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

Sakshi Aneja,Doo-Yeol Yoo,Ashutosh Sharma,Rishi Gupta

doi:10.3390/ma14071729

Sakshi Aneja, Doo-Yeol Yoo + Show 2 more

Open Access

https://doi.org/10.3390/ma14071729

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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.

Highlights

With a focus on decarbonization, different ways of reducing greenhouse gas emissions are being constantly explored [1]
The results indicate that Geopolymer concrete (GPC)-1 with precursor as fly ash alone exhibits the highest compressive strength which further increases with SS/sodium hydroxides (SH) ratio and the SH concentration
The training data is presented to the network in order to predict output compressive strength closer to target compressive strength, validation data measured network generalization to keep a check on training, and testing data measured network’s performance during and after training

Summary

Introduction

With a focus on decarbonization, different ways of reducing greenhouse gas emissions are being constantly explored [1]. The construction industry typically requires a huge amount of energy for its products and services and, is tagged as a carbonintensive sector. In the entire spectrum of the construction industry, the production of cement alone produces the largest amount of carbon dioxide and is the second largest source of CO2 emission worldwide. In this regard, geopolymer concrete offers a potential solution to completely overtake the role of cement in the construction industry. By using different industrial waste materials, two problems, viz, (1) high demand for cement,

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Conclusion