Materials Map

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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 (2/2 displayed)

  • 2022Prediction of Rapid Chloride Penetration Resistance of Metakaolin Based Concrete Using Multi-Expression Programming1citations
  • 2022Mechanical and Durability Evaluation of Metakaolin as Cement Replacement Material in Concrete13citations

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Iqbal, Mudassir
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Khan, Kaffayatullah
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Ahmad, Izaz
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2022

Co-Authors (by relevance)

  • Iqbal, Mudassir
  • Khan, Kaffayatullah
  • Ahmad, Izaz
  • Jalal, Fazal E.
  • Al-Hashem, Mohammed Najeeb
  • Alkadhim, Hassan Ali
  • Amin, Muhammad Nasir
  • Afzal, Muhammad
  • Ajwad, Ali
  • Qadir, Muhammad Ghulam
  • Faraz, Muhammad Iftikhar
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article

Prediction of Rapid Chloride Penetration Resistance of Metakaolin Based Concrete Using Multi-Expression Programming

  • Iqbal, Mudassir
  • Khan, Kaffayatullah
  • Ahmad, Izaz
  • Khan, Hayat
  • Jalal, Fazal E.
  • Al-Hashem, Mohammed Najeeb
  • Alkadhim, Hassan Ali
  • Amin, Muhammad Nasir
Abstract

<jats:p>This study investigates the resistance of concrete to Rapid Chloride ions Penetration (RCP) as an indirect measure of the concrete’s durability. The RCP resistance of concrete is modelled in multi-expression programming approach using different input variables, such as, age ofconcrete, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength (CS) of concrete. The parametric investigation was carried out by varying the hyperparameters, i.e., number of subpopulations <jats:italic>N</jats:italic><jats:sub>sub</jats:sub>, subpopulationsize <jats:italic>S</jats:italic><jats:sub>size</jats:sub>, crossover probability <jats:italic>C</jats:italic><jats:sub>prob</jats:sub>, mutation probability <jats:italic>M</jats:italic><jats:sub>prob</jats:sub>, tournament size <jats:italic>T</jats:italic><jats:sub>size</jats:sub>, code length <jats:italic>C</jats:italic><jats:sub>leng</jats:sub>, and number of generations <jats:italic>N</jats:italic><jats:sub>gener</jats:sub> to get an optimum model. The performanceof all the 29 number of trained models were assessed by comparing mean absolute error (MAE) values. The optimum model was obtained for <jats:italic>N</jats:italic><jats:sub>sub</jats:sub> = 50, <jats:italic>S</jats:italic><jats:sub>size</jats:sub> = 100, <jats:italic>C</jats:italic><jats:sub>prob</jats:sub> = 0.9, <jats:italic>M</jats:italic><jats:sub>prob</jats:sub> = 0.01, <jats:italic>T</jats:italic><jats:sub>size</jats:sub> = 9, <jats:italic>C</jats:italic><jats:sub>leng</jats:sub>= 100, and <jats:italic>N</jats:italic><jats:sub>gener</jats:sub> = 300 with MAE of 279.17 in case of training (TR) phase, whereas 301.66 for testing (TS) phase. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher <jats:italic>R</jats:italic> and<jats:italic>R</jats:italic><jats:sup>2</jats:sup> values equaling 0.96 and 0.93 (for the TR phase), and 0.92 and 0.90 (for the TS phase), respectively. Similarly, parametric and sensitivity analyses revealed that the RCP resistance is governed by the age of concrete, amount of binder, concrete CS, and aggregate quantityin the concrete mix. Among all the input variables, the RCP resistance sharply increased within the first 28 days age of the concrete specimen and similarly plummeted with increasing the quantity of fine aggregate, thus validating the model results.</jats:p>

Topics
  • impedance spectroscopy
  • phase
  • strength
  • durability
  • microwave-assisted extraction