Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques
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2016Rights
© 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.Peer-Reviewed
yesAccepted for publication
28th December 2015
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This 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.Version
Accepted ManuscriptCitation
Golafshania EM and Ashour AF (2016) Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques. Automation in Construction. 64: 7–19.Link to Version of Record
https://doi.org/10.1016/j.autcon.2015.12.026Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.autcon.2015.12.026