{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:44:57Z","timestamp":1753886697096,"version":"3.41.2"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2013,6,19]],"date-time":"2013-06-19T00:00:00Z","timestamp":1371600000000},"content-version":"vor","delay-in-days":169,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100001868","name":"National Science Council","doi-asserted-by":"publisher","award":["NSC 98-2221-E-020-035-MY2"],"award-info":[{"award-number":["NSC 98-2221-E-020-035-MY2"]}],"id":[{"id":"10.13039\/501100001868","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Scientific World Journal"],"published-print":{"date-parts":[[2013,1]]},"abstract":"<jats:p>Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web\u2010based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision\u2010making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi\u2010cut\u2010point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web\u2010based remote access to a reliable, scientific prediction mechanism.<\/jats:p>","DOI":"10.1155\/2013\/729525","type":"journal-article","created":{"date-parts":[[2013,6,19]],"date-time":"2013-06-19T21:04:17Z","timestamp":1371675857000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Subcontractor Performance Using Web\u2010Based Evolutionary Fuzzy Neural Networks"],"prefix":"10.1155","volume":"2013","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6399-5912","authenticated-orcid":false,"given":"Chien-Ho","family":"Ko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2013,6,19]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijproman.2010.01.005"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2012.06.009"},{"key":"e_1_2_7_3_2","article-title":"Making formwork construction lean","author":"Ko C. 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