Abstract

Self-healing concrete is described as the capability of material to repair their cracks independently. Cracks in concrete are well-known circumstance because of their short tensile strength. Many researchers carried out their research on self-healing concrete using different classification and clustering methods. But the temperature variation and pH variation were not minimized. In order to address these problems, a Multivariate Logistic Regressed Chi-Square Deep Recurrent Neural Network based Self-Healing (MLRCSDRNN-SH) Method is introduced. The main aim of MLRCSDRNN-SH method is to improve building structures strength through inducing the micro-bacteria in concrete. Multiple Logistic Regressed Chi-Square Deep Recurrent Neural Network (MLRCSDRNN) is used to revise bacteria’s stress-strain behaviour towards enhanced material strength in the MLRCSDRNN-SH approach. Initially, the bacteria selection is carried out in alkaline environment like Bacillus subtilis, E. coli and Pseudomonas sps. The data sample is given to the input layer. The input layer transmits sample to the hidden layer 1. The regression analysis is carried out between the multiple independent variables (i.e., parameters) using multivariate logistic function for improving the building structure strength. The regressed value is transmitted to the hidden layer 2. The pearson chi-squared independence hypothesis is performed to identify the probability of crack self-healing property for increasing the building structure strength. When probability value is higher, then the building structure strength is high. Otherwise, the output of second hidden layer is feedback to the input of hidden layer 1. The mixture with higher strength of building structure is sent to the output layer. Several specimens have different sizes used by various researchers for bacterial material study in comparison with the concrete. Depending on experimental results, compressive strength restoration proved higher self-healing ability of the concrete.

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