Automation in Construction最新文献_第8页

Drone-based bridge inspections: Current practices and future directions

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-03 DOI: 10.1016/j.autcon.2025.106101

Tommaso Panigati , Mattia Zini , Domenico Striccoli , Pier Francesco Giordano , Daniel Tonelli , Maria Pina Limongelli , Daniele Zonta

{"title":"Drone-based bridge inspections: Current practices and future directions","authors":"Tommaso Panigati , Mattia Zini , Domenico Striccoli , Pier Francesco Giordano , Daniel Tonelli , Maria Pina Limongelli , Daniele Zonta","doi":"10.1016/j.autcon.2025.106101","DOIUrl":"10.1016/j.autcon.2025.106101","url":null,"abstract":"<div><div>As transportation infrastructure networks continue to age, bridges have become critical components requiring monitoring activities to ensure safety and functionality. Inspections and Structural Health Monitoring (SHM) play a vital role in aiding decision-makers in maintaining structural integrity. Drones have gained popularity for bridge inspections because they offer enhanced safety, efficiency, and cost-effectiveness compared to traditional methods. This paper provides a multi-faceted review of existing research on drone-based bridge monitoring, focusing on equipment, inspection procedures, outcomes, the Internet of Drones (IoD), and associated communication technologies, exploring current limitations, future directions and potential advancements. In the near future, it is expected that the application of computer vision techniques to drone-captured imagery will expand the possibilities for automated surface damage detection and extraction of dynamic structural features. The main challenges lie in the integration with IoD, and the standardization of the procedures, paving the way for fully automated drone-assisted inspections.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106101"},"PeriodicalIF":9.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535044","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

Deep learning-based automated method for enhancing excavator activity recognition in far-field construction site surveillance videos

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-01 DOI: 10.1016/j.autcon.2025.106099

Yejin Shin, Seungwon Seo, Choongwan Koo

{"title":"Deep learning-based automated method for enhancing excavator activity recognition in far-field construction site surveillance videos","authors":"Yejin Shin, Seungwon Seo, Choongwan Koo","doi":"10.1016/j.autcon.2025.106099","DOIUrl":"10.1016/j.autcon.2025.106099","url":null,"abstract":"<div><div>Vision-based classifiers, highly sensitive to camera placement, face significant challenges under far-field conditions at construction sites. To address these challenges, this paper proposes a deep learning-based method for enhancing excavator activity recognition using a 3D Residual Neural Network (3D ResNet) classifier with transfer learning. Machine learning-based SHapley Additive exPlanations (SHAP) analysis was employed to evaluate classifier performance across varying camera placements, focusing on distance, height, and angle. Additionally, an image preprocessing method for object enlargement and clarity enhancement was introduced to improve accuracy. Key findings include: (i) optimal weighted F1-score of 0.866 achieved with camera placement at 20 m distance, 6 m height, and 45° angle; (ii) SHAP analysis identifying distance as the most critical factor; (iii) weighted F1-score of 0.818 obtained with real-world far-field video after applying the proposed image preprocessing. The proposed method demonstrates potential for enhancing productivity and carbon emissions management through precise excavator activity monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106099"},"PeriodicalIF":9.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521311","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

Latent normal images-based zero-negative sample rail surface defect segmentation method

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-01 DOI: 10.1016/j.autcon.2025.106097

Bin Yan , Fan Yang , Shi Qiu , Jin Wang , Lei Xu , Weidong Wang , Jun Peng

{"title":"Latent normal images-based zero-negative sample rail surface defect segmentation method","authors":"Bin Yan , Fan Yang , Shi Qiu , Jin Wang , Lei Xu , Weidong Wang , Jun Peng","doi":"10.1016/j.autcon.2025.106097","DOIUrl":"10.1016/j.autcon.2025.106097","url":null,"abstract":"<div><div>Rail surface defects pose a significant risk to the safe operation of railways, making rapid and accurate detection essential. However, existing deep learning-based detection methods struggle to identify all potential defects that may occur during operation due to imbalanced sample, which limits practical application in railway maintenance. To address this, a latent normal images-based zero-negative sample segmentation method for rail surface defects is proposed. This method utilizes an improved Pix2Pix network, which learns the characteristics of normal rails to generate latent normal images. Defect regions are then inferred based on the differences between input detection image and latent normal image. Experimental results demonstrate that the proposed method achieves a mPA of 0.9984 and a mIoU of 0.8305. Under the zero-negative sample condition, it performs comparably to other classical segmentation models, such as DeepLabv3+ and U-Net3+, which rely on a large number of labeled negative samples. Additionally, the proposed method shows better adaptability to new defects.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106097"},"PeriodicalIF":9.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521312","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

Image-based scan-to-BIM for interior building component reconstruction

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-01 DOI: 10.1016/j.autcon.2025.106091

Mun On Wong , Yifeng Sun , Huaquan Ying , Mengtian Yin , Hui Zhou , Ioannis Brilakis , Tom Kelly , Chi Chiu Lam

