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Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. Soft computing approaches have been shown to optimize and reduce uncertainties, and therefore in this paper, we aim to address these challenges by employing artificial neural network (ANN) models optimized using the grey wolf optimizer (GWO) algorithm. The optimized model proved to be more accurate than genetic algorithms and multiple linear regression models. The results indicate that the four most influential parameters on the compressive strength of SCC are the cement content, ground granulated blast furnace slag, rice husk ash, and fly ash.<\/jats:p>","DOI":"10.1155\/2022\/9887803","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T16:20:13Z","timestamp":1645114813000},"page":"1-17","source":"Crossref","is-referenced-by-count":10,"title":["Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4018-7941","authenticated-orcid":true,"given":"Amir","family":"Andalib","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Kish International Branch, Islamic Azad University, Kish Island, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9411-9413","authenticated-orcid":true,"given":"Babak","family":"Aminnejad","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7320-3843","authenticated-orcid":true,"given":"Alireza","family":"Lork","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Safadasht Branch, Islamic Azad University, Tehran, Iran"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3007-7"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.14359\/10729"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2018.07.020"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2016.11.098"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1186\/s40069-018-0246-7"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2016.05.034"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2011.07.028"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2019.03.189"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s11771-019-4243-z"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.12989\/cac.2018.22.4.355"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04663-2"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.12989\/cac.2019.24.4.329"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2008.01.014"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2011.05.016"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2010.11.108"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1080\/19648189.2016.1246693"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s12205-014-0524-0"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1007\/s11709-018-0489-z"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.12989\/cac.2019.24.2.137"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2013.12.007"},{"key":"21","first-page":"23","article-title":"Grey wolf optimizer-based ANN to predict compressive strength of AFRP-confined concrete cylinders","volume":"3","author":"A. 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