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The Use of Genetic Programming and Regression Analysis for Modeling the Modulus of Elasticity of NSC and HSC

  • Research Article - Civil Engineering
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Abstract

Artificial intelligence has recently drawn the attention of explorers to predict the physical, chemical and mechanical properties of normal-strength concrete (NSC) and high-strength concrete (HSC). This study presents gene expression programming (GEP) and regression analysis (RA) for modeling the modulus of elasticity \({(E_{\rm c})}\) from the compressive strength \({(f_{\rm c})}\) values of NSC and HSC. In order to create the models, experimental results of NSC and HSC are collected from the published literature. The evaluated results by training, testing and checking of the GEP and RA models are compared with the results obtained from the experimental studies, the formulations presented by some national building codes and the formulations proposed by some authors available in the literature. These comparisons and statistic results show that GEP and RA models are very effective methods for calculating the \({E_{\rm c}}\) from \({f_{\rm c}}\) of NSC and HSC.

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Authors and Affiliations

  1. Department of Civil Engineering, Niğde University, 51245, Niğde, Turkey

    Mustafa Sarıdemir & Metin Hakan Severcan

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  1. Mustafa Sarıdemir
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  2. Metin Hakan Severcan
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Correspondence to Mustafa Sarıdemir.

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Sarıdemir, M., Severcan, M.H. The Use of Genetic Programming and Regression Analysis for Modeling the Modulus of Elasticity of NSC and HSC. Arab J Sci Eng 41, 3959–3967 (2016). https://doi.org/10.1007/s13369-016-2043-4

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  • DOI: https://doi.org/10.1007/s13369-016-2043-4

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