Abstract

The ultrafine fly ash (FA) is a hazardous material collected from coal productions, which has been proficiently employed for the manufacturing of geopolymer concrete (GPC). In this study, the three artificial intelligence (AI) techniques, namely, artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), and gene expression programming (GEP) are used to establish a reliable and accurate model to estimate the compressive strength (f′c) of fly ash–based geopolymer concrete (FGPC). A database of 298 instances is developed from the peer-reviewed published work. The database consists of the ten most prominent explanatory variables and f′c of FGPC as a response parameter. The statistical error checks and criteria suggested in the literature are considered for the verification of the predictive strength of the models. The statistical measures considered in this study are MAE, RSE, RMSE, RRMSE, R, and performance index (ρ). These checks verify that the ANFIS predictive model gives an outstanding performance followed by GEP and ANN predictive models. In the validation stage, the coefficient of correlation (R) for ANFIS, GEP, and ANN model is 0.9783, 0.9643, and 0.9314, respectively. All three models also fulfill the external verification criterion suggested in the literature. Generally, the GEP predictive model is ideal as it delivers a simplistic and easy mathematical equation for future use. The k-fold cross-validation (CV) of the GEP model is also conducted, which verifies the robustness of the GEP predictive model. Furthermore, the parametric study is carried via proposed GEP expression. This confirms that the GEP model accurately covers the influence of all the explanatory variables used for the prediction of f′c of FGPC. Thus, the proposed GEP equation can be used in the preliminary design of FGPC.

Highlights

  • Fly ash is the unburned residual obtained from coal production and is taken out by the gases expelled from the boiler, which is accumulated by means of mechanical or electrostatic precipitator (Rafieizonooz et al, 2016; Aprianti S, 2017; Akbar et al, 2021)

  • The ultimate results of the artificial neural network (ANN) predictive model are presented in Figure 5, which shows the slope of the regression lines for training and validation data points, that is, 0.9715 and 0.9762, respectively, displays the dispersion of absolute error for the whole dataset utilized in ANN modeling

  • The three artificial intelligence (AI) techniques, namely, ANN, adaptive neuro-fuzzy interface system (ANFIS), and gene expression programming (GEP) are used for estimating the compressive strength (f ′c) of fly ash–based geopolymer concrete (FGPC)

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Summary

INTRODUCTION

Fly ash is the unburned residual obtained from coal production and is taken out by the gases expelled from the boiler, which is accumulated by means of mechanical or electrostatic precipitator (Rafieizonooz et al, 2016; Aprianti S, 2017; Akbar et al, 2021). The f ′c of FGPC fluctuates by numerous parameters such as temperature required for curing of the sample (T), the time required for curing of the sample (t), age of the sample (A), the molarity (M) of the sodium hydroxide (NaOH) solution used, the percentage of silicon dioxide (SiO2) to the water ratio (%S/W) for preparing solution of sodium silicate (Na2SiO3), ratio between sodium silicate (Na2SiO3) solution to NaOH (NsNs/NoNo), percentage by volume of total aggregates (% AG), ratio between fine aggregate to total aggregates (F/AG), ratio between alkali to fly ash (AL/FA), percentage of plasticizer (% P), and percentage of extra addition of water (% EW) (Luhar et al, 2019; Tran et al, 2019; Van Dao et al, 2019; Wang et al, 2019b; Zhang et al, 2019, Zhang et al, 2020b; Prachasaree et al, 2020; Farooq et al, 2021). Alkaroosh et al (Alkroosh and Sarker, 2019) established a GEP-based empirical relationship for the prediction of f ′c of FGPC, based on 56 instances saved from previous study (Hardjito and Rangan, 2005) The application of this model is limited to a confined database,

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