| Summary: | One of the largest sources of greenhouse gas (GHG) emissions is the construction concrete industry which has alone 50% of the world's emissions. One possible remedy to mitigate the effect of environmental issues is the use of waste and recycled material in concrete. Today, immense agricultural waste is being used as a substitute for cement in the production of sustainable concrete. Therefore, this study is aimed to predict and develop an empirical formula of the compressive strength of rice husk ash (RHA) concrete using machine learning algorithms. Methods employed in this study includes gene expression programming (GEP) and Random Forest Regression (RFR). A reliable database of 192 data points was employed for developing the models. Most influential variables including age, cement, rice husk ash, water, super plasticizer, and aggregate were employed as input parameters in the development of RHA-based concrete models. Evaluation of models was performed using different statistical parameters. These statistical measures include mean absolute error (MAE), coefficient of determination (R2), performance index (ρ), root man square error (RMSE), relative squared error (RSE) and relative root mean square (RRMSE). The GEP model outperforms the RFR ensemble model in terms of robustness, with a greater correlation of R2 = 0.96 compared to RFR's R2 = 0.91. Ensemble modeling showed an enhancement of 1.62 percent for RFR compressive strength model when compared with individual RFR compressive strength model as illustrated by statistical parameters. Moreover, GEP model shows an enhancement of 37.33 percent in average error with an average error 2.35 MPa as compared to RFR model with average error of about 3.75 MPa. Cross validation was used as external check to avoid overfitting issues of the models and confirm the generalized model output. Parametric analysis was performed to determine the impact of the input parameters on the output. Cement and age were shown to have a substantial impact on the compressive strength of RHA concrete using sensitivity analysis.
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