Application of Artificial Neural Technique for Performance Prediction of Bacterial based Concrete

Abstract

The possibility of artificial neural networks (ANNs) to forecast the performance properties of bacterial-enhanced self-healing concrete is investigated in this work.  Bacterial concrete has become a viable self-healing substitute that can greatly lower maintenance costs and lengthen structural lifetime as sustainable building materials become more and more important in civil engineering uses.  Still, improving bacterial concrete compositions is difficult because of the complicated interactions among bacterial species, nutrients, transporters, and conventional concrete ingredients.  In this work, we designed and evaluated several ANN architectures to estimate compressive strength, crack-healing efficiency, water permeability, and durability parameters of bacterial concrete mixtures.  Model training and validation used a complete dataset of 187 experimental combinations with different bacterial strains, carrier materials, and concrete compositions.  Predicting accuracy of 94.3% for compressive strength, 91.7% for fracture healing, and 89.5% for permeability decrease, the best-performing ANN model  The most important factors influencing performance were found by sensitivity analysis to be bacterial concentration, kind of calcium supply, and water-cement ratio.  This work shows that, in absence of significant laboratory testing, ANNs can be effective instruments for optimizing bacterial concrete formulas, therefore hastening the use of this sustainable building material in useful applications.