Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning.

ABSTRACT: Portland cement concrete (PCC) is a major contributor to human-made CO2 emissions. To address this environmental impact, fly ash geopolymer concrete (FAGC) has emerged as a promising low-carbon alternative. This study establishes a robust compressive strength prediction model for FAGC and develops an optimal mixture design method to achieve target compressive strength with minimal CO2 emissions. To develop robust prediction models, comprehensive factors, including fly ash characteristics, mixture proportions, curing parameters, and specimen types, are considered, a large dataset comprising 1136 observations is created, and polynomial regression, genetic programming, and ensemble learning are employed. The ensemble learning model shows superior accuracy and generalization ability with an RM

SUBMITTER: Jiang P 

PROVIDER: S-EPMC11392388 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature