Modeling of shear strength of RC beams using artificial neural network

• Main Authors: Aquino, Dan Emmanuel A., Que, Phoebee B., Urena, Aaron James B.
• Format: text
• Language: English
• Published: Animo Repository 2009
• Subjects: Concrete beams; Reinforced concrete; Polymer-impregnated concrete; Civil Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/11513
• Institution: De La Salle University

The increase in data available in literature on the shear strength of reinforced concrete (RC) beams prompted the creation of model that would be able to predict the shear strength of RC beams with a wider range of parameters. Two artificial neural network models were developed from the data currently available. The models were classified according to whether the beams had shear or without shear reinforcement. The models were then compared to existing design codes to verify the models accuracy. It was found out that the models developed by ANN were able to provide better predictions of the shear strength of RC beams. The model was also used to conduct parametric analysis. It was found out that there were other parameters that greatly affect the shear strength of RC beams other than the concrete compressive strength.