Automation in Construction最新文献

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Comprehensive lifecycle safety risk assessment for construction robotics using T-S fault tree analysis and Bayesian network

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-12 DOI: 10.1016/j.autcon.2025.106041

Liying Wang , Yuecheng Huang , Yao Wang , Botao Gu , Boning Li , Dongping Fang

The application of construction robots introduces unprecedented safety challenges, underscoring a research gap in safety risk assessment throughout the application processes. This paper focused on the lifecycle safety risks associated with the entry, debugging, operation, maintenance, and exit phases of construction robots, identifying 13 risk categories and 52 risk factors. Moreover, Takagi and Sugeno fault tree analysis (TS-FTA) and Bayesian network were integrated to establish risk assessment models based on accident type analysis, indicating environmental failures and unsafe management behaviors as critical in electrical accidents, while human and physical failures are predominant in mechanical injuries. The results underscore unique risk manifestations and management priorities, emphasizing the importance of addressing emerging risks and prioritizing resources for critical risks such as insufficient on-site safety risk control, inadequate emergency management, and cluttered environment. This paper offers a comprehensive framework for risk assessment and management in construction robot applications, contributing to safer project execution.

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引用次数: 0

Adaptive climbing and automatic inspection robot for variable curvature walls of industrial storage tank facilities

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-12 DOI: 10.1016/j.autcon.2025.106049

Jie Li , Xiang Zhou , Chao Gui , Mingxing Yang , Fengyu Xu , Xingsong Wang

Regular inspections are essential to ensure the safe operation of petrochemical storage tank facilities, but traditional manual operations in high-altitude construction environments are inefficient and hazardous. To achieve automated inspection and maintenance of the tank walls, this paper proposes an adaptive climbing inspection robot for variable curvature walls. The inspection robot with magnetic adsorption wheels, curvature-adaptive mechanisms, and inclination adjustment structure was meticulously designed, featuring multifunctional inspection and maintenance capabilities. Robot climbing dynamics and posture adaptability on curved surfaces were analyzed and evaluated. The experimental results demonstrate the robot's ability to adaptively operate on curved tank walls and perform multifunctional tasks, including time-of-flight diffraction (TOFD) ultrasonic flaw detection, polishing, painting, and cleaning. The developed robot can significantly enhance the efficiency of automated inspection operation on curved tank facility walls. Future research should focus on enhancing the robot's adaptability to irregular welded surfaces and improving its defect recognition performance.

{"title":"Adaptive climbing and automatic inspection robot for variable curvature walls of industrial storage tank facilities","authors":"Jie Li , Xiang Zhou , Chao Gui , Mingxing Yang , Fengyu Xu , Xingsong Wang","doi":"10.1016/j.autcon.2025.106049","DOIUrl":"10.1016/j.autcon.2025.106049","url":null,"abstract":"<div><div>Regular inspections are essential to ensure the safe operation of petrochemical storage tank facilities, but traditional manual operations in high-altitude construction environments are inefficient and hazardous. To achieve automated inspection and maintenance of the tank walls, this paper proposes an adaptive climbing inspection robot for variable curvature walls. The inspection robot with magnetic adsorption wheels, curvature-adaptive mechanisms, and inclination adjustment structure was meticulously designed, featuring multifunctional inspection and maintenance capabilities. Robot climbing dynamics and posture adaptability on curved surfaces were analyzed and evaluated. The experimental results demonstrate the robot's ability to adaptively operate on curved tank walls and perform multifunctional tasks, including time-of-flight diffraction (TOFD) ultrasonic flaw detection, polishing, painting, and cleaning. The developed robot can significantly enhance the efficiency of automated inspection operation on curved tank facility walls. Future research should focus on enhancing the robot's adaptability to irregular welded surfaces and improving its defect recognition performance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106049"},"PeriodicalIF":9.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395363","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

Machine intelligence for interpretation and preservation of built heritage

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-12 DOI: 10.1016/j.autcon.2025.106055

Xiaoyi Zu , Chen Gao , Yongkang Liu , Zhixing Zhao , Rui Hou , Yi Wang

Documenting and characterizing built heritage through digital format are topical issues in the architecture and heritage preservation field. Although digitalized built heritage (DBH) features are complex, they have been intelligently interpreted and perceived by researchers supported by machine learning (ML) models. This paper reviews the mainstream ML models applied in the tasks of quantitative interpreting of formal features and parsing of multi-spatial-element synergy mechanisms, and summarizes their applications in the major issues of DBH characterization research, to show their operation paradigms and demonstrate what gaps still exist. Based on the review, the ML models have been capable of quantitatively extracting the formal features of DBH and parsing the synergy weights of multi-spatial-elements. However, future research still requires advances in 1) Automatically summarizing the DBH formal features; 2) Taking point clouds as an ideal DBH carrier; 3) Forming a computer-autonomous decision-making path for built heritage preservation.

引用次数: 0

Intelligent virtual trial assembly of prefabricated frame structures for large and complex construction scenes

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-11 DOI: 10.1016/j.autcon.2025.106047

Ming Guo , Shuai Guo , Qinglong Meng , Minghua Liu , Junjie Wang , Chao Cui , Xiaolan Zhang

Virtual Trial Assembly (VTA) is increasingly employed in engineering projects to reduce costs and improve assembly efficiency. However, the complexity of construction sites and the high precision requirements for assembly make manual point cloud segmentation both time-consuming and ineffective in accurately extracting assembly features. In order to solve the problem, this paper proposes a VTA method based on semantic segmentation and 3D bounding box estimation. It enables efficient semantic segmentation of large-scale point clouds and calculates precise assembly feature points based on the estimated 3D bounding boxes, facilitating optimal assembly analysis of all components. The results show that this method enables automated VTA analysis of assembly components and guides physical assembly. This research contributes to improving the monitoring of the entire construction process, ensuring efficient and precise assembly of building components.

引用次数: 0

Biomechanical assessment of a passive back exoskeleton using vision-based motion capture and virtual modeling

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-10 DOI: 10.1016/j.autcon.2025.106035

Yuan Zhou, JoonOh Seo, Yue Gong, Kelvin HoLam Heung, Masood Khan, Ting Lei

This paper proposes a video-driven biomechanical analysis method for measuring muscular loads influenced by wearing an exoskeleton suit, combining vision-based motion capture and virtual modeling approaches. Motion data obtained from site videos is integrated with a newly developed human-exoskeleton model in biomechanical software, to simulate muscular loads on the human body and evaluate exoskeleton suits. This method has been validated through experimental tests, where simulated and directly measured muscle activations were compared for four types of lifting tasks. The results indicate that this method successfully estimates neuromuscular activations of the low back muscles with and without wearing an exoskeleton suit, though the effect of the exoskeleton suit tends to be overestimated in simulations. Despite this limitation, the proposed method is expected to assist in efficiently evaluating exoskeleton use in practice, thereby facilitating the more widespread adoption of passive exoskeletons in construction.

引用次数: 0

Knowledge-based cross-modal fusion for long-term forecasting of grouting construction parameters using large language model

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-10 DOI: 10.1016/j.autcon.2025.106036

Tianhong Zhang , Hongling Yu , Xiaoling Wang , Jiajun Wang , Binyu Ren

Accurate long-term forecasting of grouting construction parameters is essential for foundation safety and the advancement of grouting automation. Existing methods have limited generalization due to diverse equipment and complex geological conditions. This paper addressed these challenges by proposing the Knowledge-based cross-modal fusion for long-term forecasting of Grouting parameters using Large Language Model (KG-LLM). This method captured the variations and relationships among grouting parameters by integrating domain-specific knowledge through construction knowledge and cross-prompt. A cross-modal fusion method combined knowledge-driven prompts with multi-scale time embedding into the frozen LLM, ensuring high prediction accuracy and generalization. Case studies on three projects validated the predictive performance and cross-engineering generalization of KG-LLM, with notable improvements in the prediction of parameters. KG-LLM quickly adapted to other projects without further training and was not constrained by equipment type. Moreover, this method was compatible with any LLM, offering a scalable solution for advancing the intelligent of grouting construction.

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引用次数: 0

Neural radiance fields for construction site scene representation and progress evaluation with BIM

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-08 DOI: 10.1016/j.autcon.2025.106013

Yuntae Jeon , Dai Quoc Tran , Khoa Tran Dang Vo , Jaehyun Jeon , Minsoo Park , Seunghee Park

Efficient progress monitoring is crucial for construction project management to ensure adherence to project timelines and cost control. Traditional methods, which rely on either 3D point cloud data or 2D image transformations, face challenges such as data sparsity in point cloud and the need for extensive human labeling. Recent NeRF-based methods offer high-quality image rendering for accurate evaluation, but challenges remain in comparing as-built scenes with as-planned designs or measuring actual dimensions. To address these limitations, this paper proposes a NeRF-based scene understanding approach synchronized with BIM. Additionally, a formalized progress evaluation method and the automatic generation of ground truth masks for comparison using BIM on NVIDIA Omniverse are introduced. This approach enables precise progress evaluation using smartphone-captured video, enhancing its applicability and generalizability. Experiments conducted on three different scenes from the concrete pouring process demonstrate that our method achieves a measurement error range of 1% to 2.2% and 8.7 mAE for element-wise segmentation performance in completed scenes. Furthermore, it achieves 5.7 mAE for progress tracking performance in ongoing process scenes. Overall, these findings are significant for improving vision-based progress monitoring and efficiency on construction sites.

