Predicting the Shear Strength of Concrete Beams Reinforced with Fiber Reinforced Polymer Bars Using Adaptive Neuro-Fuzzy Inference System

By Mohammed A. Mashrei.

Published by The International Journal of the Constructed Environment

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In this paper an Adaptive Neuro-Fuzzy Inference System (ANFIS) model is developed to predict the shear strength of concrete beams reinforced with fiber reinforced polymer (FRP) bars. A database from tests on concrete beams reinforced with FRP bars obtained from the review of literature is used in this study. The data used in the ANFIS model are arranged in a format of six input parameters: the width and depth of beams, compressive strength of concrete, modulus of elasticity and reinforcement ratio of FRP, the shear span to depth ratio, and one output parameter which is shear strength. The results of this study indicate that the ANFIS provide good prediction values when compared with the experimental results and those results obtained by other methods.

Keywords: Concrete, Beams, Fiber Reinforced Polymers, Shear, ANFIS

The International Journal of the Constructed Environment, Volume 2, Issue 2, pp.59-74. Article: Print (Spiral Bound). Article: Electronic (PDF File; 992.312KB).

Dr. Mohammed A. Mashrei

Head Department of Civil Engineering, Civil/College of Engineering, North Carolina State University, Raleigh, Thi-Qar, Iraq

Dr. Mohammed A. Mashrei is a lecturer of civil engineering, head of Department of Civil Engineering, Thi-Qar University. Visitor scholar at North Carolina State University from 11-July 2011 to 10-July-2011. The general research interest of Dr. Mohammed A. Mashrei is in the behaviour of ferrocement, behaviour of reinforced concrete with FRP and the artificial neural network and adaptive neuro-fuzzy inference system models to structure engineering problems. Post Doctoral Fellow Department of Civil Engineering, North Carolina State University, Raleigh, NC.