Prediction of Compressive Strength of Rice Husk Ash Concrete through Different Machine Learning Processes

Cement is among the major contributors to global carbon dioxide emissions. Thus, sustainable alternatives conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics be a option further research and investigation. Since experimental work required assessing its properties both time consuming complex, machine learning can used successfully predict of containing rice ash. A total 192 data points in this study assess compressive strength blended concrete. Input parameters include age, amount cement, ash, super plasticizer, water, aggregates. Four soft computing methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), linear employed research. Sensitivity analysis, parametric correlation factor (R2) evaluate obtained results. The ANN ANFIS outperformed other methods.