[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119515-en":3,"doc-seo-119515-105":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":4,"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},119515,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Temporal Analysis of Construction Safety Incidents in Southeastern U.S. - Using Machine Learning Techniques","Construction safety incidents remain a significant concern, especially in the Southeastern U.S., where industry risk is high. This thesis applies machine learning to uncover temporal patterns in fatal, hospitalized, and non-hospitalized incidents and to strengthen proactive safety management. Data from the OSHA database covers construction safety incidents from 2013 to 2023, totaling 1,963 cases with project and injury details. Models are built in Python and Jupyter using logistic regression, decision trees, random forest, SVM, and KNN, evaluating predictive performance and key daily, seasonal, and yearly trends.","Georgia Southern University  \nGeorgia Southern Commons  \n\n| Electronic Theses and Dissertations | Jack N. Averitt College of Graduate Studies |\n| --- | --- |\n| Spring 2025\u003Cbr>Temporal Analysis of Construction Safety Incidents in Southeastern U.S. Using Machine Learning Techniques Mayowa O. Oladele\u003Cbr>Follow this and additional works at: [https://digitalcommons.georgiasouthern.edu/etd](https://digitalcommons.georgiasouthern.edu/etd)\u003Cbr> Part of the Architectural Engineering Commons, Civil Engineering Commons, Construction Engineering Commons, and the Construction Engineering and Management Commons |  |\n\nRecommended Citation  \nOladele, Mayowa O., \"Temporal Analysis of Construction Safety Incidents in Southeastern U.S. Using Machine Learning Techniques\" (2025) . Electronic Theses and Dissertations. 2967.  \n[https://digitalcommons.georgiasouthern.edu/etd/2967](https://digitalcommons.georgiasouthern.edu/etd/2967)  \nThis thesis (open access) is brought to you for free and open access by the Jack N. Averitt College of Graduate Studies at Georgia Southern Commons. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Georgia Southern Commons. For more information, please [contact digitalcommons@georgiasouthern.edu](contact digitalcommons@georgiasouthern.edu).  \nTEMPORAL ANALYSIS OF CONSTRUCTION SAFETY INCIDENTS IN SOUTHEASTERN U. S.  \nUSING MACHINE LEARNING TECHNIQUES  \nby  \nMAYOWA OLADELE  \nUnder the Direction of M. Myung Jeong  \nABSTRACT  \nConstruction safety incidents remain a significant concern, particularly in the Southeastern U.S. due to the high-risk nature of the industry. Analyzing patterns in these incidents can help improve safety practices and reduce accidents. Machine learning (ML) techniques were employed in this study to identify temporal patterns in construction safety incidents, aiming to enhance proactive safety management. The machine learning methods used in this research included logistic regression, decision trees, random forest, support vector machine (SVM), and K-nearest neighbors (KNN) .  \nThe objective of the study was to analyze temporal trends in safety incidents and identify the most effective machine learning technique for predicting and classifying these incidents. The data used in this study was sourced from the Occupational Safety and Health Administration (OSHA) database, focusing on safety incidents in the Southeastern U.S. construction industry from 2013 to 2023. A total of 1,963 cases were analyzed, and categorized as fatal, hospitalized, and non-hospitalized. Each case included detailed project and injury information. The analysis was conducted using Python and Jupyter Notebook, with separate notebooks created for each machine learning technique to streamline the coding and evaluation process. Three main questions were explored: 1) Which machine learning technique offers the most accurate prediction in classifying between fatal and non-fatal incidents? 2) How can machine learning models be used to assess the likelihood of fatal and non-fatal incidents? 3) What are the key temporal patterns (daily, seasonal, and yearly) observed in construction safety incidents in the Southeastern U.S.? The findings showed that random forest and decision trees were the most effective in predicting safety incidents, with random forest achieving the highest accuracy and reliability for both fatal and non-fatal  \nclassifications. This study highlights the potential of machine learning in improving construction safety by offering more accurate predictions and insights into high-risk incidents, aiding in better decision-making and risk management strategies.  \nINDEX WORDS: Temporal analysis, Construction safety, Machine learning, Risk prediction, Southeastern U.S., Safety incidents, OSHA.  \nTEMPORAL ANALYSIS OF CONSTRUCTION SAFETY INCIDENTS IN SOUTHEASTERN U. S.  \nUSING MACHINE LEARNING TECHNIQUES  \nby  \nMAYOWA OLADELE  \nB.S., The Federal Polytechnic Ado Ekiti, Nige","cbCaic2KxGASOpZm","https://ap.wps.com/l/cbCaic2KxGASOpZm","pdf",2505942,1,64,"English","en",105,"# Abstract\n# Dedication\n# Acknowledgments\n# Table of Contents\n## Study objectives and research questions\n## Data source and case categorization\n## Machine learning methods and evaluation approach\n## Results and key temporal patterns","[{\"question\":\"What is the goal of the study on construction safety incidents?\",\"answer\":\"The study analyzes temporal trends in construction safety incidents and identifies the most effective machine learning technique for predicting and classifying fatal versus non-fatal outcomes.\"},{\"question\":\"What data does the thesis use, and what time span is covered?\",\"answer\":\"The analysis uses OSHA database records for the construction industry in the Southeastern U.S. from 2013 to 2023.\"},{\"question\":\"Which machine learning methods performed best for prediction?\",\"answer\":\"Random forest and decision trees were the most effective, with random forest achieving the highest accuracy and reliability for both fatal and non-fatal classifications.\"}]","Temporal Analysis of Construction Safety Incidents in Southeastern U.S. - Using Machine Learning Techniques | PDF",1785724730,161,{"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},"temporal-analysis-of-construction-safety-incidents-in-southeastern-us-using-machine-learning-techniques","",{"@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/temporal-analysis-of-construction-safety-incidents-in-southeastern-us-using-machine-learning-techniques/119515/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the study on construction safety incidents?","Question",{"text":75,"@type":76},"The study analyzes temporal trends in construction safety incidents and identifies the most effective machine learning technique for predicting and classifying fatal versus non-fatal outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data does the thesis use, and what time span is covered?",{"text":80,"@type":76},"The analysis uses OSHA database records for the construction industry in the Southeastern U.S. from 2013 to 2023.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods performed best for prediction?",{"text":84,"@type":76},"Random forest and decision trees were the most effective, with random forest achieving the highest accuracy and reliability for both fatal and non-fatal classifications.","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"]