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Automated reality capture for indoor inspection using BIM and a multi-sensor quadruped robot
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-19 DOI: 10.1016/j.autcon.2024.105930
Zhengyi Chen, Changhao Song, Boyu Wang, Xingyu Tao, Xiao Zhang, Fangzhou Lin, Jack C.P. Cheng
{"title":"Automated reality capture for indoor inspection using BIM and a multi-sensor quadruped robot","authors":"Zhengyi Chen, Changhao Song, Boyu Wang, Xingyu Tao, Xiao Zhang, Fangzhou Lin, Jack C.P. Cheng","doi":"10.1016/j.autcon.2024.105930","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105930","url":null,"abstract":"This paper presents a real-time, cost-effective navigation and localization framework tailored for quadruped robot-based indoor inspections. A 4D Building Information Model is utilized to generate a navigation map, supporting robotic pose initialization and path planning. The framework integrates a cost-effective, multi-sensor SLAM system that combines inertial-corrected 2D laser scans with fused laser and visual-inertial SLAM. Additionally, a deep-learning-based object recognition model is trained for multi-dimensional reality capture, enhancing comprehensive indoor element inspection. Validated on a quadruped robot equipped with an RGB-D camera, IMU, and 2D LiDAR in an academic setting, the framework achieved collision-free navigation, reduced localization drift by 71.77 % compared to traditional SLAM methods, and provided accurate large-scale point cloud reconstruction with 0.119-m precision. Furthermore, the object detection model attained mean average precision scores of 73.7 % for 2D detection and 62.9 % for 3D detection.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"14 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867664","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
Delamination detection in concrete decks using numerical simulation and UAV-based infrared thermography with deep learning
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-19 DOI: 10.1016/j.autcon.2024.105940
Dyala Aljagoub, Ri Na, Chongsheng Cheng
{"title":"Delamination detection in concrete decks using numerical simulation and UAV-based infrared thermography with deep learning","authors":"Dyala Aljagoub, Ri Na, Chongsheng Cheng","doi":"10.1016/j.autcon.2024.105940","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105940","url":null,"abstract":"The potential of concrete bridge delamination detection using infrared thermography (IRT) has grown with technological advancements. However, most current studies require an external input (subjective threshold), reducing the detection's objectivity and accuracy. Deep learning enables automation and streamlines data processing, potentially enhancing accuracy. Yet, data scarcity poses a challenge to deep learning applications, hindering their performance. This paper aims to develop a deep learning approach using supervised learning object detection models with extended data from real and simulated images. The numerical simulation image supplementation seeks to eliminate the limited data barrier by creating a comprehensive dataset, potentially improving model performance and robustness. Mask R-CNN and YOLOv5 were tested across various training data and model parameter combinations to develop an optimal detection model. Lastly, when tested, the model showed a remarkable ability to detect delamination of varying properties accurately compared to currently employed IRT techniques.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"31 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867662","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
Egocentric-video-based construction quality supervision (EgoConQS): Application of automatic key activity queries
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-18 DOI: 10.1016/j.autcon.2024.105933
Jingjing Guo, Lu Deng, Pengkun Liu, Tao Sun
{"title":"Egocentric-video-based construction quality supervision (EgoConQS): Application of automatic key activity queries","authors":"Jingjing Guo, Lu Deng, Pengkun Liu, Tao Sun","doi":"10.1016/j.autcon.2024.105933","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105933","url":null,"abstract":"Construction quality supervision is essential for project success and safety. Traditional methods relying on manual inspections and paper records are time-consuming, error-prone, and difficult to verify. In-process construction quality supervision offers a more direct and effective approach. Recent advancements in computer vision and egocentric video analysis present opportunities to enhance these processes. This paper introduces the use of key activity queries on egocentric video data for construction quality supervision. A framework, Egocentric Video-Based Construction Quality Supervision (EgoConQS), is developed using a video self-stitching graph network to identify key activities in egocentric videos. EgoConQS facilitates efficient monitoring and quick review of key activity frames. Empirical evaluation with real-world data demonstrates an average recall of 35.85 % and a mAP score of 6.07 %, highlighting the potential of key activity queries for reliable and convenient quality supervision.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"88 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867668","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
Experimental study on in-situ mesh fabrication for reinforcing 3D-printed concrete
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-18 DOI: 10.1016/j.autcon.2024.105923
Xiangpeng Cao, Shuoli Wu, Hongzhi Cui
