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The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm

In this research, the authors have developed an algorithm for predicting the compressive strength and compressive stress–strain curve of Basalt Fiber High-Performance Concrete (BFHPC), which is enhanced by a classical programming algorithm and Logistic Map. For this purpose, different percentages of...

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Autores principales: Hematibahar, Mohammad, Vatin, Nikolai Ivanovich, Ashour Alaraza, Hayder Abbas, Khalilavi, Aghil, Kharun, Makhmud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572036/
https://www.ncbi.nlm.nih.gov/pubmed/36234316
http://dx.doi.org/10.3390/ma15196975
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author Hematibahar, Mohammad
Vatin, Nikolai Ivanovich
Ashour Alaraza, Hayder Abbas
Khalilavi, Aghil
Kharun, Makhmud
author_facet Hematibahar, Mohammad
Vatin, Nikolai Ivanovich
Ashour Alaraza, Hayder Abbas
Khalilavi, Aghil
Kharun, Makhmud
author_sort Hematibahar, Mohammad
collection PubMed
description In this research, the authors have developed an algorithm for predicting the compressive strength and compressive stress–strain curve of Basalt Fiber High-Performance Concrete (BFHPC), which is enhanced by a classical programming algorithm and Logistic Map. For this purpose, different percentages of basalt fiber from 0.6 to 1.8 are mixed with High-Performance Concrete with high-volume contact of cement, fine and coarse aggregate. Compressive strengths and compressive stress–strain curves are applied after 7-, 14-, and 28-day curing periods. To find the compressive strength and predict the compressive stress–strain curve, the Logistic Map algorithm was prepared through classical programming. The results of this study prove that the logistic map is able to predict the compressive strength and compressive stress–strain of BFHPC with high accuracy. In addition, various types of methods, such as Coefficient of Determination (R2), are applied to ensure the accuracy of the algorithm. For this purpose, the value of R2 was equal to 0.96, which showed that the algorithm is reliable for predicting compressive strength. Finally, it was concluded that The Logistic Map algorithm developed through classical programming could be used as an easy and reliable method to predict the compressive strength and compressive stress–strain of BFHPC.
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spelling pubmed-95720362022-10-17 The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm Hematibahar, Mohammad Vatin, Nikolai Ivanovich Ashour Alaraza, Hayder Abbas Khalilavi, Aghil Kharun, Makhmud Materials (Basel) Article In this research, the authors have developed an algorithm for predicting the compressive strength and compressive stress–strain curve of Basalt Fiber High-Performance Concrete (BFHPC), which is enhanced by a classical programming algorithm and Logistic Map. For this purpose, different percentages of basalt fiber from 0.6 to 1.8 are mixed with High-Performance Concrete with high-volume contact of cement, fine and coarse aggregate. Compressive strengths and compressive stress–strain curves are applied after 7-, 14-, and 28-day curing periods. To find the compressive strength and predict the compressive stress–strain curve, the Logistic Map algorithm was prepared through classical programming. The results of this study prove that the logistic map is able to predict the compressive strength and compressive stress–strain of BFHPC with high accuracy. In addition, various types of methods, such as Coefficient of Determination (R2), are applied to ensure the accuracy of the algorithm. For this purpose, the value of R2 was equal to 0.96, which showed that the algorithm is reliable for predicting compressive strength. Finally, it was concluded that The Logistic Map algorithm developed through classical programming could be used as an easy and reliable method to predict the compressive strength and compressive stress–strain of BFHPC. MDPI 2022-10-08 /pmc/articles/PMC9572036/ /pubmed/36234316 http://dx.doi.org/10.3390/ma15196975 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hematibahar, Mohammad
Vatin, Nikolai Ivanovich
Ashour Alaraza, Hayder Abbas
Khalilavi, Aghil
Kharun, Makhmud
The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm
title The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm
title_full The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm
title_fullStr The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm
title_full_unstemmed The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm
title_short The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm
title_sort prediction of compressive strength and compressive stress–strain of basalt fiber reinforced high-performance concrete using classical programming and logistic map algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572036/
https://www.ncbi.nlm.nih.gov/pubmed/36234316
http://dx.doi.org/10.3390/ma15196975
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