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

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.

Keywords

• Deep Beam
• Neural Network
• Shear Strength
• Ultra High Performance