Additive-based concrete evaluation using categorical boosting method

Abstract

This study utilized several coupled approaches to create powerful algorithms for forecasting the compressive strength (Cs) of concretes that include metakaolin (MK) and fly ash (FA). For this purpose, three various methods were considered, named random forests (RF), Categorical boosting model (CB), and extreme gradient boosting (XGB) by considering the seven most influential input variables. It was tried to divide the concrete components to binder value (B) to achieve the non-dimensional input variables. Herein, the cutting-edge Tasmanian devil Optimization (TDO) algorithm was linked with RF, XGB, and CB for the purpose of determining the optimal values of hyperparameters (named TD - CB, TD - RF, and TD - XG). It is worth mentioning that developing the mentioned algorithms optimized with TD to estimate the mechanical properties of the concrete containing several important admixtures can be recognized as this study’s contribution to practical applications. The findings indicate that the algorithms possess a notable capacity to precisely forecast the Cs of concrete, which includes MK and FA, with R2 bigger than roughly 0.97. The lower value of OBJ comprehensive index belonged to the TD - CB at 1.5762, followed by TD - XG at 1.9943 and then 2.3317 related to TD - RF with almost 70% reduction. The sensitivity analysis demonstrated that the prediction of Cs is highly influenced by all input parameters, which are higher than 0.8659, but a higher influence from MK/B at 0.9548.