Chopped and minibars reinforced high-performance concrete: machine learning prediction of mechanical properties

A novel form of high-tech concrete known basalt fiber-reinforced high-performance concrete (BFHPC) has been developed using traditional materials that require extra admixtures to improve its mechanical properties. Machine learning (ML) techniques provide a more flexible and economical way to predict the mechanical property of chopped and minibar basalt fiber-reinforced high-performance concrete based on material properties and processing parameters, enabling durable and environmentally friendly construction. Predicting the mechanical properties of BFHPC precisely is crucial since it reduces design costs and time, and it also minimizes material waste from several mixing experiments. In this study, the compressive strength and flexural strength are predicted via different types of machine learning models. Experiments carried out in the laboratory under standard controlled settings at 7, 14, and 28-day curing periods yielded sample data for analysis and model development. The mechanical characteristics of BFHPC have been predicted using a combination of decision tree, partial least squares, lasso, rigid, random forest regressor, K Neighbours, and linear regressions. According to the results, all types of regression have the best results except KNeighbors Regressor, Random Forest Regressor, and Lasso Regression, with a correlation coefficient of R2 93%. Each model's performance and application are examined thoroughly, leading to the identification of useful suggestions, existing knowledge gaps, and areas in need of further study.