[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119733-en":3,"doc-seo-119733-105":30,"detail-sidebar-cat-0-en-105":91},{"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},119733,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Developing predictive models of construction fatality characteristics using machine learning","Construction fatalities impose substantial economic and emotional burdens on construction employees, families, and organizations, making it essential to identify critical factors and build predictive approaches for fatality characteristics. Using comprehensive datasets and advanced machine learning, this study develops models for nature of injury (NOI), part of body (POB), source of injury (SOI), and event or exposure (EOE). Thirty explanatory variables from 694 NIOSH-reported fatalities are used, achieving prediction accuracies of 56.6%, 54.0%, 76.5%, and 84.9%, with particularly strong performance for fall fatalities. Findings support practical guidance for emergency response planning and first aid services.","ORCA – Online Research @  \nCardiff  \nThis is an Open Access document downloaded from ORCA, Cardiff University's institutional repository:[https://orca.cardiff.ac.uk/id/eprint/158518/](https://orca.cardiff.ac.uk/id/eprint/158518/)  \nThis is the author’s version of a work that was submitted to / accepted for publication.  \nCitation for final published version:  \nZhu, Jianbo, Shi, Qianqian, Li, Qiming, Shou, Wenchi, Li, Haijiang and Wu, Peng 2023. Developing predictive models of construction fatality characteristics using machine learning. Safety Science 164 ,  \n106149. 10.1016/j.ssci.2023.106149 Publishers page: [http://dx.doi.org/10.1016/j.ssci.2023.106149](http://dx.doi.org/10.1016/j.ssci.2023.106149)  \nPlease note:  \nChanges made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite this paper.  \nThis version is being made available in accordance with publisher policies. See [http://orca.cf.ac.uk/policies.html](http://orca.cf.ac.uk/policies.html) for usage policies. Copyright and moral rights for publications made  \navailable in ORCA are retained by the copyright holders.  \nDeveloping predictive models of construction fatality characteristics using machine learning  \nJianbo Zhu1, Qianqian Shi2*, Qiming Li 1, Wenchi Shou3, Haijiang Li4, Peng Wu5  \n1. School of Civil Engineering, Southeast University, Nanjing 211189, China  \n2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China  \n3. School of Engineering, Design and Built Environment, Western Sydney University, Sidney, NSW 2751, Australia  \n4. Cardiff School of Engineering, Cardiff University, Cardiff CF24 3AA, U.K  \n5. School of Design and the Built Environment, Curtin University, Bentley 6102, Western Australia, Australia  \n*Corresponding author  \nAbstract  \nConstruction fatalities have significant economic and emotional burdens to construction employees, families, and organizations. Understanding critical factors influencing construction fatalities and eventually developing predictive models to predict construction fatality characteristics are therefore important. Such activities, which are traditionally based on questionnaire and simple statistical analysis, can now be conducted using comprehensive datasets on construction fatality and advanced machine learning approaches. This study aims to develop predictive models of construction fatality characteristics, including nature of injury (NOI), part of body (POB), source of injury (SOI), and event or exposure (EOE) using machine learning approaches. 30 explanatory variables from 694 fatalities reported by the National Institute for Occupational Safety and Health are used to build the predictive models, with prediction accuracy of 56.6%, 54.0%, 76.5% and 84.9% for NOI, POB, SOI, EOE respectively. Specifically, the model has a prediction accuracy of 84.7% for construction fall fatalities. Important indicators for predicting SOI and EOE are largely the same, with the most important ones being the likelihood offall, PFAS (functionality and relevant training), workers’ activity, onsite safety equipment and install safety protection. Similarly, important indicators for predicting NOI and POB include fall, PFAS, injury year, workers’ activity, location and safety equipment. The results will offer useful guidance for construction organizations to establish relevant emergency response plans and first aid facilities and services that correspond to the most likely NOI, POB, SOI and EOE on construction sites.  \nKeywords: Machine learning; Construction Safety; Fatality; Safety management  \n1. Introduction  \nHealth and safety issues are ongoing concerns for the construction industry. The construction industry employs a significant number of workforce and fata","cbCaimClPfxKbow4","https://ap.wps.com/l/cbCaimClPfxKbow4","pdf",873541,1,26,"English","en",105,"# Abstract\n# 1. Introduction","[{\"question\":\"What fatality characteristics does the study aim to predict?\",\"answer\":\"The study predicts nature of injury (NOI), part of body (POB), source of injury (SOI), and event or exposure (EOE).\"},{\"question\":\"What data and variables are used to build the predictive models?\",\"answer\":\"The models use 30 explanatory variables derived from 694 fatalities reported by the National Institute for Occupational Safety and Health (NIOSH).\"},{\"question\":\"How accurate are the machine learning predictions?\",\"answer\":\"Prediction accuracy reaches 56.6% for NOI, 54.0% for POB, 76.5% for SOI, and 84.9% for EOE, with an 84.7% accuracy for construction fall fatalities.\"}]","Developing predictive models of construction fatality characteristics using machine learning | PDF",1785726009,66,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"developing-predictive-models-of-construction-fatality-characteristics-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@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/developing-predictive-models-of-construction-fatality-characteristics-using-machine-learning/119733/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What fatality characteristics does the study aim to predict?","Question",{"text":75,"@type":76},"The study predicts nature of injury (NOI), part of body (POB), source of injury (SOI), and event or exposure (EOE).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and variables are used to build the predictive models?",{"text":80,"@type":76},"The models use 30 explanatory variables derived from 694 fatalities reported by the National Institute for Occupational Safety and Health (NIOSH).",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the machine learning predictions?",{"text":84,"@type":76},"Prediction accuracy reaches 56.6% for NOI, 54.0% for POB, 76.5% for SOI, and 84.9% for EOE, with an 84.7% accuracy for construction fall fatalities.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]