{"title":"Experimental study on in-situ mesh fabrication for reinforcing 3D-printed concrete","authors":"Xiangpeng Cao, Shuoli Wu, Hongzhi Cui","doi":"10.1016/j.autcon.2024.105923","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105923","url":null,"abstract":"The lack of reinforcements persisted as a significant issue in 3D-printed concrete, particularly concerning the continuous vertical reinforcement along the direction of mortar stacking. This paper introduced an in-situ mesh fabrication technique that involved injecting high-flowability material to connect reinforcement segments, resulting in a reinforcing mesh within the stacked mortar. Parallel and interwoven reinforcing steel fibers were inserted and epoxy-coated in-situ within the cast and 3D-printed beams for flexural experiments and interfacial characterizations. The in-situ fabricated mesh exhibited more significant enhancement than the parallel independent reinforcements, both in the horizontal and vertical directions, achieving a maximum flexural enhancement of 123.6 % by an epoxy-coated steel fiber mesh. The high-flowability epoxy healed the gaps inside the concrete caused by the mesh fabrication. This paper provides experimental validation for the feasibility of reinforcement integration in all directions within the final 3D-printed concrete structure, thereby supporting the practical application of 3D printing technology.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"274 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867672","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
Physics-guided deep learning for generative design of large-diameter tunnels under existing metro lines
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-17 DOI: 10.1016/j.autcon.2024.105901
Limao Zhang, Jiaqi Wang, Zhuang Xia, Xieqing Song
{"title":"Physics-guided deep learning for generative design of large-diameter tunnels under existing metro lines","authors":"Limao Zhang, Jiaqi Wang, Zhuang Xia, Xieqing Song","doi":"10.1016/j.autcon.2024.105901","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105901","url":null,"abstract":"The overlapping construction of large-diameter tunnels is inevitable, but the construction control faces great challenges due to the complexity of underground environments. A generative design method for large-diameter tunnels under existing metro lines based on physic-guided deep learning is proposed, aiming at optimizing tunnel layouts from a physical perspective to ensure effective construction control. A topology-optimized model dataset considering soil uncertainties is fed into a physics-guided Wasserstein generative adversarial network (PGWGAN) for training, producing numerous physically consistent schemes. The optimal scheme is selected using the multiple-attribute decision-making (MADM) method under multi-working conditions. A case study on large-diameter tunnel construction demonstrates the method's feasibility, showing that it meets the safety requirements across various conditions and achieves significant improvement. This paper contributes a physics-guided generative design method for large-diameter tunnel overlapping construction. It accounts for multiple working conditions and includes an evaluation module that integrates parametric finite element analysis (FEA) with multi-attribute evaluation.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"83 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867673","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
Sensor adoption in the construction industry: Barriers, opportunities, and strategies
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-16 DOI: 10.1016/j.autcon.2024.105937
Zhong Wang, Vicente A. González, Qipei Mei, Gaang Lee
{"title":"Sensor adoption in the construction industry: Barriers, opportunities, and strategies","authors":"Zhong Wang, Vicente A. González, Qipei Mei, Gaang Lee","doi":"10.1016/j.autcon.2024.105937","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105937","url":null,"abstract":"This paper examines the underutilization of sensors in the construction industry despite their significant potential for improving performance. A systematic review was conducted on research published between 2004 and 2024, identifying 11 key barriers such as the need for advanced skill sets and user-centric design, lack of standardized practices, and challenges in data networks and management. The study applied both quantitative descriptive analysis and qualitative content analysis to explore these barriers across five stages of sensor adoption. A total of 63 articles were thoroughly reviewed to identify thematic patterns and chronological trends. The findings highlight critical areas that require attention, including the development of standardized protocols, enhancing data-driven decision-making with advanced analytics, and fostering industry-wide training programs. Additionally, leveraging Lean Construction 4.0 principles is proposed to address these challenges. The insights from this research aim to support the construction industry in integrating sensor technologies more effectively, leading to greater efficiency and improved performance.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"113 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867674","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
Real-time and high-accuracy defect monitoring for 3D concrete printing using transformer networks
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-16 DOI: 10.1016/j.autcon.2024.105925
Hongyu Zhao, Junbo Sun, Xiangyu Wang, Yufei Wang, Yang Su, Jun Wang, Li Wang
{"title":"Real-time and high-accuracy defect monitoring for 3D concrete printing using transformer networks","authors":"Hongyu Zhao, Junbo Sun, Xiangyu Wang, Yufei Wang, Yang Su, Jun Wang, Li Wang","doi":"10.1016/j.autcon.2024.105925","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105925","url":null,"abstract":"Defects and anomalies during the 3D concrete printing (3DCP) process significantly affect final construction quality. This paper proposes a real-time, high-accuracy method for monitoring defects in the printing process using a transformer-based detector. Despite limited data availability, deep learning-based data augmentation and image processing techniques were employed to enable effective training of this complex transformer model. A range of enhancement strategies was applied to the RT-DETR, resulting in remarkable improvements, including a mAP50 of 98.1 %, mAP50–95 of 68.0 %, and a computation speed of 72 FPS. The enhanced RT-DETR outperformed state-of-the-art detectors such as YOLOv8 and YOLOv7 in detecting defects in 3DCP. Furthermore, the improved RT-DETR was used to analyze the relationships between defect count, size, and printer parameters, providing guidance for operators to fine-tune printer settings and promptly address defects. This monitoring method reduces material waste and minimizes the risk of structural collapse during the printing process.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"14 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867683","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
