Automation in Construction最新文献_第7页

Mechanics-based digital twin model for structural construction using 3D scanning and FE model updating

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-06 DOI: 10.1016/j.autcon.2025.106109

Jian-Sheng Fan , Mu-Nan Xu , Yan-Chang Li , Yi-Fan Ding , Yu-Fei Liu , Bin Geng , Xin-Jun Liao

{"title":"Mechanics-based digital twin model for structural construction using 3D scanning and FE model updating","authors":"Jian-Sheng Fan , Mu-Nan Xu , Yan-Chang Li , Yi-Fan Ding , Yu-Fei Liu , Bin Geng , Xin-Jun Liao","doi":"10.1016/j.autcon.2025.106109","DOIUrl":"10.1016/j.autcon.2025.106109","url":null,"abstract":"<div><div>Construction control of large-span spatial steel structures faces significant challenges, particularly during the transition from partition assembly to structural closure. Factors such as component cutting errors, splicing inaccuracies, and temperature-induced deformations further complicate this process. Traditional deformation control relies on forward calculations based on design models and cannot establish a closed loop connecting analytical models with as-built structures. To address these limitations, a mechanics-based digital twin model is proposed using 3D scanning and finite element (FE) model updating. Real-to-virtual model establishment is achieved through 3D point cloud data and self-weight compensation; virtual-to-real construction prediction is performed by considering temperature effects and boundary condition transformations. The proposed method is applied to a high-speed railway station in Fujian Province, China. Accurate predictions of the structural deformations and cutting lengths of embedded members in construction are obtained, with an average relative error of 0.25 %.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106109"},"PeriodicalIF":9.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550445","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

Remotely operated vehicle (ROV) underwater vision-based micro-crack inspection for concrete dams using a customizable CNN framework

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-06 DOI: 10.1016/j.autcon.2025.106102

Hao Liu , Jingyue Yuan , Qiubing Ren , Mingchao Li , Zhiyong Qi , Xufang Deng

{"title":"Remotely operated vehicle (ROV) underwater vision-based micro-crack inspection for concrete dams using a customizable CNN framework","authors":"Hao Liu , Jingyue Yuan , Qiubing Ren , Mingchao Li , Zhiyong Qi , Xufang Deng","doi":"10.1016/j.autcon.2025.106102","DOIUrl":"10.1016/j.autcon.2025.106102","url":null,"abstract":"<div><div>Timely and accurate underwater structural inspection is crucial for ensuring the service ability of concrete dams. However, due to the harsh and complex environments, most in-air crack detection methods are not suitable. This paper presents an end-to-end underwater micro-crack detection framework based on customizable convolutional neural networks. First, customized model, UENet, is constructed based on multi-level feature fusion and dual-branch network for automated image enhancement. Then, lightweight patch-level classification model, LDNet, is developed and class activation mapping is embedded to provide weakly-supervised localization. Finally, two customizable networks are integrated into an end-to-end architecture to obtain inspection results directly by inputting images. Moreover, remotely operated vehicle is employed to collect underwater videos and create dataset to address the lack of underwater dam micro-crack images. Extensive experiments demonstrate that the framework is efficient, accurate, and has strong generalization, with an accuracy of 98.63 %, which provides an advanced computer-aided tool for underwater inspections.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106102"},"PeriodicalIF":9.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550443","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

Taxonomic framework for neural network-based anomaly detection in bridge monitoring

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-06 DOI: 10.1016/j.autcon.2025.106113

Imane Bayane, John Leander, Raid Karoumi

{"title":"Taxonomic framework for neural network-based anomaly detection in bridge monitoring","authors":"Imane Bayane, John Leander, Raid Karoumi","doi":"10.1016/j.autcon.2025.106113","DOIUrl":"10.1016/j.autcon.2025.106113","url":null,"abstract":"<div><div>Accurate differentiation between damage-related anomalies and data errors is a critical challenge in bridge monitoring. This paper presents a data-driven framework for anomaly detection and classification, addressing the question: How can anomalies be classified in multi-sensor bridge monitoring to distinguish structural changes from noise? The framework combines an adapted anomaly taxonomy with a deep neural network trained on synthetic data. It is validated using long-term monitoring data from a railway bridge, incorporating strain gauges, accelerometers, and an inclinometer. In offline training, the model achieves high precision, recall, and F1-scores, effectively detecting anomaly classes across sensor types. For online prediction, it provides anomaly type percentages and visualizations over daily, weekly, and annual timeframes, distinguishing frequent noise-related anomalies from rare anomalies signaling structural changes. Requiring one month of training data, the framework delivers a scalable solution for bridge monitoring and lays the groundwork for future self-learning anomaly detection in infrastructure management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106113"},"PeriodicalIF":9.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550444","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

