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Application of artificial neural networks for prediction of microbial concrete compressive strength

Application of artificial neural networks for prediction of microbial concrete compressive strength This paper aims to focus on the effect of calcium lactate and bacillus subtilis bacteria on concrete performance. The study also aims for predicting the compressive strength of microbial concrete using Artificial Neural Networks (ANN), containing different proportions of calcium lactate and Bacillus subtilis bacteria. The strength of concrete was examined for a curing period of 7, 28 and 56 days, resulting in a total of 60 datasets. Using the obtained data ANN has been trained with three input parameters (dosage of calcium lactate, concentration of bacteria, and age of the specimen) with compressive strength being output parameter. The ANN predicted values shows a good correlation with the experimentally obtained data. Results shows, the accumulation of bacteria and calcium lactate as nutrient source enhances the compressive strength and microbial concrete capable to heal the cracks itself. There is a maximum of 20% improvement in compressive strength of concrete by the addition of 105 cfu/ml concentrations of bacteria and 0.5% of calcium lactate in comparison with control concrete. The results also prove ANN to be an efficient model for prediction of the strength of microbial concrete with different concentrations of bacillus subtilis bacteria and calcium lactate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Building Pathology and Rehabilitation Springer Journals

Application of artificial neural networks for prediction of microbial concrete compressive strength

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References (29)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
ISSN
2365-3159
eISSN
2365-3167
DOI
10.1007/s41024-021-00140-3
Publisher site
See Article on Publisher Site

Abstract

This paper aims to focus on the effect of calcium lactate and bacillus subtilis bacteria on concrete performance. The study also aims for predicting the compressive strength of microbial concrete using Artificial Neural Networks (ANN), containing different proportions of calcium lactate and Bacillus subtilis bacteria. The strength of concrete was examined for a curing period of 7, 28 and 56 days, resulting in a total of 60 datasets. Using the obtained data ANN has been trained with three input parameters (dosage of calcium lactate, concentration of bacteria, and age of the specimen) with compressive strength being output parameter. The ANN predicted values shows a good correlation with the experimentally obtained data. Results shows, the accumulation of bacteria and calcium lactate as nutrient source enhances the compressive strength and microbial concrete capable to heal the cracks itself. There is a maximum of 20% improvement in compressive strength of concrete by the addition of 105 cfu/ml concentrations of bacteria and 0.5% of calcium lactate in comparison with control concrete. The results also prove ANN to be an efficient model for prediction of the strength of microbial concrete with different concentrations of bacillus subtilis bacteria and calcium lactate.

Journal

Journal of Building Pathology and Rehabilitation – Springer Journals

Published: Dec 1, 2022

Keywords: Compressive strength; Artificial neural networks; Bacillus subtilis; Calcium lactate; Self-healing of cracks

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