Determining the mix design method for normal strength concrete using machine learning

Abstract There exist many empirical data-based methods that facilitate the process of concrete mix design. The output of mix design methods are the proportions of concrete constituents that when mixed together produce hardened concrete, taking into account the required strength, workability and durability requirements. Based only on the proportions of the mix, it can be challenging to determine the designing method. Therefore, in this work, computer-generated data was used to train a simple machine learning model to determine the method by which a normal strength concrete mix was designed. The developed machine leaning model only requires knowledge of the mix’s proportions, i.e., the amounts of cement, water, sand, and gravel to accurately determine the method by which the mix was designed. It was found that a simple machine learning model (decision tree) was able to determine the mix design method with high accuracy. Moreover, via principal components analyses, and other similar techniques, it was found that amount of cement is the best predictor of the mix design method. Findings of this work provide a method for determining mix design methods and promote the use of machine learning in the field of civil engineering.