Predicting Compressive Strength of Concrete Using Histogram-Based Gradient Boosting Approach for Rapid Design of Mixtures

Applications of machine learning techniques in concrete properties' prediction have great interest to many researchers worldwide. Indeed, some of the most common machine learning methods are those based on adopting boosting algorithms. A new approach, histogram-based gradient boosting, was recently introduced to the literature. It is a technique that buckets continuous feature values into discrete bins to speed up the computations and reduce memory usage. Previous studies have discussed its efficiency in various scientific disciplines to save computational time and memory. However, the algorithm's accuracy is still unclear, and its application in concrete properties estimation has not yet been considered. This paper is devoted to evaluating the capability of histogram-based gradient boosting in predicting concrete's compressive strength and comparing its accuracy to other boosting methods. Generally, the results of the study have shown that the histogram-based gradient boosting approach is capable of achieving reliable prediction of concrete compressive strength. Additionally, it showed the effects of each model's parameters on the accuracy of the estimation.