Application of neural network model for the prediction of shear strength of reinforced concrete beams

Title:

Application of neural network model for the prediction of shear strength of reinforced concrete beams

Creator:

Ahmad, Irshad, Shah, Attuaullah, and Khqan , Akhter Naeem

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:a70c343d-0ebc-4718-8123-eba606d64391
uuid:a70c343d-0ebc-4718-8123-eba606d64391

Subject:

Neural network, shear, beam, concrete, deep beams, and size effect

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper evaluates the feasibility of using an Artificial Neural Network (ANN) model for estimating the nominal shear capacity of Reinforced Concrete (RC) beams against diagonal shear failure subjected to shear and flexure. A feedforward back-propagation ANN model was developed utilizing 622 experimental data points of RC beams, which include 111 deep beams data and 20 beams tested for low longitudinal steel ratios. The ANN model was trained on 70% of the data and then it was validated using the remaining 30% data (new data were not used for training). The trained ANN model was compared with three existing approaches, including the American Concrete Institute (ACI) code. The ANN model predictions when compared to the experimental data were very favorable, regarding also the other approaches. The prediction of ANN model was also checked for size effect and deep beams separately. The ANN model was found to be very robust in all situations. The safe form of ANN model was also derived and compared with the design equations of the three methods.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:5

Harvested from:

CDK

Metadata only:

false