{"title":"Neural radiance fields for construction site scene representation and progress evaluation with BIM","authors":"Yuntae Jeon , Dai Quoc Tran , Khoa Tran Dang Vo , Jaehyun Jeon , Minsoo Park , Seunghee Park","doi":"10.1016/j.autcon.2025.106013","DOIUrl":"10.1016/j.autcon.2025.106013","url":null,"abstract":"<div><div>Efficient progress monitoring is crucial for construction project management to ensure adherence to project timelines and cost control. Traditional methods, which rely on either 3D point cloud data or 2D image transformations, face challenges such as data sparsity in point cloud and the need for extensive human labeling. Recent NeRF-based methods offer high-quality image rendering for accurate evaluation, but challenges remain in comparing as-built scenes with as-planned designs or measuring actual dimensions. To address these limitations, this paper proposes a NeRF-based scene understanding approach synchronized with BIM. Additionally, a formalized progress evaluation method and the automatic generation of ground truth masks for comparison using BIM on NVIDIA Omniverse are introduced. This approach enables precise progress evaluation using smartphone-captured video, enhancing its applicability and generalizability. Experiments conducted on three different scenes from the concrete pouring process demonstrate that our method achieves a measurement error range of 1% to 2.2% and 8.7 mAE for element-wise segmentation performance in completed scenes. Furthermore, it achieves 5.7 mAE for progress tracking performance in ongoing process scenes. Overall, these findings are significant for improving vision-based progress monitoring and efficiency on construction sites.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106013"},"PeriodicalIF":9.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350316","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

Spectral Jump Anomaly Detection: Temperature-compensated algorithm for structural damage detection using vibration data

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-08 DOI: 10.1016/j.autcon.2025.106031

Giulio Mariniello, Tommaso Pastore, Domenico Asprone

Assessing the integrity of structural systems throughout their aging process has capital importance in infrastructure management. Monitoring these infrastructures presents challenges in distinguishing early damage from slight variations in the structural behavior caused by environmental or operational variability.

This paper introduces the Spectral Jump Anomaly Detection (SJ-AD) algorithm, a data-driven method designed to identify minor structural damage using acceleration collected under considerable environmental variability. SJ-AD focuses on anomalies in the distribution of a distance measure, the minimum jump cost, calculated between power spectra. The method effectively identifies issues in the KW-51 bridge, even with minimal structural defects and varying temperatures. Additionally, numerical experiments show that SJ-AD can detect low damping variations in noisy conditions, demonstrating robustness against minor frequency changes. Its flexible approach and sensitivity to small damages make SJ-AD a promising solution for proactive maintenance and risk management in various structural systems.

{"title":"Spectral Jump Anomaly Detection: Temperature-compensated algorithm for structural damage detection using vibration data","authors":"Giulio Mariniello, Tommaso Pastore, Domenico Asprone","doi":"10.1016/j.autcon.2025.106031","DOIUrl":"10.1016/j.autcon.2025.106031","url":null,"abstract":"<div><div>Assessing the integrity of structural systems throughout their aging process has capital importance in infrastructure management. Monitoring these infrastructures presents challenges in distinguishing early damage from slight variations in the structural behavior caused by environmental or operational variability.</div><div>This paper introduces the Spectral Jump Anomaly Detection (<span>SJ-AD</span>) algorithm, a data-driven method designed to identify minor structural damage using acceleration collected under considerable environmental variability. <span>SJ-AD</span> focuses on anomalies in the distribution of a distance measure, the minimum jump cost, calculated between power spectra. The method effectively identifies issues in the KW-51 bridge, even with minimal structural defects and varying temperatures. Additionally, numerical experiments show that <span>SJ-AD</span> can detect low damping variations in noisy conditions, demonstrating robustness against minor frequency changes. Its flexible approach and sensitivity to small damages make <span>SJ-AD</span> a promising solution for proactive maintenance and risk management in various structural systems.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106031"},"PeriodicalIF":9.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350317","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

Indoor visual positioning using stationary semantic distribution registration and building information modeling

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-08 DOI: 10.1016/j.autcon.2025.106033

