Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete

Superplasticizers (SPs), also known as naturally high-water reducers, are substances used to create high-strength concrete. Due the system’s complexity, predicting concrete’s compressive strength can be difficult. In this study, a prediction model for with SP was developed handle high-dimensional complex non-linear relationship between mixing design of and After performing statistical analysis dataset, correlation performed then 16 supervised machine learning regression techniques were used. Finally, by using Extra Trees method creating variable values, it shown that values concrete increased addition in optimal dose. The results indicate superplasticizers often reduce water content 25 35 per cent consequently resistivity 50 75 optimum amount up 12 kg cubic meter well. From one point, increase does not lead rise strength, remains constant. According findings, additive has most impact on after cement. Given scant information now available concrete-including superplasticizer, is prudent plan future studies. It conceivable investigate how impacted reduction.