DOI: 10.1016/j.autcon.2025.106121

Yuntae Jeon , Dai Quoc Tran , Khoa Tran Dang Vo , Jaehyun Jeon , Minsoo Park , Seunghee Park

引用次数: 0

Enhancing worker monitoring and management on large-scale construction sites with UAVs and digital twin modeling

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-12 DOI: 10.1016/j.autcon.2025.106108

Mingqiao Han, Jihan Zhang, Yijun Huang, Jiwen Xu, Xi Chen, Ben M. Chen

{"title":"Enhancing worker monitoring and management on large-scale construction sites with UAVs and digital twin modeling","authors":"Mingqiao Han, Jihan Zhang, Yijun Huang, Jiwen Xu, Xi Chen, Ben M. Chen","doi":"10.1016/j.autcon.2025.106108","DOIUrl":"10.1016/j.autcon.2025.106108","url":null,"abstract":"<div><div>Monitoring large-scale work sites is challenging, particularly in vast outdoor areas. Unmanned aerial vehicles (UAVs) provide an effective solution for site monitoring and worker management. This paper introduces a UAV-based framework integrated with digital twin (DT) modeling to enhance real-time data management and worker authorization verification. The pretrained YOLO-LCA model improved detection accuracy from 31.5% to 96.4%. The framework combines multi-object tracking with 3D site reconstruction, enabling precise global registration and situational awareness. Cross-referencing UAV detections with GPS-enabled worker IDs ensures that only authorized personnel are present, effectively identifying unapproved workers. The proposed framework has undergone large-scale validation across multiple construction projects in Hong Kong, demonstrating significant potential for modernizing work site management. By integrating UAVs and DT technology, this framework supports efficient monitoring, operational safety, and informed decision-making, providing a scalable approach to addressing the demands of large-scale construction site management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106108"},"PeriodicalIF":9.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601474","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

Dataset and benchmark for as-built BIM reconstruction from real-world point cloud

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-11 DOI: 10.1016/j.autcon.2025.106096

Yudong Liu, Han Huang, Ge Gao, Ziyi Ke, Shengtao Li, Ming Gu

引用次数: 0

Integrated algorithm for identifying failure modes and assessing reliability of concrete-filled steel tubes under lateral impact

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-11 DOI: 10.1016/j.autcon.2025.106118

Nan Xu, Yanhui Liu

{"title":"Integrated algorithm for identifying failure modes and assessing reliability of concrete-filled steel tubes under lateral impact","authors":"Nan Xu, Yanhui Liu","doi":"10.1016/j.autcon.2025.106118","DOIUrl":"10.1016/j.autcon.2025.106118","url":null,"abstract":"<div><div>Concrete-filled steel tube (CFST) columns are susceptible to transverse impact and catastrophic fracture failure might trigger progressive collapse of entire buildings. This paper aims to predict CFST failure modes (bending deformation, crack and fracture) and conduct reliability evaluation implementing intelligent algorithms. Fixed-supported CFST impact samples were gathered, which contain 10 inputs and 3 outputs (crack deflection, fracture deflection and maximum deflection). Results indicated that support vector regression (SVR) predicted three outputs optimally adopting RBF kernel function, and osprey optimization algorithm (OOA) optimizing SVR achieved more superior prediction than particle swarm optimization. Three output variables were utilized to identify CFST failure modes, OOA-SVR(R) possessed 97.70 % accuracy for 87 test samples. Monte Carlo sampling was conducted to estimate fracture vulnerability curves considering random distribution of input variables. Finally, a performance-based CFST design procedure was proposed, declining computational requirements and strengthening user-friendliness in contrast to conventional approaches.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106118"},"PeriodicalIF":9.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591536","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

Intelligent detection of bonding status in external building insulation layers using ground-penetrating radar 利用探地雷达智能检测建筑外保温层的粘接状态

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-11 DOI: 10.1016/j.autcon.2025.106100

