Prediction of Concrete Strength Using XGBoost

Abstract

This paper aims to test the extent to which different materials affect concrete strength and to use Machine Learning to test the accuracy of different mathematical algorithms in predicting concrete strength to select the most appropriate method to predict concrete strength through a given database. There is a strong need to predict concrete strength, as cement can only show its solidity after it has solidified and been put into use. So, it is not possible to propose some samples for strength testing before each use of cement to ensure safety. Although this is indeed a method, it is too expensive and cost-intensive. Thus, it is not cost-effective to implement. In this paper, some data was found through the academic forum and analyzed using Python, the degree of correlation between different parameters for concrete strength was summarized, and different algorithms were used to compare the accuracy. Finally, XGBoost was successfully used to predict concrete strength.