Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

Version 1 : Received: 16 May 2022 / Approved: 17 May 2022 / Online: 17 May 2022 (13:53:48 CEST)

A peer-reviewed article of this Preprint also exists.

Chen, H.; Sun, Z.; Zhong, Z.; Huang, Y. Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network. Materials 2022, 15, 4491. Chen, H.; Sun, Z.; Zhong, Z.; Huang, Y. Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network. Materials 2022, 15, 4491.

Abstract

The fatigue life of concrete is affected by many interwoven factors whose effect is nonlinear. Be-cause of its unique self-learning ability and strong generalization capability, the Bayesian regu-larized backpropagation neural network (BR-BPNN) is proposed to predict concrete behavior in tensile fatigue. The optimal model was determined through various combinations of network parameters. The average relative impact value (ARIV) was constructed to evaluate the correla-tion between fatigue life and its influencing parameters (maximum stress level Smax, stress ratio R, static strength f, failure probability P). ARIV results were also compared with other factor as-sessment methods (weight equation and multiple linear regression analyses). Using BR-BPNN, S-N curves were then obtained for the combinations of R=0.1, 0.2, 0.5; f=5, 6, 7MPa; P=5%, 50%, 95%. The tensile fatigue results under different testing conditions were finally compared for compatibility. It was concluded that Smax has the most significant negative effect on fatigue life; the degree of influence of R, P, and f, which positively correlate with fatigue life, decreases suc-cessively. ARIV is confirmed as a feasible way to analyze the importance of parameters and could be recommended for future applications. The tensile fatigue performance of plain concrete under different stress states (flexural tension, axial tension, splitting tension) does not differ sig-nificantly. Besides utilizing the valuable fatigue test data scattered in the literature, insights gained from this work could provide a reference for subsequent fatigue test program design and fatigue evaluation.

Keywords

concrete tensile fatigue; neural networks; Bayesian regularization; parameter assessment; fatigue life prediction

Subject

Engineering, Civil Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.