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Development and Application of Low-Carbon Cementitious Material

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Composites".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 1978

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Centre for Advanced and Innovative Technologies – VŠB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
Interests: brittle fracture initiation in steels; micromechanic; fatigue of metals; damage mechanics
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Special Issue Information

Dear Colleagues,

Every year, over 10 billion tons of industrial solid waste are discharged by the mining, metallurgy, chemical, power and structural engineering industries. Most commonly, this solid waste is disposed of by landfill. However, this not only occupies land but also generates increasingly severe pressures on the environment and, moreover, risks becoming a bottleneck for the sustainable development of society. Meanwhile, to enable the construction of urban developments and infrastructure, over 10 billion tons of natural resources are consumed annually in the production of building materials. This causes massive emissions of sulfur oxides, carbon oxides and dust—and these emissions are becoming an urgent problem. In order to facilitate resource recycling and improve environmental protection, it is thus a major strategic priority to convert huge quantities of solid waste with potential hydration ability—such as fly ash, blast furnace slag, steel slag or waste concrete—into low-carbon cementitious materials, which can then be used to produce building materials. Low-carbon cementitious materials have recently become a hotspot of global research, thanks to the outstanding advantages they offer in terms of low energy consumption and efficient resource recycling. However, some major problems still remain to be solved, including issues related to raw materials, curing, treatment technologies, equipment and performance. I would like to invite you to focus on problems such as these when investigating the potential applications of low-carbon cementitious materials.

Prof. Dr. Bohumír Strnadel
Guest Editor

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Keywords

  • cement
  • concrete
  • mortar
  • industrial solid waste
  • fly ash
  • slag
  • mineral admixtures
  • alkali activated cementitious materials
  • sulfur oxide
  • carbon oxide
  • dust emission
  • energy saving
  • concrete curing conditions
  • treatment technology

Published Papers (1 paper)

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Research

15 pages, 3181 KiB  
Article
Predicting the Compressive Strength of the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method
by Jiandong Huang, Mohanad Muayad Sabri Sabri, Dmitrii Vladimirovich Ulrikh, Mahmood Ahmad and Kifayah Abood Mohammed Alsaffar
Materials 2022, 15(12), 4193; https://0-doi-org.brum.beds.ac.uk/10.3390/ma15124193 - 13 Jun 2022
Cited by 23 | Viewed by 1746
Abstract
Concrete is the most widely used material in construction. It has the characteristics of strong plasticity, good economy, high safety, and good durability. As a kind of structural material, concrete must have sufficient strength to resist various loads. At the same time, due [...] Read more.
Concrete is the most widely used material in construction. It has the characteristics of strong plasticity, good economy, high safety, and good durability. As a kind of structural material, concrete must have sufficient strength to resist various loads. At the same time, due to the brittleness of concrete, compressive strength is the most important mechanical property of concrete. To solve the disadvantages of the low efficiency of the traditional concrete compressive strength prediction methods, this study proposes a firefly algorithm (FA) and random forest (RF) hybrid machine-learning method to predict the compressive strength of concrete. First, a database is built based on the data of published articles. The dataset in the database contains eight input variables (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age) and one output variable (concrete compressive strength). Then, the correlation of the eight input variables was analyzed, and the results showed that there was no high correlation between the input variables; thus, they could be used as input variables to predict the compressive strength of concrete. Next, this study used the FA algorithm to optimize the hyperparameters of RF to obtain better hyperparameters. Finally, we verified that the FA and RF hybrid machine-learning model proposed in this study can predict the compressive strength of concrete with high accuracy by analyzing the R values and RSME values of the training set and test set and comparing the predicted value and actual value of the training set and test machine. Full article
(This article belongs to the Special Issue Development and Application of Low-Carbon Cementitious Material)
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