{"title":"Image-based scan-to-BIM for interior building component reconstruction","authors":"Mun On Wong , Yifeng Sun , Huaquan Ying , Mengtian Yin , Hui Zhou , Ioannis Brilakis , Tom Kelly , Chi Chiu Lam","doi":"10.1016/j.autcon.2025.106091","DOIUrl":"10.1016/j.autcon.2025.106091","url":null,"abstract":"<div><div>Image-based scan-to-BIM is a cost-effective and accessible solution for generating digital models of real-world environments. However, its indoor application remains challenging due to cluttered occlusions, complex geometries, and various surfaces. This paper develops a photogrammetry and instance segmentation-integrated approach for image-based interior building component reconstruction. Specifically, the approach consists of (1) boundary surface modeling by integrating vertical surface representations and concave polygons, (2) semantic mapping of building components between 3D point clouds and 2D images using learning-based instance segmentation and camera projection, and (3) boundary refinement based on hole and color features for optimizing elements' geometries. The approach is validated using six interior scenes, which shows around 60 % reduction in geometric deviations (56 mm) compared to existing approaches, with mean intersection-over-union ratios of 82 %, 78 %, and 72 % for doors, windows, and lift openings. The approach provides centimeter-level accuracy using commonly available devices, striving to broaden the application of image-based scan-to-BIM.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106091"},"PeriodicalIF":9.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527008","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

Embedded visualizations in crane operation user interfaces for real-time assistance

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-28 DOI: 10.1016/j.autcon.2025.106078

Jiantsen Goh , Yihai Fang , Barrett Ens

{"title":"Embedded visualizations in crane operation user interfaces for real-time assistance","authors":"Jiantsen Goh , Yihai Fang , Barrett Ens","doi":"10.1016/j.autcon.2025.106078","DOIUrl":"10.1016/j.autcon.2025.106078","url":null,"abstract":"<div><div>While crane operations are becoming increasingly complex, challenges remain in creating user interfaces (UIs) that effectively support real-time decision-making and situational awareness. This paper presents a comprehensive review and evaluation of visualizations used in crane operations, focusing on user interfaces designed to enhance operator performance and safety during lifts. Through a systematic review of existing literature, this paper synthesizes key trends, design principles, and technologies in crane UIs, with a particular emphasis on the role of embedded visualizations. A tailored UI evaluation model is developed, drawing from principles in human-computer interaction and Augmented Reality (AR) design realms, to assess the efficacy of these systems in real-time operations. This review also identifies key research gaps, including the need for empirical testing of individual visualizations and comprehensive UI configurations to better understand their impact on operator performance. Overall, this paper makes valuable contributions to the field by laying the groundwork for improving both the safety and productivity of crane operations through more effective, user-centered UIs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106078"},"PeriodicalIF":9.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512220","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

Comprehensive floor plan vectorization with sparse point set representation

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-27 DOI: 10.1016/j.autcon.2025.106023

Jici Xing , Longyong Wu , Tianyi Zeng , Yijie Wu , Jianga Shang

引用次数: 0

Real-time defect detection in underground sewage pipelines using an improved YOLOv5 model

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-27 DOI: 10.1016/j.autcon.2025.106068

Jingyi Lu , Wenjie Song , Yuxuan Zhang , Xianfei Yin , Shunyi Zhao

引用次数: 0

Efficient mixed-integer linear programming model for integrated management of ready-mixed concrete production and distribution

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-27 DOI: 10.1016/j.autcon.2025.106074

Aldana S. Tibaldo, Jorge M. Montagna, Yanina Fumero

{"title":"Efficient mixed-integer linear programming model for integrated management of ready-mixed concrete production and distribution","authors":"Aldana S. Tibaldo, Jorge M. Montagna, Yanina Fumero","doi":"10.1016/j.autcon.2025.106074","DOIUrl":"10.1016/j.autcon.2025.106074","url":null,"abstract":"<div><div>The ready-mixed concrete industry plays a key role in the construction sector. Tight integration between different actors is required for developing a successful project. In this context, this paper presents a contribution to optimal daily planning of production and distribution operations in the concrete industry to meet the requirements of construction sites. Customized products must be produced and delivered satisfying the specific time windows proposed by customers. To solve this problem, traditional models often resort to approaches based on decomposition techniques or approximate methods, which may provide poor quality solutions that deviate significantly from optimal planning. In contrast, this article presents an integrated and exact approach to solve the problems of ready-mixed concrete batching, production and distribution, reaching the optimal global solution in good computational times. Several examples are solved to assess the capability and performance of the proposed formulation and its applicability to a real case in this industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106074"},"PeriodicalIF":9.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510113","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

Land surveying with UAV photogrammetry and LiDAR for optimal building planning

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-27 DOI: 10.1016/j.autcon.2025.106092

Paul Sestras , Gheorghe Badea , Ana Cornelia Badea , Tudor Salagean , Sanda Roșca , Shuraik Kader , Fabio Remondino

引用次数: 0

Corrigendum to “Comparing dynamic viewpoint control techniques for teleoperated robotic welding in construction” [Automation in Construction 172 (2025) 106053]

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-26 DOI: 10.1016/j.autcon.2025.106093

Sungboo Yoon , Moonseo Park , Changbum R. Ahn

引用次数: 0