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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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 (3/3 displayed)

  • 2023A Neural Network-Based Prediction of Superplasticizers Effect on the Workability and Compressive Characteristics of Portland Pozzolana Cement-Based Mortars5citations
  • 2022An Artificial Neural Network Based Prediction of Mechanical and Durability Characteristics of Sustainable Geopolymer Composite29citations
  • 2018Strength Enhancement of Silty Sand Soil Subgrade of Highway Pavement Using Lime and Fines from Demolished Concrete Wastes5citations

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Chart of shared publication
Kumar, Prem
2 / 4 shared
Duraimurugan, S.
1 / 2 shared
Vasugi, V.
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Ganesh, A. Chithambar
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Sankar, M.
1 / 4 shared
Santhi, M. Helen
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Natrayan, Lakshmaiya
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Khwairakpam, Selija
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Banzibaganye, Gerard
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2023
2022
2018

Co-Authors (by relevance)

  • Kumar, Prem
  • Duraimurugan, S.
  • Vasugi, V.
  • Ganesh, A. Chithambar
  • Sankar, M.
  • Santhi, M. Helen
  • Natrayan, Lakshmaiya
  • Khwairakpam, Selija
  • Banzibaganye, Gerard
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article

A Neural Network-Based Prediction of Superplasticizers Effect on the Workability and Compressive Characteristics of Portland Pozzolana Cement-Based Mortars

  • Kumar, Prem
  • Duraimurugan, S.
  • Ganesan, Senthil Kumaran
  • Vasugi, V.
  • Ganesh, A. Chithambar
  • Sankar, M.
Abstract

<jats:p>Portland Pozzolana Cement (PPC) mortars are predominantly employed in plastering works to achieve better workability, superior surface finish, and higher fineness to offer better cohesion with fine aggregates than the ordinary Portland cement (OPC) mortars. To achieve high performance in the cement mortar similar to cement concrete, the addition of a superplasticizer is recommended. The present study investigates the impact of addition of sulphonated naphthalene formaldehyde- (SNF)-based (0.5%, 0.6%, 0.7%, and 0.8%) and lignosulphate- (LS)-based (0.2%, 0.3%, 0.4%, and 0.5%) superplasticizers on the workability and compressive strength characteristics of PPC mortars. Plastering mortars of ratio 1 : 4 were prepared with natural sand and manufacturing sand (M sand) as fine aggregates. A flow table test was conducted on all the mortar mix proportions, and the effects of the inclusion of superplasticizers on flow properties were recorded at different time intervals (0, 30, 60, 90, and 120 minutes). PPC mortar cubes were prepared, cured, and examined to assess the inclusion of chemical admixtures on compressive strength at different ages (1, 3, 7, 14, and 28 days). The experimental findings from the workability and compressive strength of PPC mortars were analyzed, and the corresponding results were predicted using artificial intelligence. Experimental investigations demonstrated that the desired flow characteristics and higher compressive strength results were achieved from a 0.7% dosage of ligno-based superplasticizer. The predicted workability and compressive strength results at various ages acquired by implementing an Artificial Neural Network (ANN) were found to be in close agreement with the experimental results.</jats:p>

Topics
  • impedance spectroscopy
  • surface
  • inclusion
  • strength
  • cement
  • laser sintering