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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Manikandan, P.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (4/4 displayed)

  • 2022An Artificial Neural Network Based Prediction of Mechanical and Durability Characteristics of Sustainable Geopolymer Composite29citations
  • 2020Pozzolanic Properties of Agro Waste Ashes for Potential Cement Replacement Predicted using ANN18citations
  • 2020Prediction of self-healing characteristics of GGBS admixed concrete using Artificial Neural Network22citations
  • 2016Micro/Nanostructure and Tribological Characteristics of Pressureless Sintered Carbon Nanotubes Reinforced Aluminium Matrix Composites30citations

Places of action

Chart of shared publication
Selija, K.
1 / 1 shared
Kumaran, G. Senthil
1 / 1 shared
Kumar, V. Prem
2 / 2 shared
Vasugi, V.
3 / 5 shared
Santhi, M. Helen
1 / 2 shared
Natrayan, L.
1 / 27 shared
Duraimurugan, S.
1 / 2 shared
Elavenil, S.
2 / 4 shared
Manikanta, C.
1 / 1 shared
Chaitanya, M.
1 / 1 shared
Le, H. R.
1 / 3 shared
Basu, S.
1 / 12 shared
Sieh, R.
1 / 1 shared
Elayaperumal, A.
1 / 1 shared
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2020
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Co-Authors (by relevance)

  • Selija, K.
  • Kumaran, G. Senthil
  • Kumar, V. Prem
  • Vasugi, V.
  • Santhi, M. Helen
  • Natrayan, L.
  • Duraimurugan, S.
  • Elavenil, S.
  • Manikanta, C.
  • Chaitanya, M.
  • Le, H. R.
  • Basu, S.
  • Sieh, R.
  • Elayaperumal, A.
OrganizationsLocationPeople

article

Prediction of self-healing characteristics of GGBS admixed concrete using Artificial Neural Network

  • Elavenil, S.
  • Manikandan, P.
  • Kumar, V. Prem
  • Vasugi, V.
  • Chaitanya, M.
Abstract

<jats:title>Abstract</jats:title><jats:p>Concrete has become a significant part of our lives; the utilization of concrete is increasing at a high rate. One of the most constituents of concrete is Ordinary Portland Cement (OPC). The manufacturing process of OPC leads to the emission of huge amounts of CO<jats:sub>2</jats:sub>. Thus the researchers have started finding alternatives for the replacement of cement. The primary objective of this paper is to investigate the behavior of M40 grade concrete when partially replaced with Ground Granulated Blast Furnace slag (GGBS) at the same time using SAP and study the self-healing behavior of partially replacement concrete. In the self-healing process, the healing agent absorbs the moisture content within the atmosphere to heal the crack. Superabsorbent Polymers (SAPs) are materials that will absorb and retain an oversized volume of water and aqueous solutions. In this investigation, 51 samples of cubes are prepared for compressive strength test and self-healing test, the specimen is pre-cracked on the 28th day for healing purposes. Further, this article aims to predict the self-healing characteristics of the M40 grade of concrete using Neural Networks by incorporating different proportions of GGBS (0%, 40% and 60%) and SAPs. The predicted results obtained from the ANN model were in good agreement with experimental values.</jats:p>

Topics
  • impedance spectroscopy
  • polymer
  • crack
  • strength
  • cement