Yuhan Li , Xiaopeng Yang , Junbo Gong , Jian Wang , Zihang Jiang , Tian Lan

{"title":"Intelligent detection of bonding status in external building insulation layers using ground-penetrating radar","authors":"Yuhan Li , Xiaopeng Yang , Junbo Gong , Jian Wang , Zihang Jiang , Tian Lan","doi":"10.1016/j.autcon.2025.106100","DOIUrl":"10.1016/j.autcon.2025.106100","url":null,"abstract":"<div><div>The bonding status of external building insulation layer is crucial for thermal insulation and long-term safety, but existing detection methods lack efficiency and accuracy. This paper explores the use of Ground-Penetrating Radar (GPR) technology for accurately estimating bonding areas and precisely identifying top and subgrade debonding defects in external building insulation layers. It proposes an end-to-end intelligent detection method based on GPR, incorporating a multi-task branch network that automatically selects C-scan depth slices for semantic segmentation to estimate bonding areas and utilizes B-scan slices for target detection of debonding defects. Results show that area estimation’s relative error is 0.70%, debonding detection accuracy reaches 78.45%, and the model performs well in complex scenarios. This paper provides the application of GPR in building inspection, promoting hazard discovery and technological advancement. Future work will focus on improving clutter suppression algorithms and C-scan depth slice extraction methods to further enhance detection results.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106100"},"PeriodicalIF":9.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591535","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

Estimating track geometry irregularities from in-service train accelerations using deep learning

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-10 DOI: 10.1016/j.autcon.2025.106114

Zihao Jin , Wei Zhang , Zhenyu Yin , Ning Zhang , Xueyu Geng

{"title":"Estimating track geometry irregularities from in-service train accelerations using deep learning","authors":"Zihao Jin , Wei Zhang , Zhenyu Yin , Ning Zhang , Xueyu Geng","doi":"10.1016/j.autcon.2025.106114","DOIUrl":"10.1016/j.autcon.2025.106114","url":null,"abstract":"<div><div>Timely identification of Track Geometry Irregularities (TGIs) is essential for ensuring the safety and comfort of high-speed rail operations. Existing inspection methods rely on costly Track Recording Vehicles (TRVs) and manual trolleys, resulting in infrequent and expensive inspections. This paper proposes a data-driven approach for estimating TGIs using a Convolutional Neural Network with Multi-Head and Multi-Layer Perceptron (CNN-MH-MLP) architecture. A comprehensive vehicle-track-embankment-ground Finite Element (FE) model incorporating geometric wheel-rail nonlinearity is developed to generate the in-service train acceleration data used for training the network. The CNN-MH-MLP network demonstrates strong performance in estimating TGIs, exhibiting robustness to noise. Optimized sensor placement with three sensors achieves the best trade-off between accuracy and efficiency. Furthermore, the network's transferability highlights the significance of detailed numerical models in producing virtual databases. This work is expected to facilitate the development of intelligent systems for real-time TGI monitoring, improving inspection efficiency and reducing labor costs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106114"},"PeriodicalIF":9.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579210","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

Large language model-empowered paradigm for automated geotechnical site planning and geological characterization

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-09 DOI: 10.1016/j.autcon.2025.106103

Zehang Qian, Chao Shi

{"title":"Large language model-empowered paradigm for automated geotechnical site planning and geological characterization","authors":"Zehang Qian, Chao Shi","doi":"10.1016/j.autcon.2025.106103","DOIUrl":"10.1016/j.autcon.2025.106103","url":null,"abstract":"<div><div>A sound site investigation scheme must satisfy requirements of various local, regional, or national codes, and it is imperative to have an efficient system for information retrieval, summarization, and reasoning along with a rapid interpretation tool for real-time risk-informed decision-making. Emerging large language models (LLMs) offer a promising solution for automatically processing unstructured natural languages and facilitating collaborative reasoning between humans and machines. This paper develops a customized LLM-based agent named “Geologist” to streamline geotechnical site planning and subsequent geological interpretation. A Multihop-Retrieval-Augmented Generation system is proposed to retrieve site-specific requirements from multiple site investigation design codes. Moreover, a progressive human-machine collaboration scheme is orchestrated for interpretable geological modelling. The efficiency of the proposed LLM-guided paradigm is validated through illustrative examples and real-world case histories. Results show that the proposed workflow facilitates real-time and accurate information retrieval as well as automatic development of subsurface geological cross-sections with quantified stratigraphic uncertainty.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106103"},"PeriodicalIF":9.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579209","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 system for micro-defects in 3D printed concrete

