Predicting the Compressive Strength of Concrete Containing Fly Ash and Rice Husk Ash Using ANN and GEP Models

Mohammed Najeeb Al-Hashem; Muhammad Nasir Amin; Muhammad Raheel; Kaffayatullah Khan; Hassan Ali Alkadhim; Muhammad Imran; Shahid Ullah; Mudassir Iqbal

doi:10.3390/ma15217713

Predicting the Compressive Strength of Concrete Containing Fly Ash and Rice Husk Ash Using ANN and GEP Models

Materials (Basel). 2022 Nov 2;15(21):7713. doi: 10.3390/ma15217713.

Authors

Mohammed Najeeb Al-Hashem¹, Muhammad Nasir Amin¹, Muhammad Raheel^{2

3}, Kaffayatullah Khan¹, Hassan Ali Alkadhim¹, Muhammad Imran⁴, Shahid Ullah², Mudassir Iqbal²

Affiliations

¹ Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
² Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan.
³ Department of Civil Engineering, University of Engineering and Technology, Mardan 23200, Pakistan.
⁴ School of Civil and Environmental Engineering (SCEE), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan.

Abstract

Climate change has become trending news due to its serious impacts on Earth. Initiatives are being taken to lessen the impact of climate change and mitigate it. Among the different initiatives, researchers are aiming to find suitable alternatives for cement. This study is a humble effort to effectively utilize industrial- and agricultural-waste-based pozzolanic materials in concrete to make it economical and environmentally friendly. For this purpose, a ternary blend of binders (i.e., cement, fly ash, and rice husk ash) was employed in concrete. Different variables such as the quantity of different binders, fine and coarse aggregates, water, superplasticizer, and the age of the samples were considered to study their influence on the compressive strength of the ternary blended concrete using gene expression programming (GEP) and artificial neural networking (ANN). The performance of these two models was evaluated using R², RMSE, and a comparison of regression slopes. It was observed that the GEP model with 100 chromosomes, a head size of 10, and five genes resulted in an optimum GEP model, as apparent from its high R² value of 0.80 and 0.70 in the TR and TS phase, respectively. However, the ANN model performed better than the GEP model, as evident from its higher R² value of 0.94 and 0.88 in the TR and TS phase, respectively. Similarly, lower values of RMSE and MAE were observed for the ANN model in comparison to the GEP model. The regression slope analysis revealed that the predicted values obtained from the ANN model were in good agreement with the experimental values, as shown by its higher R² value (0.89) compared with that of the GEP model (R² = 0.80). Subsequently, parametric analysis of the ANN model revealed that the addition of pozzolanic materials enhanced the compressive strength of the ternary blended concrete samples. Additionally, we observed that the compressive strength of the ternary blended concrete samples increased rapidly within the first 28 days of casting.

Keywords: ANN; GEP; compressive strength; fly ash; parametric and sensitivity analyses; rice husk ash.

Grants and funding

GRANT1638/King Faisal University