Artificial Neural Network Model for Predicting the Tendon Stress in Unbonded Posttensioned Concrete Members at the Ultimate Limit State
Publication: Journal of Structural Engineering
Volume 148, Issue 10
Abstract
Existing design guidelines, codes, and literature provide different calculation models for the estimation of tendon stresses in unbonded posttensioned concrete members at the ultimate limit state. Most of these methods are based on theoretical (e.g., collapse mechanism and bond-reduction models) and statistically-based empirical models, with only a few or no surrogate models based on artificial neural networks (ANNs). This study presents an ANN-based model to predict stress in unbonded tendons at the ultimate limit state based on a database of 251 prestressed concrete members with unbonded tendons collected from the literature. The predictions from the ANN-based model show very good agreement with the experimental results given in the literature during training, testing, and validation. A sensitivity analysis has been performed to quantify the degree of influence of the input variables used in the developed ANN model. The analysis shows that the predictions of tendon stress using neural networks are more accurate than those results obtained using the models given in the design guidelines and the literature.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Structural Engineering
Volume 148 • Issue 10 • October 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 20, 2021
Accepted: May 9, 2022
Published online: Jul 21, 2022
Published in print: Oct 1, 2022
Discussion open until: Dec 21, 2022
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