Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches.

ABSTRACT: The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na₂SiO₃/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods,

SUBMITTER: Amin MN 

PROVIDER: S-EPMC9147713 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature