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

dc.contributor.authorAneja, Sakshi
dc.contributor.authorSharma, Ashutosh
dc.contributor.authorGupta, Rishi
dc.contributor.authorYoo, Doo-Yeol
dc.date.accessioned2021-04-08T21:28:22Z
dc.date.available2021-04-08T21:28:22Z
dc.date.copyright2021en_US
dc.date.issued2021
dc.description.abstractGeopolymer 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.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.citationAneja, 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.urihttps://doi.org/10.3390/ma14071729
dc.identifier.urihttp://hdl.handle.net/1828/12835
dc.language.isoenen_US
dc.publisherMaterialsen_US
dc.subjectgeopolymer concreteen_US
dc.subjectfly-ashen_US
dc.subjectbottom-ashen_US
dc.subjectneural networken_US
dc.subjectsustainabilityen_US
dc.subjectindustrial waste managementen_US
dc.titleBayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concreteen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Aneja_Sakshi_Materials_2021.pdf
Size:
3.69 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2 KB
Format:
Item-specific license agreed upon to submission
Description: