Automation in Construction最新文献_第9页

BIM-focused incentive-driven adoption of information management technology in bridge construction

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-17 DOI: 10.1016/j.autcon.2025.106052

Xue Yan , Xinran Liao , Shuping Cheng , Ting Wang

{"title":"BIM-focused incentive-driven adoption of information management technology in bridge construction","authors":"Xue Yan , Xinran Liao , Shuping Cheng , Ting Wang","doi":"10.1016/j.autcon.2025.106052","DOIUrl":"10.1016/j.autcon.2025.106052","url":null,"abstract":"<div><div>In bridge construction, the integration of information management technologies like BIM, blockchain, big data, and precast components shows promise but lack initial adoption motivation. Centering on BIM, this paper constructs an incentive model for collaboration between owners and construction units, thereby exploring the technology diffusion mechanisms. Utilizing a simulation on the Shenzhen-Zhongshan Bridge (SZB) in China, the paper identifies effective incentive ranges and scenarios. Results indicate that combining penalties and early completion benefits is more effective than simple subsidy in reducing costs and enhancing incentives. Further analysis reveals that incentives can encourage BIM adoption but may cause a “Matthew Effect”. These findings facilitate owners in designing compound incentive programs and assist construction units in determining the optimal technology application level to seize market opportunities. Future research should explore a broader spectrum of incentive types and combinations for a more comprehensive understanding of effective incentive strategies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106052"},"PeriodicalIF":9.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422265","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

Transformer-based deformation measurement of underground structures from a single-camera video

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-17 DOI: 10.1016/j.autcon.2025.106070

Hao-Ruo Xu , Jia-Ning Yin , Ning Zhang

{"title":"Transformer-based deformation measurement of underground structures from a single-camera video","authors":"Hao-Ruo Xu , Jia-Ning Yin , Ning Zhang","doi":"10.1016/j.autcon.2025.106070","DOIUrl":"10.1016/j.autcon.2025.106070","url":null,"abstract":"<div><div>Measuring the deformation of underground structures, such as excavation and tunnels, is crucial for safe construction and operation. However, conventional methods, such as inclinometer probes and total stations, are labour-intensive and time-consuming for engineers. This paper proposes a transformer-based 3D reconstruction method using single-camera video to rapidly measure structural deformations. The proposed method extracts features from video frames to reconstruct point clouds, generating centrelines and Poisson models for deformation analysis. This allows fast and precise deformation measurement at any position, outperforming traditional methods. The proposed method has achieved sub-millimetre accuracy in small-scale inclinometer casings, and a 5 cm accuracy level in a large-scale tunnel, confirming its capability for detecting subtle deformations. Discrepancies in accuracy for larger structures were attributed to limitations in camera resolution, suggesting that employing 100-megapixel cameras could guarantee millimetre-level accuracy. The method's simplicity and adaptability demonstrate its potential as a practical supplement to existing deformation measurement methods.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106070"},"PeriodicalIF":9.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430333","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

Comparing dynamic viewpoint control techniques for teleoperated robotic welding in construction

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-16 DOI: 10.1016/j.autcon.2025.106053

Sungboo Yoon , Moonseo Park , Changbum R. Ahn

{"title":"Comparing dynamic viewpoint control techniques for teleoperated robotic welding in construction","authors":"Sungboo Yoon , Moonseo Park , Changbum R. Ahn","doi":"10.1016/j.autcon.2025.106053","DOIUrl":"10.1016/j.autcon.2025.106053","url":null,"abstract":"<div><div>Dynamic viewpoints offer effective visual feedback in teleoperation for construction, where tasks often require precise manipulation during frequent viewpoint adjustments. However, the comparative performance of various dynamic viewpoint control techniques remains unclear. This paper investigates the impact of dynamic viewpoint control techniques on task performance and user experience during teleoperation in construction. A user study was conducted in a remote welding-at-height scenario with 20 participants, including experienced welders and university students, to compare five techniques: (1) coupled vision-motion, (2) decoupled vision-motion with hand or head motion-based control, and (3) hybrid vision-motion with manual or automatic switching. Results showed that decoupled vision-motion with head motion-based control outperformed other techniques in task efficiency and user preference. Hybrid vision-motion with manual switching was more effective than decoupled vision-motion in contexts involving occlusions, reducing physical demand and enhancing welding quality. Based on these findings, guidelines are proposed for viewpoint control in teleoperated construction robots.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106053"},"PeriodicalIF":9.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422263","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

Adaptive sliding mode and safety control for excavators using Kinematic Control Barrier Function and sliding mode disturbance observer

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-16 DOI: 10.1016/j.autcon.2025.106046

Weidi Huang , Qi Wang , Shuwei Yang, Junhui Zhang, Bing Xu

{"title":"Adaptive sliding mode and safety control for excavators using Kinematic Control Barrier Function and sliding mode disturbance observer","authors":"Weidi Huang , Qi Wang , Shuwei Yang, Junhui Zhang, Bing Xu","doi":"10.1016/j.autcon.2025.106046","DOIUrl":"10.1016/j.autcon.2025.106046","url":null,"abstract":"<div><div>Excavators have complex structures, large load, and often works in scenarios with safety hazards. Existing methods overlook control-level safety and over-prioritize accuracy, neglecting input smoothness. To address these challenges, a Barrier Functions Adaptive Sliding Mode (BFASM) safety control method based on Kinematic Control Barrier Function (KCBF) and Sliding Mode Disturbance Observer (SMDO) is proposed. Specifically, a virtual motion trajectory tracking controller is established and CBF provides safe joint velocity inputs for system. A Barrier Function (BF)-based anti-saturation adaptive sliding mode controller is proposed. SMDO is used to estimate the lumped disturbance. BF is used to design the bounded adaptive control gain to ensure that sliding variables remain in predefined neighborhoods of zero, and its size does not depend on disturbance boundary. Simulation results demonstrate that the proposed safety controller effectively ensures safety and the tracking controller keeps errors within a predefined range of 1° with no chattering of control inputs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106046"},"PeriodicalIF":9.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422264","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

Multi-class segmentation of structural damage and pathological manifestations using YOLOv8 and Segment Anything Model

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-13 DOI: 10.1016/j.autcon.2025.106037

Paulo Alberto Sampaio Santos, Michele Tereza Marques Carvalho

{"title":"Multi-class segmentation of structural damage and pathological manifestations using YOLOv8 and Segment Anything Model","authors":"Paulo Alberto Sampaio Santos, Michele Tereza Marques Carvalho","doi":"10.1016/j.autcon.2025.106037","DOIUrl":"10.1016/j.autcon.2025.106037","url":null,"abstract":"<div><div>Advances in computer vision have significantly improved bridge inspection by enabling precise damage detection and failure prediction. However, these techniques require costly datasets and specialized expertise. To overcome this, an approach combining YOLO object detection and SAM segmentation effectively identifies cracks, scaling, rust stains, exposed reinforcement, and efflorescence. Six models were fine-tuned, including the YOLOv8 architecture, three variations with modified detection layers for small, medium, and large damage, an optimized TensorRT version, and the new Yolov9-GELAN architecture. The YOLOv8l model achieved precision, recall, <span><math><mrow><mi>m</mi><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></math></span>, and <span><math><mrow><mi>m</mi><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn><mo>−</mo><mn>95</mn></mrow></msub></mrow></math></span> of 0.946, 0.916, 0.951, and 0.892, respectively. The model’s outputs enhanced SAM-based instance segmentation, reducing uncertainties. A publicly available COCO-format dataset with 41,132 annotated images supports further research. This paper advances bridge inspection and construction by providing a robust model for multi-class object detection and instance segmentation of structural damages, with architectures tailored to detect small, medium, and large damages for more precise inspections.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106037"},"PeriodicalIF":9.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403539","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

Data-driven additive manufacturing with concrete: Enhancing in-line sensory data with domain knowledge, Part I: Geometry

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-13 DOI: 10.1016/j.autcon.2025.106020

J. Versteege, R.J.M. Wolfs, T.A.M. Salet

{"title":"Data-driven additive manufacturing with concrete: Enhancing in-line sensory data with domain knowledge, Part I: Geometry","authors":"J. Versteege, R.J.M. Wolfs, T.A.M. Salet","doi":"10.1016/j.autcon.2025.106020","DOIUrl":"10.1016/j.autcon.2025.106020","url":null,"abstract":"<div><div>First-time-right manufacturing is an important step toward unlocking the full potential of digital fabrication with concrete (DFC), which can be advanced through data-driven approaches. Non-invasive in-line sensors can collect vast amounts of measurements during the manufacturing process. However, knowledge-driven feature engineering (KDFE) strategies are necessary to extract meaningful information, referred to as features, from the raw sensory data. This contribution, part of a two-part study, presents an approach to integrating KDFE with various in-line sensors in a 3D concrete printing (3DCP) facility, focusing on 2D laser scanning techniques to capture the ‘as-printed’ layer geometry during production. The geometric profiles are translated into features that quantify layer dimensions, cross-sectional area, and surface texture, reducing data complexity while enhancing relevancy. Real-world data is utilized to demonstrate the approach. A companion paper extends the methodology to other sensors, including those monitoring moisture and temperature, further advancing process monitoring in 3DCP.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106020"},"PeriodicalIF":9.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395367","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

Efficient and scalable architecture for location-based mobile applications using metrica

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-13 DOI: 10.1016/j.autcon.2025.106056

Anna Przewięźlikowska , Wioletta Ślusarczyk , Klaudia Wójcik , Marek Ślusarski

引用次数: 0

Egocentric camera-based method for detecting static hazardous objects on construction sites

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-13 DOI: 10.1016/j.autcon.2025.106048

Ziming Liu , Jiuyi Xu , Christine Wun Ki Suen , Meida Chen , Zhengbo Zou , Yangming Shi

{"title":"Egocentric camera-based method for detecting static hazardous objects on construction sites","authors":"Ziming Liu , Jiuyi Xu , Christine Wun Ki Suen , Meida Chen , Zhengbo Zou , Yangming Shi","doi":"10.1016/j.autcon.2025.106048","DOIUrl":"10.1016/j.autcon.2025.106048","url":null,"abstract":"<div><div>The construction site is a hazardous workplace, accounting for more than 20 % of worker fatalities compared to other industries in the United States. Predominant causes of these fatalities are slips, trips, and falls (STFs). Therefore, identifying hazardous objects on construction sites that could lead to STFs is crucial for enhancing construction safety. Previous studies using fixed-position cameras often miss observations of obstructed or hidden objects. This paper proposes an alternative approach using safety helmets with lightweight wide-angle cameras and leveraging open-vocabulary object detection (OVOD) methods to identify hazardous objects on construction sites that could lead to STFs. In addition, an egocentric view dataset specifically for construction sites was created and released for benchmarking purposes. Research results indicated a 79.0 % weighted F1-score in classifying static hazardous objects on construction sites. This proposed system has the potential to enhance construction safety and provide a valuable dataset for future construction safety research.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106048"},"PeriodicalIF":9.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395364","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

Heterogeneous graph attention network for rail fastener looseness detection using distributed acoustic sensing and accelerometer data fusion

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-13 DOI: 10.1016/j.autcon.2025.106051

Yiqing Dong, Yaowen Yang, Chengjia Han, Chaoyang Zhao, Aayush Madan, Lipi Mohanty, Yuguang Fu

{"title":"Heterogeneous graph attention network for rail fastener looseness detection using distributed acoustic sensing and accelerometer data fusion","authors":"Yiqing Dong, Yaowen Yang, Chengjia Han, Chaoyang Zhao, Aayush Madan, Lipi Mohanty, Yuguang Fu","doi":"10.1016/j.autcon.2025.106051","DOIUrl":"10.1016/j.autcon.2025.106051","url":null,"abstract":"<div><div>Ensuring rail fasteners' integrity is crucial for railway safety. Traditional methods for detecting loosened fasteners are laborious and economically inefficient. This paper introduces FusionHGAT, an attention-enhanced heterogeneous Graph Neural Network (GNN), designed for precise, automated detection of rail fastener looseness by fusing data from Distributed Acoustic Sensing (DAS) and accelerometers. The method collects sensor data during rail track excitations, constructs a graph based on spatial relationships, and implements FusionHGAT through a three-step procedure: feature extraction with 1D-Convolution Neural Networks, feature embedding via a Transformer module, and feature fusion using Graph Attention Network layers. Experimental results demonstrate FusionHGAT's outstanding performance, achieving 100 % accuracy and validating the model's superiority. Building on the results presented in this work, our graph-based methodology enhances the detection of fastener looseness through spatial-temporal data fusion, highlighting its potential for future real-time railway infrastructure monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106051"},"PeriodicalIF":9.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395365","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

Edge-fog-cloud-based digital twin network for autonomous and distributed structural health monitoring of a mega dam cluster

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-13 DOI: 10.1016/j.autcon.2025.106050

Ying Li , Qingzhao Kong , Bing Xiong , Fudong Chi , Yongqian Qu , Cui Wang

{"title":"Edge-fog-cloud-based digital twin network for autonomous and distributed structural health monitoring of a mega dam cluster","authors":"Ying Li , Qingzhao Kong , Bing Xiong , Fudong Chi , Yongqian Qu , Cui Wang","doi":"10.1016/j.autcon.2025.106050","DOIUrl":"10.1016/j.autcon.2025.106050","url":null,"abstract":"<div><div>Structural health monitoring (SHM) of mega engineering is huge, complex, and time-consuming. To address these challenges, this paper proposes an edge-fog-cloud-based digital twin network and provides its application on a mega dam cluster consisting of three dams along a river. Primary features of the network include an intelligent seismograph signal identification algorithm with Convolutional Neural Network (CNN) in the edge computing layer, a streaming finite element analysis (FEA) method for cumulatively simulating effects of water pressure and continuous seismic ground motion in the fog computing layer, and a real-time 3D virtual model visualization approach on Web driven by FEA response in the cloud computing layer. All processes are automated. Performance analysis indicates that the seismograph signal identification algorithm achieves an impressive accuracy of 95 %, virtual model spatial mapping deviation is only 5 %, and SHM processing speed is 9 times faster than the previous manual work. This digital twin network provides high-efficiency, autonomous and distributed SHM for the mega dam cluster, effectively minimizing labor costs, economic expenses and energy consumption.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106050"},"PeriodicalIF":9.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395366","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