Optimized crane mat design and transit path planning using a graph search algorithm

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-06 DOI: 10.1016/j.autcon.2025.106107

Monjurul Hasan , Ming Lu

引用次数: 0

Data-driven multi-objective prediction and optimization of construction productivity and energy consumption in cutter suction dredging

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-06 DOI: 10.1016/j.autcon.2025.106104

Yong Chen , Qiubing Ren , Mingchao Li , Huijing Tian , Liang Qin , Dianchun Wu

{"title":"Data-driven multi-objective prediction and optimization of construction productivity and energy consumption in cutter suction dredging","authors":"Yong Chen , Qiubing Ren , Mingchao Li , Huijing Tian , Liang Qin , Dianchun Wu","doi":"10.1016/j.autcon.2025.106104","DOIUrl":"10.1016/j.autcon.2025.106104","url":null,"abstract":"<div><div>In dredging construction, cutter suction dredger (CSD) operation typically relies on manual experience with suboptimal control parameters, which can easily lead to low productivity and high energy consumption. This paper presents an intelligent decision-making approach for optimizing CSD control parameters based on multi-objective optimization (MOO). It employs high-dimensional feature selection techniques to identify key parameters affecting CSD performance, and develops a multi-output regressor chain-extreme gradient boosting (RC-XGBoost) model for concurrent prediction and an improved multi-objective gray wolf optimization algorithm to derive the decision-making solutions. Tian Jing Hao CSD operational data from the Pinglu Canal project in China is taken for case-study. Results show that RC-XGBoost model can effectively predict productivity and energy consumption with R<sup>2</sup> values of 0.961 and 0.989, respectively. The MOO framework demonstrates remarkable adaptability to diverse scenarios. It enables the adaptive acquisition of optimal control parameter combinations under various geological conditions, thus enhancing the operational reliability. Overall, productivity increases by 3.08 %, while energy consumption decreases by 2.40 %. This paper offers an approach for operators to optimize CSD productivity and energy consumption.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106104"},"PeriodicalIF":9.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550442","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-based semantic enrichment and knowledge graph generation via geometric relation checking

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-05 DOI: 10.1016/j.autcon.2025.106081

Georgios Nektarios Lilis, Meng Wang, Kyriakos Katsigarakis, Dimitrios Mavrokapnidis, Ivan Korolija, Rovas Dimitrios

引用次数: 0

Inner wall defect detection in oil and gas pipelines using point cloud data segmentation

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-04 DOI: 10.1016/j.autcon.2025.106098

Zhouyu Yan, Hong Zhao

引用次数: 0

Construction site hazard recognition via mobile immersive virtual reality and eye tracking

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-04 DOI: 10.1016/j.autcon.2025.106080

Bekir Enes Özel , Mehmet Koray Pekeriçli

{"title":"Construction site hazard recognition via mobile immersive virtual reality and eye tracking","authors":"Bekir Enes Özel , Mehmet Koray Pekeriçli","doi":"10.1016/j.autcon.2025.106080","DOIUrl":"10.1016/j.autcon.2025.106080","url":null,"abstract":"<div><div>The perilousness of construction sites is well-known. The need for competent safety management is apparent as well. Although hazard recognition is crucial for safety management, studies show an alarmingly high portion of hazards are unrecognized in construction sites. This situation indicates that better methods are required to understand the process of hazard recognition more profoundly and to measure the hazard recognition skills of construction professionals. This paper deploys mobile immersive virtual reality, eye tracking, and real-time development platforms for hazard recognition research. The presented system, namely SafeGaze, offers a mobile, flexible, easy-to-use, and high-fidelity solution that is capable of gathering varied and precise data about the hazard recognition skills of construction professionals. The aimed contributions are providing an explicit framework for gathering and analyzing hazard recognition data, introducing mobile immersive virtual reality to the domain, and providing graphical optimization methods for the utilization of standalone head mounted displays in construction sites.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"173 ","pages":"Article 106080"},"PeriodicalIF":9.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535045","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

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