Forecasting the Properties of Concrete Employing Experimental Data Using Machine Learning Algorithms

This study has been undertaken to investigate the compressive strength, Flexural strength and split tensile strength of concrete of grade M30 and M40 in present investigation by laboratory and predicting the strength through Machine learning technique. Flexural strength and split tensile strength which establishes the concrete class, is one of the most crucial characteristics of concrete. The primary characteristic of concrete's durability and safety is its predictable compressive strength, Flexural strength and split tensile strength which is necessary for the use of concrete structures. To explore the time-dependent behavior of concrete strength, considering factors such as curing duration and age. Main aim is to compare the performance of different regression methods, such as linear regression, ridge regression, lasso regression, or machine learning approaches like Random Forest and evaluate their suitability for concrete strength prediction and to find the accuracy of algorithms and regression.