Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2021Design and formulation of microbially induced self-healing concrete for building structure strength enhancement4citations

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Sundravel, K. Vijaya
1 / 1 shared
Ramesh, S.
1 / 12 shared
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2021

Co-Authors (by relevance)

  • Sundravel, K. Vijaya
  • Ramesh, S.
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article

Design and formulation of microbially induced self-healing concrete for building structure strength enhancement

  • Jegatheeswaran, D.
  • Sundravel, K. Vijaya
  • Ramesh, S.
Abstract

<jats:p>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 classificationand 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 methodis 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 <jats:italic>Bacillus subtilis, E. coli</jats:italic> and <jats:italic>Pseudomonas sps</jats:italic>. 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 themultiple 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 crackself-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 structureis 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.</jats:p>

Topics
  • impedance spectroscopy
  • crack
  • strength
  • stress-strain behavior
  • tensile strength
  • clustering