Authors
1
Department of Civil Engineering, Mohan Babu University (SVEC), Tirupati 517102, Andhra Pradesh, India
2
Department of Civil Engineering, National Institute of Technology Jamshedpur, Jharkhand, India-831014
3
Department of Civil Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand-248002, India.
Abstract
Geopolymers represent a cutting-edge class of inorganic materials that provide a sustainable substitute for conventional cement and concrete. Through meticulous combinations and ratios of elements like fly ash (FA), silica fume, ground granulated blast slag (GGBS), alkaline solutions, aggregates, superplasticizers, and fibers, geopolymer concrete mixes are generated as part of the experimental program. The investigation concentrates on the prediction of the 28-day compressive strength, a pivotal parameter in assessing concrete performance. In total, the dataset employed comprises 96 data points, two advanced techniques, namely Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are harnessed for this research. The ANN demonstrates an R2 value of 0.992 on the training dataset, indicating its capacity to elucidate around 99.2% of the variability. On the other hand, SVR boasts an R2 value of 0.995, signifying an ability to account for about 99.5% of the variance. When applied to the testing data, the ANN achieves an R2 of 0.96, while SVR attains an R2 of 0.99. This study suggests that SVR exhibits slightly superior performance in terms of elucidating variance within the testing dataset.
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