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Effectiveness of retrieval augmented generation-based large language models for generating construction safety information
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-13 DOI: 10.1016/j.autcon.2024.105926
Miyoung Uhm, Jaehee Kim, Seungjun Ahn, Hoyoung Jeong, Hongjo Kim
{"title":"Effectiveness of retrieval augmented generation-based large language models for generating construction safety information","authors":"Miyoung Uhm, Jaehee Kim, Seungjun Ahn, Hoyoung Jeong, Hongjo Kim","doi":"10.1016/j.autcon.2024.105926","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105926","url":null,"abstract":"While Generative Pre-Trained Transformers (GPT)-based models offer high potential for context-specific information generation, inaccurate numerical responses, a lack of detailed information, and hallucination problems remain as the main challenges for their use in assisting safety engineering and management tasks. To address the challenges, this paper systematically evaluates the effectiveness of the Retrieval-Augmented Generation-based GPT (RAG-GPT) model for generating detailed and specific construction safety information. The RAG-GPT model was compared with four other GPT models, evaluating the models' responses from three different groups––2 researchers, 10 construction safety experts, and 30 construction workers. Quantitative analysis demonstrated that the RAG-GPT model showed superior performance compared to the other models. Experts rated the RAG-GPT model as providing more contextually relevant answers, with high marks for accuracy and essential information inclusion. The findings indicate that the RAG strategy, which uses vector data to enhance information retrieval, significantly improves the accuracy of construction safety information.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"29 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816505","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
Structural performance evaluation via digital-physical twin and multi-parameter identification
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-12 DOI: 10.1016/j.autcon.2024.105907
Yixuan Chen, Sicong Xie, Jian Zhang
{"title":"Structural performance evaluation via digital-physical twin and multi-parameter identification","authors":"Yixuan Chen, Sicong Xie, Jian Zhang","doi":"10.1016/j.autcon.2024.105907","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105907","url":null,"abstract":"The performance of existing structures is often compromised by damage and condition changes, challenging current evaluation methods in accurately assessing their service status. This paper introduces a structural performance evaluation method via digital-physical twin and multi-parameter identification. Key features include: (1) a digital twin framework that integrates non-contact sensing data with finite element models. (2) a technique for local stiffness reduction using intelligent crack inspection data, where deep learning extracts crack information and a mechanical model calculates stiffness reduction coefficients. (3) a multi-parameter identification approach combining non-contact monitoring data with twin substructure models, employing substructure interaction technology and an enhanced unscented Kalman filter algorithm to identify critical parameters like support stiffness. The method's feasibility is demonstrated through a case study involving a frame structure, offering a new paradigm for the safety assessment of existing structures.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"10 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816507","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
From surveys to simulations: Integrating Notre-Dame de Paris' buttressing system diagnosis with knowledge graphs
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-12 DOI: 10.1016/j.autcon.2024.105927
Antoine Gros, Livio De Luca, Frédéric Dubois, Philippe Véron, Kévin Jacquot
{"title":"From surveys to simulations: Integrating Notre-Dame de Paris' buttressing system diagnosis with knowledge graphs","authors":"Antoine Gros, Livio De Luca, Frédéric Dubois, Philippe Véron, Kévin Jacquot","doi":"10.1016/j.autcon.2024.105927","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105927","url":null,"abstract":"The assessment of structural safety and a thorough understanding of buildings' structural behavior are critical to enhancing the resilience of the built environment. Cultural Heritage (CH) buildings present unique diagnosis challenges due to their diverse designs and construction techniques, often requiring attention during maintenance or disaster relief efforts. However, collaboration across CH and Architecture, Engineering, and Construction (AEC) fields is hindered by increasing information complexity and prolonged feedback loops. This paper introduces a methodological approach utilizing Knowledge Graph technologies to integrate structural diagnosis information and processes. The approach is applied to the diagnosis of the Notre-Dame de Paris buttressing system, demonstrated through a proof-of-concept knowledge system. By leveraging Knowledge Graph functionalities, insights are derived from the spatialization and provenance of mechanical phenomena, including observed or simulation-predicted cracks in mortar-bound masonry.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"86 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816508","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
Visual–tactile learning of robotic cable-in-duct installation skills
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-12 DOI: 10.1016/j.autcon.2024.105905
Boyi Duan, Kun Qian, Aohua Liu, Shan Luo
