Gradient Boosting Hybridized with Exponential Natural Evolution Strategies for Estimating the Strength of Geopolymer Self-Compacting Concrete

Abstract

The current global demand to minimize carbon dioxide (CO2$) emissions from Portland cement manufacturing processes has led to the use of environmentally friendly additives in cement products. The so-called green cementitious composites have become increasingly essential in the design of cementitious composite mixtures, providing the environmental compatibility of concrete as a building material. Engineers face a difficult problem in predicting the mechanical properties of green composites due to their changing nature under various circumstances. Machine learning models then emerge as surrogate models to perform this difficult task. The very design of such models has become a challenge for machine learning. This study presents a gradient boosting algorithm hybridized with Natural Exponential Evolution Strategies inspired by nature to predict the mechanical properties of geopolymeric self-compacting concrete. The hybrid model is used to evolve the parameters, automating the selection of the best set of internal parameters to estimate the strength properties of geopolymer self-compacting concrete. Results show the predictive ability superiority of machine learning models and optimization algorithms hybridization compared to manually tuned models. In addition, this approach can minimize laboratory work, potentially optimize experimental design, and reduce sample production time and associated activity burden