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Computers and Concrete Volume 24, Number 2, August 2019 , pages 137-150 DOI: https://doi.org/10.12989/cac.2019.24.2.137 |
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Prediction of the compressive strength of self-compacting concrete using surrogate models |
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Panagiotis G. Asteris, Ali Ashrafian and Mohammad Rezaie-Balf
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| Abstract | ||
| In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of selfcompacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture. | ||
| Key Words | ||
| artificial intelligence models; compressive strength; multivariate adaptive regression splines (MARS); M5P model tree; self-compacting concrete; surrogate models | ||
| Address | ||
| Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece Ali Ashrafian: Department of Civil Engineering, Tabari University of Babol, P.O. Box 47139-75689, Babol, Iran Mohammad Rezaie-Balf: Department of Civil Engineering, Graduate University of Advanced Technology-Kerman, P.O. Box 76315-116, Kerman, Iran | ||