Comparative Study of Mathematical Models for the Strength of Bacterial Concrete Using Multiple Regression Analysis and Artificial Neural Network
| International Journal of Civil Engineering |
| © 2023 by SSRG - IJCE Journal |
| Volume 10 Issue 12 |
| Year of Publication : 2023 |
| Authors : C.M. Meera, Subha Vishnudas |
10.14445/23488352/IJCE-V10I12P101
How to Cite?
C.M. Meera, Subha Vishnudas, "Comparative Study of Mathematical Models for the Strength of Bacterial Concrete Using Multiple Regression Analysis and Artificial Neural Network," SSRG International Journal of Civil Engineering, vol. 10, no. 12, pp. 1-8, 2023. Crossref, https://doi.org/10.14445/23488352/IJCE-V10I12P101
Abstract:
This study advocates the adoption of Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) techniques for predicting concrete behaviour. It underscores these statistical prediction tools’ expeditious and reliable nature, offering valuable insights for subsequent mass concreting endeavours through a swift assessment of concrete behaviour. The research endeavours to construct predictive models using a raw dataset curated from specific studies conducted by various researchers, employing Multiple Regression Analysis tools. In addition to MRA, the study incorporates Artificial Neural Network techniques to train the raw dataset. Subsequently, it utilizes various regression analyses to predict a new model derived from this updated dataset. The study’s primary objective is to discern the combined influence of bacterial addition and concrete grade on the compressive strength of concrete. This holistic approach aims to advance our understanding of the intricate interactions among these variables, contributing to the enhancement of predictive models for concrete behaviour in the context of mass concreting projects. The study also contributes to exploring new dimensions in the research field of bacterial concrete.
Keywords:
Artificial Neural Network, Bacillus subtilis, Bacterial concrete, Behavior prediction, Multiple Regression Analysis.
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