Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete
Date
2021
Authors
Aneja, Sakshi
Sharma, Ashutosh
Gupta, Rishi
Yoo, Doo-Yeol
Journal Title
Journal ISSN
Volume Title
Publisher
Materials
Abstract
Geopolymer concrete (GPC) offers a potential solution for sustainable construction by
utilizing waste materials. However, the production and testing procedures for GPC are quite cumbersome
and expensive, which can slow down the development of mix design and the implementation
of GPC. The basic characteristics of GPC depend on numerous factors such as type of precursor
material, type of alkali activators and their concentration, and liquid to solid (precursor material)
ratio. To optimize time and cost, Artificial Neural Network (ANN) can be a lucrative technique
for exploring and predicting GPC characteristics. In this study, the compressive strength of fly-ash
based GPC with bottom ash as a replacement of fine aggregates, as well as fly ash, is predicted using
a machine learning-based ANN model. The data inputs are taken from the literature as well as
in-house lab scale testing of GPC. The specifications of GPC specimens act as input features of the
ANN model to predict compressive strength as the output, while minimizing error. Fourteen ANN
models are designed which differ in backpropagation training algorithm, number of hidden layers,
and neurons in each layer. The performance analysis and comparison of these models in terms of
mean squared error (MSE) and coefficient of correlation (R) resulted in a Bayesian regularized ANN
(BRANN) model for effective prediction of compressive strength of fly-ash and bottom-ash based
geopolymer concrete.
Description
Keywords
geopolymer concrete, fly-ash, bottom-ash, neural network, sustainability, industrial waste management
Citation
Aneja, S., Sharma, A., Gupta, R., & Yoo, D. (2021). Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete. Materials, 14(7), 1-17. https://doi.org/10.3390/ma14071729.