[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128266-en":3,"doc-seo-128266-105":30,"detail-sidebar-cat-0-en-105":84},{"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},128266,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Labor Productivity Losses Across Construction Trades - A Machine Learning Approach - Journal Article","Labor productivity is a critical concern in construction, and prior trade-specific studies have generated inconsistent results because they examine different factors. This cross-trade study systematically identifies and evaluates productivity-reducing inefficiencies across multiple construction trades using expert surveys and machine learning. XGBoost models are trained per trade, benchmarked against common algorithms, and interpreted with SHAP to determine the most influential drivers. Findings show trade sensitivity and support targeted strategies addressing employment terms, safety culture, skill development, communication, and resource quality and location.","Missouri University of Science and Technology  \nScholars' Mine  \n\n| Civil, Architectural and Environmental\u003Cbr>Engineering Faculty Research & Creative Works | Civil, Architectural and Environmental Engineering |\n| --- | --- |\n| 01 Sep 2025\u003Cbr>Labor Productivity Losses Across Construction Trades: A Machine Learning Approach\u003Cbr>Tamima Elbashbishy\u003Cbr>Islam H. El-Adaway\u003Cbr>Missouri University of Science and Technology, [eladaway@mst.edu](eladaway@mst.edu)\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/civarc_enveng_facwork](https://scholarsmine.mst.edu/civarc_enveng_facwork)\u003Cbr> Part of the Construction Engineering and Management Commons |  |\n\nRecommended Citation  \nT. Elbashbishy and I. H. El-Adaway, \"Labor Productivity Losses Across Construction Trades: A Machine Learning Approach,\" Journal of Management in Engineering, vol. 41, no. 5, article no. 04025043, American Society of Civil Engineers, Sep 2025.  \nThe definitive version is available at [https://doi.org/10.1061/JMENEA.MEENG-6671](https://doi.org/10.1061/JMENEA.MEENG-6671)  \n[This Article-Journal is brought to you for free and open access by Scholars](This Article-Journal is brought to you for free and open access by Scholars)' Mine. It has been accepted for inclusion in Civil, Architectural and Environmental Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nDownloaded on 07/30/25from ascelibrary .org by Missouri University of Science and Technology . Copyright ASCE . For personal use only; all rights reserved .  \nLabor Productivity Losses across Construction Trades:  \nA Machine Learning Approach  \nTamima Elbashbishy, S. M.ASCE 1 ; and Islam H. El-adaway, F.ASCE 2  \nAbstract: Labor productivity is a major concern in the construction industry. Existing research on construction labor productivity (CLP) within specific trades has produced inconsistent findings due to differences in the factors analyzed. This lack of consistency makes it difficult to identify the most critical drivers of productivity losses across trades. To address this gap, this study adopts a cross-trade analytical approach to systematically identify and evaluate the inefficiencies impacting labor productivity in multiple construction trades. Specifically, the study (1) identified common organizational and project-level inefficiencies that influence labor performance; (2) conducted an expert-based survey to measure the frequency and perceived impact of these inefficiencies across key trades; (3) developed a series of extreme gradient boosting (XGBoost) models—one for each trade—to explore the relationship between specific inefficiencies and labor productivity losses; and (4) utilized Shapley additive explanations (SHAP) to interpret the models and identify the most influential productivity-reducing factors. The performance of the XGBoost models was benchmarked against four widely used machine learning algorithms: artificial neural networks (ANN), decision trees (DT), random forest (RF), and gradient-boosted decision trees (GBDT), confirming the robustness of the chosen approach. The results reveal that productivity is trade-sensitive, with different trades facing distinct challenges. For instance, the most critical factors for concreting were “decrease in the proportion of direct work” and “lack of a labor employment system,” whereas ironworking was most affected by “drawing errors/lack of drawings” and “high turnover rate.” Based on these findings, targeted strategies were proposed under five core themes: (1) labor employment terms,(2) safety culture,(3) skill development,(4) communication, and (5) quality and location of resources. Ultimately, this study provides both trade-specific insights and a holis","cbCaivlwZcnM0D8Q","https://ap.wps.com/l/cbCaivlwZcnM0D8Q","pdf",2288115,1,20,"English","en",105,"# Abstract\n# Introduction\n## Background: labor productivity importance\n## Motivation: skilled labor shortages","[{\"question\":\"What targeted strategy themes are proposed based on the findings?\",\"answer\":\"The proposed strategies fall under five core themes: labor employment terms, safety culture, skill development, communication, and the quality and location of resources.\"}]","Labor Productivity Losses Across Construction Trades - A Machine Learning Approach - Journal Article | PDF",1785946322,50,{"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":79,"head_meta":81,"extra_data":83,"updated_unix":28},"labor-productivity-losses-across-construction-trades-a-machine-learning-approach-journal-article","",{"@graph":36,"@context":78},[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/labor-productivity-losses-across-construction-trades-a-machine-learning-approach-journal-article/128266/",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","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"What targeted strategy themes are proposed based on the findings?","Question",{"text":76,"@type":77},"The proposed strategies fall under five core themes: labor employment terms, safety culture, skill development, communication, and the quality and location of resources.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,90,94,98,103,107,112,115,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":46,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":29,"slug":106},6,"Technology","technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":21,"slug":118},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":21,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":99,"slug":129},19,"General","general"]