[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120383-en":3,"doc-seo-120383-105":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},120383,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predictive Analytics for Military Construction Overruns - A Machine Learning Approach - Thesis Abstract","This research analyzes cost and schedule overruns in a defined set of Air Force MILCON projects using data from the Built Infrastructure Common Operating Picture (BICOP). Advanced machine learning methods, including LASSO regression and Random Forest, are used to identify patterns and develop predictive models intended to improve project-manager decision-making. Results show location as the most influential factor for cost and schedule overrun tendencies. The best schedule-overrun model reaches 82% accuracy, 20% above the no-information baseline, while the best cost-overrun model reaches 70% accuracy, 11% above baseline. Despite outperforming the baseline, the models are not consistently accurate enough for reliable decision support, indicating further refinement and testing are needed before early-delays or cost-increase flags can be trusted.","Air Force Institute of Technology  \nAFIT Scholar  \n\n| Theses and Dissertations | Student Graduate Works |\n| --- | --- |\n| 3-2024\u003Cbr>Predictive Analytics for Military Construction Learning Approach\u003Cbr>William D. Hunter\u003Cbr>Follow this and additional works at: [https://scholar.afit.edu/etd](https://scholar.afit.edu/etd)\u003Cbr> Part of the Construction Engineering and Management Commons | Overruns: A Machine |\n\nRecommended Citation  \nHunter, William D., \"Predictive Analytics for Military Construction Overruns: A Machine Learning Approach\"(2024) . Theses and Dissertations. 7752.  \n[https://scholar.afit.edu/etd/7752](https://scholar.afit.edu/etd/7752)  \nThis Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, [please contact AFIT.ENWL.Repository@us.af.mil](please contact AFIT.ENWL.Repository@us.af.mil).  \nPREDICTIVE ANALYTICS FOR MILITARY CONSTRUCTION OVERRUNS: A  \nMACHINE LEARNING APPROACH  \nWilliam D Hunter, 2nd Lieutenant, USAF  \nAFIT-ENV-MS-24-M-132  \nDEPARTMENT OF THE AIR FORCE  \nAIR UNIVERSITY  \nAIR FORCE INSTITUTE OF TECHNOLOGY  \nWright-Patterson Air Force Base, Ohio  \nDISTRIBUTION STATEMENT A.  \nAPPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.  \nThe views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.  \nAFIT-ENV-MS-24-M-132  \nPREDICTIVE ANALYTICS FOR MILITARY CONSTRUCTION OVERRUNS: A  \nMACHINE LEARNING APPROACH  \nPresented to the Faculty  \nDepartment of Systems Engineering and Management  \nGraduate School of Engineering and Management  \nAir Force Institute of Technology  \nAir University  \nAir Education and Training Command  \nIn Partial Fulfillment of the Requirements for the  \nDegree of Master of Science in Engineering Management  \nWilliam D Hunter, BS  \n2nd Lieutenant, USAF  \nMarch 2024  \nDISTRIBUTION STATEMENT A.  \nAPPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.  \nAFIT-ENV-MS-24-M-132  \nPREDICTIVE ANALYTICS FOR MILITARY CONSTRUCTION OVERRUNS: A  \nMACHINE LEARNING APPROACH  \nWilliam D Hunter, BS  \n2nd Lieutenant, USAF  \nCommittee Membership:  \nDr. Brent T Langhals, PhD  \nChair  \nMajor Brigham Moore, PhD, P.E.  \nMember  \nDr. Torrey Wagner, PhD  \nMember  \nAFIT-ENV-MS-24-M-132  \nAbstract  \nThis research focused on analyzing cost and schedule overruns in a specific set ofAir Force MILCON projects. Using data from the Built Infrastructure Common Operating Picture (BICOP), several advanced machine learning techniques, including LASSO regression and Random Forest, were applied to uncover patterns and build predictive models. The work sought to enhance the decision-making capabilities and efficiency of project managers within the Air Force through data-driven insights. A key finding from the models indicated that location was the most important factor in influencing a project’s tendency toward cost and schedule overrun. The best model for predicting schedule overrun achieved an accuracy of 82%, which was 20% higher than the no-information rate. In contrast, the best model in predicting cost overrun attained an accuracy of 70%, which was 11% higher than the no-information rate. Although the predictive models demonstrated some level of predictive capability and exceeded the noinformation rate, they were not consistently accurate enough to be recommended as an aid for decision-making. The findings suggest that while machine learning can provide valuable insights into factors influencing project overruns, further refinement and testing are needed before these models can be considered reliable for use to flag early signs of potential construction project delays or cost increases.  \nAcknowledgments  ","cbCaifTU88BIPZTu","https://ap.wps.com/l/cbCaifTU88BIPZTu","pdf",2267814,1,100,"English","en",105,"# List of Figures\n# List of Tables\n# 1. Introduction\n## Background\n## Problem Statement\n## Objectives\n# 2. Literature Review\n## Military Construction (MILCON) Projects","[{\"question\":\"What data source and methods are used to study MILCON cost and schedule overruns?\",\"answer\":\"The study uses Built Infrastructure Common Operating Picture (BICOP) data and applies machine learning techniques including LASSO regression and Random Forest.\"},{\"question\":\"Which factor most strongly influences the likelihood of overruns?\",\"answer\":\"Location is identified as the most important factor affecting a project’s tendency toward cost and schedule overrun.\"},{\"question\":\"How accurate are the best predictive models for schedule and cost overruns?\",\"answer\":\"The best schedule-overrun model achieves 82% accuracy, outperforming the no-information rate by 20%. The best cost-overrun model achieves 70% accuracy, improving on the no-information rate by 11%.\"}]","Predictive Analytics for Military Construction Overruns - A Machine Learning Approach - Thesis Abstract | PDF",1785729755,252,{"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},"predictive-analytics-for-military-construction-overruns-a-machine-learning-approach-thesis-abstract","",{"@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/predictive-analytics-for-military-construction-overruns-a-machine-learning-approach-thesis-abstract/120383/",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 data source and methods are used to study MILCON cost and schedule overruns?","Question",{"text":75,"@type":76},"The study uses Built Infrastructure Common Operating Picture (BICOP) data and applies machine learning techniques including LASSO regression and Random Forest.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factor most strongly influences the likelihood of overruns?",{"text":80,"@type":76},"Location is identified as the most important factor affecting a project’s tendency toward cost and schedule overrun.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the best predictive models for schedule and cost overruns?",{"text":84,"@type":76},"The best schedule-overrun model achieves 82% accuracy, outperforming the no-information rate by 20%. 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