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Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites

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Abstract

This article introduces an adaptive network-based fuzzy inference system (ANFIS) model and two linear and nonlinear regression models to predict the compressive strength of geopolymer composites. Geopolymers are highly complex materials which involve many variables which make modeling its properties very difficult. There is no systematic approach in the mix design for geopolymers. The amounts of silica modulus, Na2O content, w/b ratios, and curing time have a great influence on the compressive strength. In this study, by developing and comparing parametric linear and nonlinear regressions and ANFIS models, we dealt with predicting the compressive strength of geopolymer composites for possible use in mix-design framework considering the mentioned complexities. ANFIS model developed by generalized bell-shaped membership function was recognized the best approach, and the prediction results of linear and nonlinear regression models as empirical methods showed the weakness of these models comparing ANFIS model.

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Authors and Affiliations

  1. Department of Civil Engineering, Bingol University, Bingol, Turkey

    Mehrzad Mohabbi Yadollahi & Ahmet Benli

  2. Department of Civil Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

    Ramazan Demirboga

Authors
  1. Mehrzad Mohabbi Yadollahi
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  2. Ahmet Benli
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  3. Ramazan Demirboga
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Correspondence to Ahmet Benli.

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Yadollahi, M.M., Benli, A. & Demirboga, R. Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites. Neural Comput & Applic 28, 1453–1461 (2017). https://doi.org/10.1007/s00521-015-2159-6

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  • DOI: https://doi.org/10.1007/s00521-015-2159-6

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