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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Norwegian University of Science and Technology

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (4/4 displayed)

  • 2022An engineered ML model for prediction of the compressive strength of Eco-SCC based on type and proportions of materials11citations
  • 2019An evolutionary-based prediction model of the 28-day compressive strength of high-performance concrete containing cementitious materials12citations
  • 2019Energy dissipation and storage in underground mining operations39citations
  • 2017Green concrete with high-volume fly ash and slag with recycled aggregate and recycled water to build future sustainable cities67citations

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Chart of shared publication
Sadrossadat, Ehsan
2 / 2 shared
Seibi, Abdennour
1 / 3 shared
Dong, Xiangjian
1 / 1 shared
Chart of publication period
2022
2019
2017

Co-Authors (by relevance)

  • Sadrossadat, Ehsan
  • Seibi, Abdennour
  • Dong, Xiangjian
OrganizationsLocationPeople

article

An evolutionary-based prediction model of the 28-day compressive strength of high-performance concrete containing cementitious materials

  • Sadrossadat, Ehsan
  • Basarir, Hakan
Abstract

<p>High-performance concrete (HPC) is a class of concretes that may contain more cementitious materials other than portland cement, such as fly ash and blast furnace slag, in addition to chemical admixtures, e.g., plasticizers. Strength, durability, and rheological properties of the normal concrete are enhanced in HPC. The compressive strength of HPC can be considered as a key factor to identify the level of its quality in concrete technology and the construction industry. This parameter can be directly acquired by experimental observations. However, testing methods are often time consuming, expensive, or inefficient. This article aims to develop and propose a new mathematical equation formulating the compressive strength of HPC specimens 28 days in age through a robust artificial intelligence algorithm known as linear genetic programming (LGP) using a valuable experimental database. The LGP-based model proposed here can be used for manual calculations and is able to estimate the compressive strength of HPC samples with a good degree of accuracy. The performance of the LGP model is confirmed through comparing the results with those provided by other models. The sensitivity analysis is also conducted, and it is concluded that the amount of cementitious materials, such as cement and furnace slag, have more influence than other variables.</p>

Topics
  • impedance spectroscopy
  • strength
  • cement
  • durability