Abstract:
This study uses machine-learning (ML) methodologies to introduce predictive models
for the compressive strength of sandcrete mixed with groundnut shell ash (GSA). The models
were developed utilizing 140 datasets acquired from published articles. The datasets contained
several input variables: aggregate-to-binder ratio, peanut shell ash concentration, and curing
time. The output feature was 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 analyses of several models, 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 was the most significant
factor in determining compressive strength. Overall, the boosted decision-tree model achieved
an R² of 0.965 during testing, indicating excellent predictive accuracy. Additionally, it was found
that using 10% to 30% GSA as a cement substitute optimally enhances sandcrete strength. These
findings contribute to the understanding of sustainable construction materials and support the
practical application of GSA in construction.
