Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites
Feng Bin,
Shahab Hosseini,
Jie Chen
et al.
Abstract:This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present… Show more
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