Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Computers and Concrete Volume 28, Number 6, December 2021 , pages 635-642 DOI: https://doi.org/10.12989/cac.2021.28.6.635 |
|
|
|
PSO based neural network to predict torsional strength of FRP strengthened RC beams |
||
Harish Narayana and Prashanth Janardhan
|
||
| Abstract | ||
| In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R2 of 0.9292 with an RMSE of 5.35 for training and an R2 of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R2 of 0.9125 with an RMSE of 6.17 for training and an R2 of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy. | ||
| Key Words | ||
| artificial neural network; fiber reinforced polymer; particle swarm optimization; prediction; RC beams; torsional strength | ||
| Address | ||
| Harish Narayana: Department of Civil Engineering, National Institute of Technology Goa, Ponda, Goa, India Prashanth Janardhan: Department of Civil Engineering, National Institute of Technology Silchar, Assam, India | ||