Prediction of Shear Strength of Ultra High Performance Reinforced Concrete Deep Beams without Stirrups by Neural Network

Yaseen, Sinan Abdulkhaleq and Aziz, Omar Qarani and Bakar, B.H. Abu (2017) Prediction of Shear Strength of Ultra High Performance Reinforced Concrete Deep Beams without Stirrups by Neural Network. Eurasian Journal of Science and Engineering, 3 (1). pp. 142-164. ISSN 24145629

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Official URL: http://dx.doi.org/10.23918/eajse.v3i1sip142

Abstract

Shear strength of ultra high performance reinforced concrete deep beams without stirrups predicted by neural network models. The neural network model based on 233 beams from literatures considering different parameters such as span to depth ratio, shear span to depth ratio, concrete compressive strength, amount of longitudinal reinforcement,…etc. Neural network can be used as an effective tool for predicting the shear capacity of normal & high strength concrete deep beams. Prediction shear strength by neural network very close to the experimental results with correlation coefficient of 0.836, while for ACIdesign eq., proposed eq. by Aziz & Zsutty where 0.394, 0.5624, and 0.488 respectively. The predicted shear strength model by neural network compared with ACI Code, Aziz and Zsutty equations, the results show that the Neural Network approach adequately captured the influence of concrete compressive strength on the shear capacity of reinforced concrete deep beams without shear reinforcement.

Item Type: Article
Uncontrolled Keywords: Deep Beam, Neural Network, Shear Strength, Ultra High Performance
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Ishik Eurasian Journal of Science and Engineering > VOL 3, NO 1 (2017)
Depositing User: Depositor @ Ishik University
Date Deposited: 26 Apr 2018 20:14
Last Modified: 26 Apr 2018 20:14
URI: http://eprints.ishik.edu.iq/id/eprint/50

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