Automation in tower cranes over the past two decades (2003–2024)
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-14 DOI: 10.1016/j.autcon.2024.105889
Muhammad Muddassir, Tarek Zayed, Ali Hassan Ali, Mohamed Elrifaee, Sulemana Fatoama Abdulai, Tong Yang, Amr Eldemiry
{"title":"Automation in tower cranes over the past two decades (2003–2024)","authors":"Muhammad Muddassir, Tarek Zayed, Ali Hassan Ali, Mohamed Elrifaee, Sulemana Fatoama Abdulai, Tong Yang, Amr Eldemiry","doi":"10.1016/j.autcon.2024.105889","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105889","url":null,"abstract":"Tower cranes play a vital role in modern construction for transporting material, yet the persisting issue of crane-related accidents, often attributable to human error, underscores the urgent need for automated crane operations to enhance safety on construction sites. Despite active research in this area, a gap exists in systematically examining and categorising advancements in tower crane automation and identifying key trends and limitations. This paper aims to address this gap by employing a mixed-methods approach, encompassing scientometric and systematic analyses. The scientometric analysis sheds light on key researchers, institutions, journals, and global research networks. Also, the systematic analysis delves into four primary research areas: crane operations, motion control, layout planning, and transport path optimisation. This paper identifies critical knowledge gaps and limitations in tower crane automation, suggests future research directions, and offers industry insights into current methodologies and global trends.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"12 Suppl 1 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867680","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
Scan vs. BIM: Automated geometry detection and BIM updating of steel framing through laser scanning
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-13 DOI: 10.1016/j.autcon.2024.105931
Siwei Lin, Liping Duan, Bin Jiang, Jiming Liu, Haoyu Guo, Jincheng Zhao
{"title":"Scan vs. BIM: Automated geometry detection and BIM updating of steel framing through laser scanning","authors":"Siwei Lin, Liping Duan, Bin Jiang, Jiming Liu, Haoyu Guo, Jincheng Zhao","doi":"10.1016/j.autcon.2024.105931","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105931","url":null,"abstract":"3D laser scanning can serve the geometric deformation detection of steel structures. However, the process of handling large-scale point clouds remains labor-intensive and time-consuming. This paper presents an automated approach to extracting the precise axes from point clouds and updating the associated BIM model for steel structures. The strategy involves the initial geometry extraction from IFC files and instance segmentation through the reference point cloud simplification and index rules. Then the axes of all components with different sections are detected through the corresponding standard sections and genetic algorithm. Lastly, the geometric information for each component in the BIM is updated by modifying the IFC file. The method is implemented on a steel framing comprising 218 components, indicating that the workflow works effectively with noise and occlusion. The difference in average distances from 218 components to the scanned point cloud is reduced from 17.50 mm before updating to 4.00 mm after updating.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"38 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816503","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
Disturbance observer-based passivity and impedance control for trajectory tracking in autonomous hydraulic excavators
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-13 DOI: 10.1016/j.autcon.2024.105898
Junjie Gong, Jian Chen, Dengsheng Cai, Wei Wei, Yu Long
{"title":"Disturbance observer-based passivity and impedance control for trajectory tracking in autonomous hydraulic excavators","authors":"Junjie Gong, Jian Chen, Dengsheng Cai, Wei Wei, Yu Long","doi":"10.1016/j.autcon.2024.105898","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105898","url":null,"abstract":"Trajectory tracking control is pivotal for achieving autonomous operation in hydraulic excavators. This paper proposes a robust control scheme, merging passivity-based and impedance control, enhancing robustness and stability. First, the excavator’s coupled nonlinear dynamics are transformed into an open-loop port Hamiltonian model with disturbances. Through an energy shaping method, this model becomes an ideal closed-loop port Hamiltonian system, stabilized asymptotically by damping injection. An improved robust disturbance observer estimates system disturbances, guiding control compensation term design. Hydraulic cylinder forces and displacements are calculated from the closed-loop port Hamiltonian system’s matching equations. By integrating passivity and impedance control, a flow controller resolves electrohydraulic servo system nonlinearity. Comparative analysis with existing methodologies demonstrates the proposed robust controller’s superior tracking accuracy, even in the presence of shock disturbances.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"52 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867684","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
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