[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120211-en":3,"doc-seo-120211-105":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},120211,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Investigating on Combining System Dynamics and Machine Learning for Predicting Safety Performance in Construction Projects - Conference Paper","This study develops an investigative approach that integrates system dynamics modeling with machine learning to build an early-warning decision-support mechanism for safety management in construction projects. The model incorporates 53 interrelated indicators and construction-phase information during planning, then is validated through extreme-state and sensitivity tests using accident trends. Simulated projects are stored into datasets with serious versus fatal accident rates, preprocessed so features reflect planning-phase data while the target is accident occurrence. Results indicate feasible combination for safety prediction without real project data, and highlight differing model behavior for serious accuracy versus fatal detection limitations due to low fatal frequency.","IOP Conference Series: Earth and Environmental Science  \nPAPER • OPEN ACCESS  \nInvestigating on Combining System Dynamics and Machine Learning for Predicting Safety Performance in Construction Projects  \nTo cite this article: Mirza Muntasir Nishat et al 2024 IOP Conf. Ser. : Earth Environ. Sci. 1389 012034  \nYou may also like  \n-IAEA  \n-Research on Construction of Safety Performance Measurement Index System Based on Mathematical Model in Computer Environment  \nLi Zuo and Fengtai Mei  \n-Evaluating the traffic characteristics of the road network in Ramadi city using sustainable transportation indicators (Hazard Index)  \nRafal M. khudier, Thaer Sh Mahmood and Hamid A. Awad  \nView the article online for updates and enhancements.  \nThis content was downloaded from IP address [129.241.236.103](129.241.236.103) on 23/09/2024 at 14:09  \nIOP Conf. Series: Earth and Environmental Science 1389 (2024) 012034 doi:10.1088/1755-1315/1389/1/012034  \nInvestigating on Combining System Dynamics and Machine Learning for Predicting Safety Performance in Construction Projects  \nMirza Muntasir Nishat1*, Ingrid Renolen Borkenhagen1, Jenni Sveen Olsen1 and Antoine Rauzy1  \n1 Norwegian University of Science and Technology (NTNU), Trondheim, Norway  \n*[E-mail: mirza.m.nishat@ntnu.no](E-mail: mirza.m.nishat@ntnu.no)  \nAbstract. This study focuses on an investigative approach to combine system dynamics and machine learning algorithms to develop an early warning system for the safety management of construction projects. As the construction industry is highly accident-prone, developing a decision-support system has always been a challenge for the research community. Therefore, 53 indicators that in􀏐luence eachother and the construction phase were included in the planning phase of the model. The system dynamics model was validated using extreme state and sensitivity tests, which showed reasonable trends in the number of accidents. For each simulated project, all indicator data was stored in one dataset, using two different accident rates: one for serious and one for fatal accidents. Consequently, two separate datasets were generated, one for serious accidents, which was balanced, and one for fatal accidents. Machine learning was applied to both datasets to predict safety performance. The datasets were pre-processed so that the features consisted only of data from the planning phase, with the target feature being occurrence of accident. The study revealed two key 􀏐indings. First, the study showed the possibility of combining system dynamics and machine learning for safety predictions in cases where real project data is not available. Secondly, the results showed that it is possible to carry out projects with a higher risk of major accidents and provide an early warning of poor safety performance. The data set with serious accidents resulted in lower accuracy but higher recall values. However, the models struggled to identify fatal accidents as the values for the fatal accident dataset were too low. Therefore, it was discussed how other safety measurements could be more appropriate. Thus, the combination of system dynamics and machine learning has the potential to serve as a decision-support tool in construction projects and to disseminate knowledge about safety performance.  \n1. Introduction  \nWithout a doubt, the construction business is one of the riskiest industries in the global economy. This claim is supported by several sources, including a 2022 study by [1] concentrating on the high rate of work-related injuries and fatalities in the Norwegian construction sector. Nine fatalities were reported in Norway in 2021. Since 2012, the yearly average fatality rate has been high, with slipping from buildings and being slammed by objects being the most prevalent causes  \nContent from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title","cbCaibWq5TP36h7a","https://ap.wps.com/l/cbCaibWq5TP36h7a","pdf",1455521,1,13,"English","en",105,"# Abstract\n# 1. Introduction\n# Safety performance indicators and metrics\n# Study approach: combining system dynamics and machine learning\n# Validation and dataset construction\n# Results and discussion\n# Conclusion","[{\"question\":\"What is the core objective of the study on construction safety performance?\",\"answer\":\"To combine system dynamics and machine learning to create an early-warning decision-support approach for safety management in construction projects.\"},{\"question\":\"How are safety indicators and construction phases used in the model?\",\"answer\":\"The planning phase includes 53 interrelated indicators influencing each other and the construction phase, and simulated project data are stored into datasets for analysis.\"},{\"question\":\"Why is fatal-accident prediction more difficult in the reported results?\",\"answer\":\"Fatal accidents have too low values in the fatal-accident dataset, causing the machine learning models to struggle in identifying them.\"}]","Investigating on Combining System Dynamics and Machine Learning for Predicting Safety Performance in Construction Projects - Conference Paper | PDF",1785728745,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"investigating-on-combining-system-dynamics-and-machine-learning-for-predicting-safety-performance-in-construction-projects-conference-paper","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/investigating-on-combining-system-dynamics-and-machine-learning-for-predicting-safety-performance-in-construction-projects-conference-paper/120211/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the core objective of the study on construction safety performance?","Question",{"text":76,"@type":77},"To combine system dynamics and machine learning to create an early-warning decision-support approach for safety management in construction projects.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are safety indicators and construction phases used in the model?",{"text":81,"@type":77},"The planning phase includes 53 interrelated indicators influencing each other and the construction phase, and simulated project data are stored into datasets for analysis.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is fatal-accident prediction more difficult in the reported results?",{"text":85,"@type":77},"Fatal accidents have too low values in the fatal-accident dataset, causing the machine learning models to struggle in identifying them.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]