[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119261-en":3,"doc-seo-119261-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119261,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Improving Safety through Leveraging Machine Learning and Safety-Related Data in the Construction Industry - Conference Paper","This study proposes a conceptual framework that integrates safety-related data with machine learning to enhance understanding of construction safety performance and support safety management. Machine learning can uncover latent hazards and risks using project-specific safety data and improve safety and decision-making processes beyond dependence on historical safety outcomes. Results indicate meaningful performance gains through proactive risk and measure identification and through improved insight into complex upcoming projects shaped by technical and organizational complexity. The approach also requires careful handling of data compatibility, standardization gaps, misinformation risks, and ethical considerations such as transparency, privacy, and fairness.","IOP Conference Series: Earth and Environmental Science  \nPAPER • OPEN ACCESS  \nImproving Safety through Leveraging Machine Learning and Safety-Related Data in the Construction Industry  \nTo cite this article: Casper Pilskog Orvik 2024 IOP Conf. Ser. : Earth Environ. Sci. 1389 012012  \nView the article online for updates and enhancements.  \nYou may also like  \n-The Influence of Organisational Safety Climate Factors on Offsite Manufacturing Safety Performance  \nS C Vithanage, M C P Sing, P Davis et al.  \n- (Invited) Quantitative Measurement of the Safety Performance of Li-Ion Batteries  \nChisu Kim, Alexis Perea, David Rozon et al.  \n-Research on Construction of Safety Performance Measurement Index System Based on Mathematical Model in Computer Environment  \nLi Zuo and Fengtai Mei  \nThis content was downloaded from IP address [129.241.236.142](129.241.236.142) on 25/09/2024 at 12:09  \nIOP Conf. Series: Earth and Environmental Science 1389 (2024) 012012 doi:10.1088/1755-1315/1389/1/012012  \nImproving Safety through Leveraging Machine Learning and Safety-Related Data in the Construction Industry  \nCasper Pilskog Orvik*1  \n1 Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway.  \n* E-mail: [casper.p.orvik@ntnu.no](casper.p.orvik@ntnu.no)  \nAbstract. This study presents a conceptual framework for integrating safetyrelated data with machine learning to improve its understanding of safety performance and construction safety management. Machine Learning techniques could discover latent hazards and risks by utilizing project-speci􀏐ic safety-related data and improve safety and decision-making processes. Findings suggest that machine learning can signi􀏐icantly improve safety performance by proactively identifying risks and measures from safety-related data rather than relying solely on historical safety outcomes and data. This could also provide a better understanding of the forthcoming construction projects' complex challenges and the impact of increasingly technical and organizational complexities on safety.  \nHowever, challenges such as data compatibility, lack of standardization, misinformation risks, and ethical concerns (transparency, privacy, and fairness) necessitate a cautious approach to the use of machine learning. This proactive approach could lead to safer construction environments and continuous improvements in safety management. Future work will re􀏐ine data collection and develop predictive models, with the current research in the ‘DiSCo’ project aiming for sustainable safety improvements in the construction industry.  \n1. Introduction  \nThe construction industry is a dynamic and complex accident-prone industry, with the highest rate of fatal accidents in Europe, with over 22.5% of all work fatalities in 2021 related to the construction industry (Eurostat, 2023). Studies indicate that nearly 30-40% of accidents can be attributed to earlier project phases and design-related decisions (Driscoll et al., 2008, Behm, 2005). Operating project-based, each project has unique objectives and timelines, and increased uncertainty makes performing under these complex conditions more dif􀏐icult; therefore, it is essential to comprehend these new approaches and challenges for construction safety.  \nAlthough the industry constantly improves safety with new approaches and technologies, it still faces signi􀏐icant risks. Insuf􀏐icient traditional safety measures and strategies cannot address the multifaceted challenges the construction is facing when performing under complex conditions. The idea is to use innovative approaches like machine learning to identify and mitigate potential risks for improving overall safety. By better understanding the situation, construction projects could cope with project characteristics such as higher complexity and uncertainty. Such an approach could help the decision-makers improve current and newer unforeseen challenges.  \nContent from th","cbCaioN6cnuaIq6S","https://ap.wps.com/l/cbCaioN6cnuaIq6S","pdf",914500,1,14,"English","en",105,"# Abstract\n## Introduction\n## Machine learning and safety performance\n## Research question and approach\n## Conceptual framework and safety profile","[{\"question\":\"What is the main goal of the study on construction safety?\",\"answer\":\"The study aims to improve construction safety performance by integrating safety-related data with machine learning to better understand safety and support safety management decisions.\"},{\"question\":\"How can machine learning improve safety performance in construction projects?\",\"answer\":\"By using project-specific safety-related data, machine learning can proactively discover latent hazards and risks, identify risks and mitigation measures, and support improved decision-making compared with relying only on historical safety outcomes.\"},{\"question\":\"What challenges must be addressed when using machine learning for safety data?\",\"answer\":\"The paper highlights challenges including data compatibility issues, lack of standardization, risks of misinformation, and ethical concerns related to transparency, privacy, and fairness.\"}]","Improving Safety through Leveraging Machine Learning and Safety-Related Data in the Construction Industry - 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