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Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

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dc.contributor.author Aneja, Sakshi
dc.contributor.author Sharma, Ashutosh
dc.contributor.author Gupta, Rishi
dc.contributor.author Yoo, Doo-Yeol
dc.date.accessioned 2021-04-08T21:28:22Z
dc.date.available 2021-04-08T21:28:22Z
dc.date.copyright 2021 en_US
dc.date.issued 2021
dc.identifier.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. en_US
dc.identifier.uri https://doi.org/10.3390/ma14071729
dc.identifier.uri http://hdl.handle.net/1828/12835
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Materials en_US
dc.subject geopolymer concrete en_US
dc.subject fly-ash en_US
dc.subject bottom-ash en_US
dc.subject neural network en_US
dc.subject sustainability en_US
dc.subject industrial waste management en_US
dc.title Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete en_US
dc.type Article en_US
dc.description.scholarlevel Faculty en_US
dc.description.reviewstatus Reviewed en_US


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