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-08 DOI: 10.1016/j.autcon.2025.106105

Hongyu Zhao , Xiangyu Wang , Junbo Sun , Fei Wu , Xianda Liu , Zhaohui Chen , Yufei Wang

{"title":"Automated analysis system for micro-defects in 3D printed concrete","authors":"Hongyu Zhao , Xiangyu Wang , Junbo Sun , Fei Wu , Xianda Liu , Zhaohui Chen , Yufei Wang","doi":"10.1016/j.autcon.2025.106105","DOIUrl":"10.1016/j.autcon.2025.106105","url":null,"abstract":"<div><div>The internal micro-defects of 3D printed concrete (3DPC) play a pivotal role in influencing its mechanical properties. Nonetheless, the acquisition of representative internal micro-defect information is hindered by computational inefficiencies and quantification limitations of the current equipment system. This paper proposes a deep learning based system to assist SEM equipment in automatically quantifying micro-defects of 3DPC for in-depth microstructural analysis that surpasses traditional SEM methods. Through optimal resizing approach and model enhancement tactics, the proposed micro-defect segmentation model leverages advantages of both convolutional neural networks and transformer. This improvement segmentation capability achieves higher accuracy and faster speed than current algorithms, enabling system to achieve accurate quantitative analyses of micro-defects. Using this automated analysis system, the relationship among micro-defect areas in 3DPC, mechanical properties, and printer parameters is investigated. Therefore, the proposed system reduces labour and computational time, demonstrating significant potential for applications in analyzing concrete microstructure.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106105"},"PeriodicalIF":9.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579208","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

BIM-driven software and algorithm for optimal floor tile layout minimizing material waste

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-07 DOI: 10.1016/j.autcon.2025.106115

Faruk Ergen, Önder Halis Bettemir

引用次数: 0

Digitizing contract administration via electroencephalography: Exploring the brain-behavior link in contract clause review

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-07 DOI: 10.1016/j.autcon.2025.106112

Xinyan Wei , Pin-Chao Liao , Heap-Yih Chong

{"title":"Digitizing contract administration via electroencephalography: Exploring the brain-behavior link in contract clause review","authors":"Xinyan Wei , Pin-Chao Liao , Heap-Yih Chong","doi":"10.1016/j.autcon.2025.106112","DOIUrl":"10.1016/j.autcon.2025.106112","url":null,"abstract":"<div><div>Digital transformation in contract administration seeks to improve efficiency and transparency, yet cognitive biases in contract interpretation remain unresolved. This paper investigates how stakeholders' professional expertise shapes their cognitive and behavioral responses to contract terms, particularly for the main contractor's obligations or liabilities. A controlled experiment was designed to compare three groups of samples using electroencephalography (EEG) and behavioral metrics. The legal practice group demonstrated shorter gaze durations, higher accuracy, and minimal biases, whereas while the construction groups showed heightened sensitivity to contractor obligations, with prolonged gaze duration correlating to reduced risk overestimation. These findings emphasize the role of expertise in mitigating cognitive biases and the value of attentional recovery (prolonged focus) for unbiased interpretation, advancing strategies to integrate neuroscience into contract management. Future research should prioritize AI-driven tools leveraging EEG insights and tailored training programs to reduce interpretation biases, extending neurocognitive applications to diverse stakeholder groups in contract administration.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106112"},"PeriodicalIF":9.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562727","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