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dc.contributor.authorGolafshani, E.M.*
dc.contributor.authorAshour, Ashraf*
dc.date.accessioned2016-01-05T12:08:50Z
dc.date.available2016-01-05T12:08:50Z
dc.date.issued2016
dc.identifier.citationGolafshania EM and Ashour AF (2016) Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques. Automation in Construction. 64: 7–19.en_US
dc.identifier.urihttp://hdl.handle.net/10454/7628
dc.descriptionyesen_US
dc.description.abstractThis paper introduces a novel symbolic regression approach, namely biogeographical-based programming (BBP), for the prediction of elastic modulus of self-compacting concrete (SCC). The BBP model was constructed directly from a comprehensive dataset of experimental results of SCC available in the literature. For comparison purposes, another new symbolic regression model, namely artificial bee colony programming (ABCP), was also developed. Furthermore, several available formulas for predicting the elastic modulus of SCC were assessed using the collected database. The results show that the proposed BBP model provides slightly closer results to experiments than ABCP model and existing available formulas. A sensitivity analysis of BBP parameters also shows that the prediction by BBP model improves with the increase of habitat size, colony size and maximum tree depth. In addition, among all considered empirical and design code equations, Leemann and Hoffmann and ACI 318-08’s equations exhibit a reasonable performance but Persson and Felekoglu et al.’s equations are highly inaccurate for the prediction of SCC elastic modulus.en_US
dc.language.isoenen_US
dc.rights© 2016 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en_US
dc.subjectSelf- compacting concrete; Elastic modulus; Symbolic regression; Artificial bee colony programming; Biogeographical-based programmingen_US
dc.titlePrediction of self-compacting concrete elastic modulus using two symbolic regression techniquesen_US
dc.status.refereedyesen_US
dc.date.Accepted28th December 2015
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
dc.identifier.doihttps://doi.org/10.1016/j.autcon.2015.12.026
refterms.dateFOA2018-07-25T14:15:43Z


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