Assessment of flexural and splitting strength of steel fiber reinforced concrete using automated neural network search

Zhenhao Zhang , Suvash C. Paul , Biranchi Panda , Yuhao Huang , Ankit Garg , Yi Zhang , Akhil Garg , Wengang Zhang

Abstract

Flexural and splitting strength behavior of conventional concrete can significantly be improved by incorporating the fibers in it. A significant number of research studies have been conducted on various types of fibers and their influence on the tensile capacity of concrete. However, as an important property, tensile capacity of fiber reinforced concrete (FRC) is not modelled properly. Therefore, this paper intends to formulate a model based on experiments that show the relationship between the fiber properties such as the aspect ratio (length/diameter), fiber content, compressive strength, flexural strength and splitting strength of FRC. For the purpose of modeling, various FRC mixes only with steel fiber are adopted from the existing research papers. Automated neural network search (ANS) is then developed and used to investigate the effect of input parameters such as fiber content, aspect ratio and compressive strength to the output parameters of flexural and splitting strength of FRC. It is found that the ANS model can be used to predict the flexural and splitting strength of FRC in a sensible precision.

Key Words

fiber aspect ratio; fiber content; compressive strength; flexural strength; splitting strength, FRC; ANS

Address

Zhenhao Zhang — School of Civil Engineering, Changsha University of Science and Technology, 960 2nd Section of Wanjiali South Road, Changsha, Hunan, China
Suvash C. Paul — Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor Darul Ehsan, Malaysia
Biranchi Panda — Singapore Centre for 3D Printing, School of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore
Yuhao Huang — Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, 243 Daxue Road, Jinping District, Shantou City, Guangdong Province, China
Ankit Garg — Department of Civil and Environmental Engineering, Shantou University, 243 Daxue Road, Jinping District, Shantou City, Guangdong Province, China
Yi Zhang — Leibniz Universität Hannover, 1 Welfengarten, Hannover, Germany
Akhil Garg — Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, 243 Daxue Road, Jinping District, Shantou City, Guangdong Province, China
Wengang Zhang — School of Civil and Architectural Engineering, Shandong University of Technology, 266 Xincun West Road, Zhangdian District, Zibo City, Shandong Province, China

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