A Study of Machine Learning Algorithms for Concrete Compressive Strength Prediction

The toughness of concrete is judged by its compressive power, and the compressive strength is typically determined using a traditional crushing test on a concrete cylinder. To achieve proper outcomes, it is recommended that one waits 28 days before testing the cylinder. Using machine learning techniques, this process can be accelerated. This paper includes a compressive strength examination of concrete as well as the development of machine learning models to predict compressive strength using machine learning methods such as Random Forest Regression, CatBoost, Light GBM, and ANN. The efficiency of different algorithms is assessed, and the model with the least RMSE (Root Mean Squared Error) is ultimately selected to forecast concrete compressive strength.