Xiaoping Zhou , Yukang Wang , Jichao Zhao , Maozu Guo

Indoor Visual Positioning (IVP) is a prerequisite for applications like indoor location-based services in smart buildings. Building Information Modeling (BIM), representing physical and functional characteristics of buildings, is widely used in IVP. Existing BIM-based IVP methods register visual features from sensed images to BIM but suffer inaccuracies caused by dramatic disturbances from unstable objects like chairs. Stationary objects like walls may address this issue and provide a more reliable IVP scheme, yet it remains to be explored. This paper proposes an IVP scheme leveraging stationary object registration from sequential images to BIM, termed Stationary Semantic Distribution-driven Visual Positioning (S2VP). In the offline phase, S2VP generates “stationary semantic distribution-positions” datasets from BIM. During positioning, the stationary semantic distribution of sensed images is first estimated, and the indoor position is computed via a particle filter model. Experiments show that S2VP achieves an average positioning error of 0.37 m, outperforming existing methods.

{"title":"Indoor visual positioning using stationary semantic distribution registration and building information modeling","authors":"Xiaoping Zhou , Yukang Wang , Jichao Zhao , Maozu Guo","doi":"10.1016/j.autcon.2025.106033","DOIUrl":"10.1016/j.autcon.2025.106033","url":null,"abstract":"<div><div>Indoor Visual Positioning (IVP) is a prerequisite for applications like indoor location-based services in smart buildings. Building Information Modeling (BIM), representing physical and functional characteristics of buildings, is widely used in IVP. Existing BIM-based IVP methods register visual features from sensed images to BIM but suffer inaccuracies caused by dramatic disturbances from unstable objects like chairs. Stationary objects like walls may address this issue and provide a more reliable IVP scheme, yet it remains to be explored. This paper proposes an IVP scheme leveraging stationary object registration from sequential images to BIM, termed Stationary Semantic Distribution-driven Visual Positioning (S2VP). In the offline phase, S2VP generates “stationary semantic distribution-positions” datasets from BIM. During positioning, the stationary semantic distribution of sensed images is first estimated, and the indoor position is computed via a particle filter model. Experiments show that S2VP achieves an average positioning error of 0.37 m, outperforming existing methods.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106033"},"PeriodicalIF":9.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369764","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

Compaction test of rolled rockfill material using multimodal Rayleigh wave dispersion inversion

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-07 DOI: 10.1016/j.autcon.2025.106043

Yao Wang , Hai Liu , Xu Meng , Guiquan Yuan , Huiguo Wang , Ruige Shi , Mengxiong Tang , Billie F. Spencer

This paper investigated the potential of Rayleigh wave multimodal dispersion inversion to advance automatic construction through real-time, in-situ measurement of rockfill compaction. An acquisition system and inversion method were developed to automate the process of obtaining compaction depth profiles and implemented during a dynamic rolling test. A rockfill layer under 2 m was tested, with Rayleigh wave data collected after different compaction passes. Multi-mode dispersion inversion was used to analyze the material's velocity structure. The results show that multimodal dispersion curves accurately reflect changes in compaction. As compaction increased, the velocity structure transitioned from a complex layered to a uniform single-layered form, with a corresponding rise in the elastic modulus. Furthermore, the calculated Young's modulus exhibited a strong positive correlation with dry density measured by excavation tests. These findings offer an approach for intelligent compaction techniques, contributing to the automation of in-situ compaction monitoring in rockfill construction.

{"title":"Compaction test of rolled rockfill material using multimodal Rayleigh wave dispersion inversion","authors":"Yao Wang , Hai Liu , Xu Meng , Guiquan Yuan , Huiguo Wang , Ruige Shi , Mengxiong Tang , Billie F. Spencer","doi":"10.1016/j.autcon.2025.106043","DOIUrl":"10.1016/j.autcon.2025.106043","url":null,"abstract":"<div><div>This paper investigated the potential of Rayleigh wave multimodal dispersion inversion to advance automatic construction through real-time, in-situ measurement of rockfill compaction. An acquisition system and inversion method were developed to automate the process of obtaining compaction depth profiles and implemented during a dynamic rolling test. A rockfill layer under 2 m was tested, with Rayleigh wave data collected after different compaction passes. Multi-mode dispersion inversion was used to analyze the material's velocity structure. The results show that multimodal dispersion curves accurately reflect changes in compaction. As compaction increased, the velocity structure transitioned from a complex layered to a uniform single-layered form, with a corresponding rise in the elastic modulus. Furthermore, the calculated Young's modulus exhibited a strong positive correlation with dry density measured by excavation tests. These findings offer an approach for intelligent compaction techniques, contributing to the automation of in-situ compaction monitoring in rockfill construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106043"},"PeriodicalIF":9.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143292210","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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