{"title":"Visual–tactile learning of robotic cable-in-duct installation skills","authors":"Boyi Duan, Kun Qian, Aohua Liu, Shan Luo","doi":"10.1016/j.autcon.2024.105905","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105905","url":null,"abstract":"Cable-in-duct installation is one of the most challenging contact-rich interior finishing tasks for construction robots. Such precise robotic cable manipulation skills are expected to be endowed with high adaptability towards unstructured on-site construction activities via Sim2Real transfer. This paper presents a Sim2Real transferable reinforcement learning (RL) policy learning method for multi-stage robotic cable-in-duct installation, employing reward shaping to support unified task completion through a multi-stage RL policy. Specifically, the Foreground-aware Siamese Tactile Regression Network (FSTR-Net) is introduced as a feature-level unsupervised domain adaptation method to enhance the Sim2Real transfer of the RL strategy. Evaluations demonstrate that the robotic skill for cable-in-duct installation attains a success rate exceeding 98% in the simulator. FSTR-Net achieves over 99% accuracy for tactile-based in-hand fish tape pose estimation. Furthermore, real-world experiments show an average success rate of 95.8%, validating the RL strategy’s generalization and the approach’s effectiveness in mitigating the domain gap.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"8 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816506","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
Neural topic modeling of machine learning applications in building: Key topics, algorithms, and evolution patterns
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-12 DOI: 10.1016/j.autcon.2024.105890
Peng Zhou, Yifan Qi, Qian Yang, Yuan Chang
{"title":"Neural topic modeling of machine learning applications in building: Key topics, algorithms, and evolution patterns","authors":"Peng Zhou, Yifan Qi, Qian Yang, Yuan Chang","doi":"10.1016/j.autcon.2024.105890","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105890","url":null,"abstract":"The application of machine learning (ML) in the building domain has rapidly evolved due to developments in ML algorithms. Abundant studies have reviewed the use of ML algorithms to address building-domain-related challenges, but some research questions remain unclear: (i) what is the landscape of ML application topics in building domain, (ii) what are the preferences among different ML application topics and algorithms, and (iii) how these topics, ML algorithms, and their preferences evolve until forming current landscape. To address these aspects, an ML-based topic modeling (TM) approach was used in this paper to identify all ML application topics, elucidate the horizontal correlation and vertical knowledge hierarchy among the topics to reveal their static correlation and dynamic evolution with ML algorithms. Several findings that answered each research question were drawn, and recommendations that can facilitate balanced and rational ML advancements in the building domain are proposed for future research.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"46 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816511","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
Semi-supervised crack detection using segment anything model and deep transfer learning
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-11 DOI: 10.1016/j.autcon.2024.105899
Jiale Li, Chenglong Yuan, Xuefei Wang, Guangqi Chen, Guowei Ma
{"title":"Semi-supervised crack detection using segment anything model and deep transfer learning","authors":"Jiale Li, Chenglong Yuan, Xuefei Wang, Guangqi Chen, Guowei Ma","doi":"10.1016/j.autcon.2024.105899","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105899","url":null,"abstract":"Computer vision models have shown great potential in pavement distress detection. There is still challenge of low robustness under different scenarios. The model robustness is enhanced with more annotated data. However, this approach is labor-intensive and not a sustainable long-term solution. This paper proposes a semi-supervised instance segmentation method for road distress detection based on deep transfer learning. The interactive segmentation method utilizing SAM are used to enhance the production efficiency of segmentation datasets. The DCNv3 and lightweight segmentation heads are strategically designed to offset potential speed losses. The deep transfer learning method fine-tunes the pre-trained models, enhancing their competency for new tasks. The proposed model achieves comparable performance to supervised learning with fewer annotated data, accurately determining crack dimensions across varied scenarios. This paper provides an efficient and practical approach for pavement distress identification using the hybrid computer vision methodology.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"3 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816510","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
Automated identification of hazardous zones on construction sites using a 2D digital information model
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-11 DOI: 10.1016/j.autcon.2024.105922
Jongwoo Cho, Jiyu Shin, Junyoung Jang, Tae Wan Kim
{"title":"Automated identification of hazardous zones on construction sites using a 2D digital information model","authors":"Jongwoo Cho, Jiyu Shin, Junyoung Jang, Tae Wan Kim","doi":"10.1016/j.autcon.2024.105922","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105922","url":null,"abstract":"Construction sites are high-risk environments owing to the dynamic changes and improper placement of temporary facilities, requiring comprehensive safety management and spatial hazard analyses. Existing construction site layout planning (CSLP) studies have limitations in identifying hazardous zones and accommodating the flexibility stakeholders require. This paper introduces a site information model framework to define digital objects and relationships in the CSLP, proposing methods to identify automatically unsafe spaces by considering facility hazards and visibility. By establishing ontological relationships and developing algorithms to quantify risk in unoccupied spaces, the framework identifies unsafe spaces in alignment with the perceptions of safety practitioners. Case studies at four sites demonstrated the reliability of the framework with a high precision, recall, and an F1-score of 0.945. This framework allows safety practitioners to evaluate systematically and improve site layouts during the preconstruction phase. Future integration with scheduling information could enhance the spatiotemporal hazard analysis and contribute to safer construction sites.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"38 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816509","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
