{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T10:59:06Z","timestamp":1774263546498,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["NSERC IRCPJ 428226\u201315"],"award-info":[{"award-number":["NSERC IRCPJ 428226\u201315"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper presents a novel approach, using hybrid feature selection (HFS), machine learning (ML), and particle swarm optimization (PSO) to predict and optimize construction labor productivity (CLP). HFS selects factors that are most predictive of CLP to reduce the complexity of CLP data. Selected factors are used as inputs for four ML models for CLP prediction. The study results showed that random forest (RF) obtains better performance in mapping the relationship between CLP and selected factors affecting CLP, compared with the other three models. Finally, the integration of RF and PSO is developed to identify the maximum CLP value and the optimum value of each selected factor. This paper introduces a new hybrid model named HFS-RF-PSO that addresses the main limitation of existing CLP prediction studies, which is the lack of capacity to optimize CLP and its most predictive factors with respect to a construction company\u2019s preferences, such as a targeted CLP. The major contribution of this paper is the development of the hybrid HFS-RF-PSO model as a novel approach for optimizing factors that influence CLP and identifying the maximum CLP value.<\/jats:p>","DOI":"10.3390\/a14070214","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T09:32:07Z","timestamp":1626341527000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization"],"prefix":"10.3390","volume":"14","author":[{"given":"Sara","family":"Ebrahimi","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Hole School of Construction, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3744-273X","authenticated-orcid":false,"given":"Aminah Robinson","family":"Fayek","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Hole School of Construction, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"given":"Vuppuluri","family":"Sumati","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Hole School of Construction, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.11648\/j.ajce.20140202.14","article-title":"Critical factors affecting construction labor productivity in Egypt","volume":"2","author":"Hafez","year":"2014","journal-title":"Am. 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