Mathematical model and machine learning techniques to predict the compressive strength of groundnut shell ash blended sandcrete

Abstract

This study uses machine learning methodologies to introduce predictive models for the compressive strength of sandcrete mixed with groundnut shell ash. The models were developed utilizing 140 data sets acquired from the published articles. The datasets contained several input variables: aggregate to binder ratio, peanut shell ash concentration and curing time. The output feature is the compressive strength of the sandcrete. Four mathematical and machine learning models were used to predict the compressive strength of peanut shell ash-blended sandcrete. Based on the analysis of several models, it was shown that the boosted decision tree model outperformed others in predicting compressive strength. The sensitivity analysis outcomes of the boosted decision tree model show that the aggregate-to-binder ratio is the most significant factor in determining compressive strength. This study systematically evaluates the compressive strength of peanut shell ash blended sandcrete and contributes to the current understanding and practical application in this field.