Shear strength estimation of a FRP-strengthened RC beam: A comparison between an artificial neural network and guideline equations
Hamid Nezaminia
Department of Civil Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.
DOI:
https://doi.org/10.7494/cmms.2024.3.0830
Abstract:
In recent years, several experimental tests have been conducted on the shear strengthening of reinforced concrete (RC) beams strengthened by fiber-reinforced polymer (FRP) systems. In this regard, some equations have also been proposed to estimate the shear strength of beams reinforced with FRP systems. The aim of this study is to investigate the estimation of the shear strength of beams reinforced with FRP systems using an artificial neural network model. For this purpose, a comprehensive and extensive review of forty published articles has been carried out to compile data on 304 RC beams strengthened with externally bonded FRP systems to improve their shear strength. These laboratory results have been used to provide a database for the ANN model to evaluate the shear behavior. The input to the ANN model consists of the 11 variables, including the sectional geometry, reinforcement ratio, FRP ratio, and the characteristics of concrete, steel reinforcement, and composite material, while the output variable is the shear strength of the FRP-strengthened RC beam. In order to evaluate the effectiveness of the neural network model in estimating the shear capacity of RC beams, the results obtained from the neural network model are compared with the equations from the Publication No. 345 and ACI 440.2R guidelines. The comparison of the results shows that the predictive power of the proposed model is much better than the experimental guidelines. Specifically, the mean absolute relative error (MARE) criteria for the studied data is 13%, 34% and 39% for the ANN model, ACI 440.2R guideline and the Publication No. 345 guideline, respectively.
Cite as:
Nezaminia, H. (2024). Shear strength estimation of a FRP-strengthened RC beam: A comparison between an artificial neural network and guideline equations. Computer Methods in Materials Science, 24(3), 5 – 32. https://doi.org/10.7494/cmms.2024.3.0830
Article (PDF):
Keywords:
Concrete beam, Fiber-reinforced polymers, Shear strength, Artificial neural network, ACI440.2R
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