{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T04:38:53Z","timestamp":1768711133490,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Eng"],"abstract":"<jats:p>Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms\u2014Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)\u2014were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an R2 of 0.901 and RMSE of 19.94 on the training set and an R2 of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments.<\/jats:p>","DOI":"10.3390\/eng6090219","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T13:01:13Z","timestamp":1756818073000},"page":"219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable Machine Learning-Based Estimation of Labor Productivity in Rebar-Fixing Tasks"],"prefix":"10.3390","volume":"6","author":[{"given":"Farah Faaq","family":"Taha","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 32001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0665-610X","authenticated-orcid":false,"given":"Mohammed Ali","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Construction and Building Engineering Technologies Department, Najaf Engineering Technical College, Al-Furat-Al-Awsat Technical University, Najaf 54003, Iraq"}]},{"given":"Saja Hadi Raheem","family":"Aldhamad","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, Al-Iraqia University, Baghdad 10081, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0680-8540","authenticated-orcid":false,"given":"Hamza","family":"Imran","sequence":"additional","affiliation":[{"name":"Department of Environmental Science, College of Energy and Environmental Science, Alkarkh University of Science, Baghdad 10081, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0484-350X","authenticated-orcid":false,"given":"Lu\u00eds Filipe Almeida","family":"Bernardo","sequence":"additional","affiliation":[{"name":"GeoBioTec, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"},{"name":"Department of Civil Engineering and Architecture, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3865-490X","authenticated-orcid":false,"given":"Miguel C. S.","family":"Nepomuceno","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Architecture, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"04017045","DOI":"10.1061\/(ASCE)CO.1943-7862.0001340","article-title":"Engineering approach using ANN to improve and predict construction labor productivity under different influences","volume":"143","author":"Aziz","year":"2017","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.asej.2018.10.010","article-title":"Evolution of studies in construction productivity: A systematic literature review (2006\u20132017)","volume":"10","author":"Dixit","year":"2019","journal-title":"Ain Shams Eng. 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