Automation in Construction最新文献_第7页

Dual hierarchical attention-enhanced transfer learning for semantic segmentation of point clouds in building scene understanding 用于建筑场景理解中点云语义分割的双分层注意力增强迁移学习

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-07 DOI: 10.1016/j.autcon.2024.105799

Limao Zhang , Zeyang Wei , Zhonghua Xiao , Ankang Ji , Beibei Wu

{"title":"Dual hierarchical attention-enhanced transfer learning for semantic segmentation of point clouds in building scene understanding","authors":"Limao Zhang , Zeyang Wei , Zhonghua Xiao , Ankang Ji , Beibei Wu","doi":"10.1016/j.autcon.2024.105799","DOIUrl":"10.1016/j.autcon.2024.105799","url":null,"abstract":"<div><div>Targeted to the challenge of indoor scene understanding for intelligent devices, this paper question focuses on enhancing accuracy in semantic information extraction. A framework including a dual hierarchical attention network, transfer learning, interpretability analysis, and modeling module is applied to segment and reconstruct the indoor scene. A high-rise as-built building case is used to verify the method, the results show that: (1) the method achieves a high mIoU of 0.970 in point cloud segmentation and outperforms state-of-the-art methods, both demonstrating strong performance; (2) the method has sound feature extraction and learning ability in term of the interpretive analysis; (3) the method accelerates by 37 % than manual operations, achieving higher accuracy and efficiency. Overall, the method provides an effective solution to segment multi-class objects for indoor scene understanding and can serve as a basis for automated modeling to contribute to an accurate BIM model with great potential for practical application.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105799"},"PeriodicalIF":9.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418465","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

Corrigendum to “Automated geometric reconstruction and cable force inference for cable-net structures using 3D point clouds” [Automation in Construction, 165 (2024), 105543] 利用三维点云对索网结构进行自动几何重建和索力推断"[《建筑自动化》，165 (2024)，105543] 更正

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-07 DOI: 10.1016/j.autcon.2024.105821

Siwei Lin , Liping Duan , Jiming Liu , Xiao Xiao , Ji Miao , Jincheng Zhao

引用次数: 0

Non-invasive vision-based personal comfort model using thermographic images and deep learning 利用热成像图像和深度学习建立基于视觉的无创个人舒适度模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-05 DOI: 10.1016/j.autcon.2024.105811

Vincent Gbouna Zakka , Minhyun Lee , Ruixiaoxiao Zhang , Lijie Huang , Seunghoon Jung , Taehoon Hong

{"title":"Non-invasive vision-based personal comfort model using thermographic images and deep learning","authors":"Vincent Gbouna Zakka , Minhyun Lee , Ruixiaoxiao Zhang , Lijie Huang , Seunghoon Jung , Taehoon Hong","doi":"10.1016/j.autcon.2024.105811","DOIUrl":"10.1016/j.autcon.2024.105811","url":null,"abstract":"<div><div>An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive vision-based personal comfort model that integrates thermographic images and deep learning. Unlike previous studies, the entire thermographic image of the upper body is directly used during model training, minimizing complex data processing and maximizing the use of rich skin temperature distribution. The proposed method is validated using thermographic images and corresponding thermal sensation votes (TSV) from 10 participants under different experimental conditions. Results show that the model based on a 3-point TSV scale achieves exceptional classification performance with an average accuracy of 99.51 %, outperforming existing models. The model performance using a 7-point TSV scale is slightly lower, with an average accuracy of 89.90 %. This method offers potential for integrating thermal comfort models into real-time building environmental control, optimizing occupant comfort and energy consumption.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105811"},"PeriodicalIF":9.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418464","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

Optimizing bucket-filling strategies for wheel loaders inside a dream environment 优化轮式装载机在梦境环境中的铲斗装载策略

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105804

Daniel Eriksson , Reza Ghabcheloo , Marcus Geimer

引用次数: 0

Spatio-temporal heat risk analysis in construction: Digital twin-enabled monitoring 建筑时空热风险分析：数字孪生监测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105805

Yoojun Kim , Youngjib Ham

引用次数: 0

Data-driven logistics collaboration for prefabricated supply chain with multiple factories 多工厂预制供应链的数据驱动型物流协作

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105802

Yishu Yang , Ying Yu , Chenglin Yu , Ray Y. Zhong

引用次数: 0

Deep learning network for indoor point cloud semantic segmentation with transferability 用于室内点云语义分割的可移植性深度学习网络

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105806

Luping Li , Jian Chen , Xing Su , Haoying Han , Chao Fan

{"title":"Deep learning network for indoor point cloud semantic segmentation with transferability","authors":"Luping Li , Jian Chen , Xing Su , Haoying Han , Chao Fan","doi":"10.1016/j.autcon.2024.105806","DOIUrl":"10.1016/j.autcon.2024.105806","url":null,"abstract":"<div><div>Semantic segmentation is crucial for interpreting point cloud data and plays a fundamental role in automating the creation of as-built BIM. Existing neural network models for semantic segmentation often heavily rely on the training dataset, resulting in a significant performance drop when applied to new datasets. This paper presents AttTransNet, a neural network model for automated point cloud semantic segmentation. Its attention-based pooling module improves local feature extraction from point clouds while reducing computational costs. The transfer learning framework enhances segmentation accuracy with minimal training on target datasets. Comparative experiments show that AttTransNet reduces training time by 80 % and improves segmentation accuracy by over 20 % compared with other SOTA methods. Cross-dataset experiments reveal that the transfer learning framework increases accuracy on new datasets by 150 %. By adding semantic information to point clouds, AttTransNet aids BIM modelers with direct reference, encouraging broader application of automated point cloud segmentation in the industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105806"},"PeriodicalIF":9.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418463","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