Automated six-degree-of-freedom Stewart platform for heavy floor tiling
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-10 DOI: 10.1016/j.autcon.2024.105932
Siwei Chang, Zemin Lyu, Jinhua Chen, Tong Hu, Rui Feng, Haobo Liang
{"title":"Automated six-degree-of-freedom Stewart platform for heavy floor tiling","authors":"Siwei Chang, Zemin Lyu, Jinhua Chen, Tong Hu, Rui Feng, Haobo Liang","doi":"10.1016/j.autcon.2024.105932","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105932","url":null,"abstract":"While existing floor tiling robots provide automated tiling for small tiles, robots designed for large and heavy tiles are rare. This paper develops a six-degree-of-freedom Stewart platform-based floor tiling robot for automated tiling of heavy tiles. The key contributions of this paper are: 1) establishing mechanical and kinematic models for a parallel robot to enhance the payload capacity of existing floor tiling robots. 2) designing a dual-camera system for precise visual alignment by capturing tile corner points from a complete perspective. Experimental validation demonstrated the robot's ability to automatically tile heavy floor tiles, with highly synchronized motions. The dual camera system achieved angle and distance deviations within ±0.001° and 0.5 mm. Quantitative analysis using the Borg RPE scale and EMG signals validated a reduction in physical strain. This research provides a feasible solution for automating heavy floor tile installation, effectively mitigating physical fatigue while enhancing the tiling alignment precision.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"22 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816513","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
Parametric design methodology for developing BIM object libraries in construction site modeling
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-10 DOI: 10.1016/j.autcon.2024.105897
Vito Getuli, Alessandro Bruttini, Farzad Rahimian
{"title":"Parametric design methodology for developing BIM object libraries in construction site modeling","authors":"Vito Getuli, Alessandro Bruttini, Farzad Rahimian","doi":"10.1016/j.autcon.2024.105897","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105897","url":null,"abstract":"The adoption of Building Information Modeling (BIM) in construction site layout planning and activity scheduling faces challenges due to the lack of standardized approaches for digitally reproducing and organizing site elements that meet information requirements of diverse regulatory frameworks and stakeholders' use cases. This paper addresses the question of how to streamline the development of BIM objects for construction site modeling by proposing a vendor-neutral parametric design methodology and introduces a dedicated hierarchical structure for BIM object libraries to support users in their implementation. The methodology includes a six-step process for creating informative content, parametric geometries, and documentation, and is demonstrated through the development and implementation of a construction site BIM object library suitable for the Italian context. This approach fills a gap in BIM object development standards and offers a foundation for future research, benefiting practitioners and industry stakeholders involved in BIM-based site layout modeling and activity planning.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"10 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816518","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
Ensemble learning framework for forecasting construction costs
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-09 DOI: 10.1016/j.autcon.2024.105903
Omar Habib, Mona Abouhamad, AbdElMoniem Bayoumi
{"title":"Ensemble learning framework for forecasting construction costs","authors":"Omar Habib, Mona Abouhamad, AbdElMoniem Bayoumi","doi":"10.1016/j.autcon.2024.105903","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105903","url":null,"abstract":"Construction cost forecasting is vital for tendering processes, enabling the evaluation of bidding offers to maximize revenues and avoid losses. In recent years, the automation of this forecasting process has gained attention due to the limitations of traditional approaches that rely on human experts, which can lead to subjective judgments. This paper introduces an ensemble learning decision-support framework that combines regression random forests and gradient-boosting regression trees through regression voting to automate cost estimation for residential and commercial projects. Evaluation of this approach using the dataset from San Francisco’s building inspection department in the United States demonstrated significant performance improvements over support vector regression. This paper highlights the importance of automating construction cost forecasting with artificial intelligence techniques for construction companies and is expected to encourage companies and building inspection departments worldwide to publish more datasets for the application of advanced deep learning models.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"86 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816521","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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