https://orcid.org/0000-0002-8038-2381
Abstract
The primary objective of this investigation is to construct a machine learning (ML) model to enhance the predictive accuracy of geopolymer concrete (GPC) compressive strength (CS) while extracting valuable insights from input variables. Considering the crucial task of optimizing hyperparameters, three efficient optimization algorithms are harnessed for this purpose: the FOX optimization algorithm (FOX), the golden jackal optimization algorithm, and the honey badger algorithm. The dataset under examination incorporates 156 experimental samples comprising 15 variables. Through a systematic exploration of population size and epoch parameters, hyperparameters are fine-tuned for the 12 XGB models. Notably, model XGB_06, which employs FOX with a population size of 20, attains the highest coefficient of determination value of 0.9662 on the testing dataset, achieving a fine equilibrium between computational efficiency and superior accuracy. This model’s excellence is further proved by its outperformance of eight well-known ML algorithms during the comparative analysis step. In addition, this study presents the results of partial dependence plots, shedding light on the intricate relationships between input variables and GP’s CS. The insights gleaned here have far-reaching implications, offering avenues for refining the predictive modeling of GPC’s CS and extending its applications within materials science and engineering.
Issue Section:
Technical Papers
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