Using a machine learning method with novel optimization algorithms in estimating compressive strength rates in ultra-high performance concrete

Abstract

To effectively incorporate Ultra-High-Performance Concrete (UHPC) into a construction project, it is essential to inquire into its composition and content to determine the concrete's pertinent Compressive Strength (CS). Determining the relationships between ingredients may necessitate additional expenditure and energy expenditure. The present research endeavor aimed to replicate the CS behavior of UHPC via environmentally sustainable constituents. In this context, the present study employed Support Vector Regression (SVR) as a machine learning approach, coupled with Biogeography-Based Optimization (BBO) and Flow Direction Algorithm (FDA), to construct an accurate model of concrete compressive strength (CS). Coupled machine learning models with optimizers can be a powerful tool for predicting the mechanical properties of UHPC and other complex materials. By improving accuracy and efficiency, these models can help accelerate the development of new UHPC formulations with desired mechanical properties, optimize manufacturing processes, and reduce the associated costs. The modeling of the CS values utilized a total of eight components. In general, the presented study indicated that SVR-FDA had obtained a high correlation and low errors compared to SVR-BBO, which can be concluded that the hybrid machine learning method saves time and energy against laboratory experiments.