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

• Published in: Materials Vol. 15; no. 19; p. 6975
• Main Authors: Hematibahar, Mohammad, Vatin, Nikolai Ivanovich, Ashour Alaraza, Hayder Abbas, Khalilavi, Aghil, Kharun, Makhmud
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 08.10.2022; MDPI
• Subjects: Algorithms; Artificial intelligence; Balaguru, Perumalsamy Naidu; Basalt; basalt fiber high-performance concrete; Cement; classical programming; Compressive properties; Compressive strength; compressive strength prediction; compressive stress–strain curve; Concrete; Contact stresses; Data mining; Density; Fiber reinforced concretes; Investigations; logistic function and map; Mechanical properties; Methods; Programming; Regression analysis; Stress-strain curves; Tensile strength; Russia
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma15196975

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.