Energy-efficient configuration and scheduling framework for electric construction machinery collaboration systems 电动工程机械协作系统的节能配置和调度框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-03 DOI: 10.1016/j.autcon.2024.105808

Xiaohui Huang , Wanbin Yan , Guibao Tao , Sujiao Chen , Huajun Cao

{"title":"Energy-efficient configuration and scheduling framework for electric construction machinery collaboration systems","authors":"Xiaohui Huang , Wanbin Yan , Guibao Tao , Sujiao Chen , Huajun Cao","doi":"10.1016/j.autcon.2024.105808","DOIUrl":"10.1016/j.autcon.2024.105808","url":null,"abstract":"<div><div>The electrification of construction machinery has created a perceptible future trend of the development of electric construction machinery collaboration systems (ECMCSs). However, there is a lack of research on energy-efficient operation of ECMCS. This paper proposes a theoretical configuration and scheduling framework promoting the applications of ECMCSs. In the configuration stage, this paper considers the effect of charging time and proposes an electric matching factor to achieve an optimal system configuration. In the scheduling stage, a multi-objective scheduling problem is formulated for achieving energy-efficient system operation, which considers the transport volume, cost and idle time. A validation of the framework was carried out using a case study that found the optimal system solution, while the advantages of the considered ECMCS compared to a fossil fuel-powered system were discussed. The impact of battery and charging technology developments was also assessed. This framework can be widely applied to deployment of ECMCSs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105808"},"PeriodicalIF":9.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418459","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

Detecting district heating leaks in thermal imagery: Comparison of anomaly detection methods 从热图像中检测区域供热泄漏：异常检测方法的比较

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-02 DOI: 10.1016/j.autcon.2024.105709

Elena Vollmer, Julian Ruck, Rebekka Volk, Frank Schultmann

{"title":"Detecting district heating leaks in thermal imagery: Comparison of anomaly detection methods","authors":"Elena Vollmer, Julian Ruck, Rebekka Volk, Frank Schultmann","doi":"10.1016/j.autcon.2024.105709","DOIUrl":"10.1016/j.autcon.2024.105709","url":null,"abstract":"<div><div>District heating systems offer means to transport heat to end-energy users through underground pipelines. When leakages occur, a lack of reliable monitoring makes pinpointing their locations a difficult and costly task for network operators. In recent years, aerial thermography has emerged as a means to find leakages as hot-spots, with several papers proposing image analysis algorithms for their detection. While all publications boast high performance metrics, the methods are constructed around very different datasets, making a true comparison impossible.</div><div>Using a new set of aerial thermal images from two German cities, this paper implements, improves, and evaluates three anomaly detection methods for leakage detection: triangle-histogram-thresholding, saliency mapping, and local thresholding with filter kernels. The approaches are integrated into a software pipeline with globally applicable pre- and postprocessing, including vignetting correction. While all methods reliably detect thermal anomalies and are suitable for automated leakage detection, triangle-histogram-thresholding is the most robust.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105709"},"PeriodicalIF":9.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418375","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

Strategic alignment of BIM and big data through systematic analysis and model development 通过系统分析和模型开发实现 BIM 和大数据的战略协调

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-02 DOI: 10.1016/j.autcon.2024.105801

Apeesada Sompolgrunk , Saeed Banihashemi , Hamed Golzad , Khuong Le Nguyen

{"title":"Strategic alignment of BIM and big data through systematic analysis and model development","authors":"Apeesada Sompolgrunk , Saeed Banihashemi , Hamed Golzad , Khuong Le Nguyen","doi":"10.1016/j.autcon.2024.105801","DOIUrl":"10.1016/j.autcon.2024.105801","url":null,"abstract":"<div><div>Organisations increasingly rely on data-driven strategies, utilising analytics to achieve competitive advantages. This paper systematically investigates the integration of big data into Building Information Modeling (BIM) within the Architecture, Engineering, and Construction (AEC) sectors, named “big BIM data.” Employing mixed methods of systematic and bibliometric analysis, it synthesises findings from 125 records published 2013–23. While many studies are at preliminary stages with conceptual or small-scale experimental approaches, the paper categorises its results into four domains: AEC organisational infrastructure, big BIM data (IT) infrastructure, AEC organisational strategic domain, and big BIM data (IT) strategic domain, aligned with the Strategic Alignment Model (SAM), exploring organisational competencies, governance factors, and strategic frameworks. This paper introduces the AEC Organisational - Big BIM Data SAM as the research agenda to implement big BIM data utilisation across AEC industry. This framework thoroughly addresses organisational dynamics while emphasising interconnectedness among individual projects, organisational tiers, and industry-wide standards.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105801"},"PeriodicalIF":9.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418443","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