Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.

ABSTRACT: The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensi

SUBMITTER: Javed MF 

PROVIDER: S-EPMC11199582 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature