Version 1
: Received: 19 November 2023 / Approved: 21 November 2023 / Online: 21 November 2023 (10:46:52 CET)
How to cite:
Xie, Z.; Dong, F. Probabilistic Assessment of Long-Span CFST Arch Bridges Based on Symmetrical Reliability Theory. Preprints2023, 2023111293. https://doi.org/10.20944/preprints202311.1293.v1
Xie, Z.; Dong, F. Probabilistic Assessment of Long-Span CFST Arch Bridges Based on Symmetrical Reliability Theory. Preprints 2023, 2023111293. https://doi.org/10.20944/preprints202311.1293.v1
Xie, Z.; Dong, F. Probabilistic Assessment of Long-Span CFST Arch Bridges Based on Symmetrical Reliability Theory. Preprints2023, 2023111293. https://doi.org/10.20944/preprints202311.1293.v1
APA Style
Xie, Z., & Dong, F. (2023). Probabilistic Assessment of Long-Span CFST Arch Bridges Based on Symmetrical Reliability Theory. Preprints. https://doi.org/10.20944/preprints202311.1293.v1
Chicago/Turabian Style
Xie, Z. and Fenghui Dong. 2023 "Probabilistic Assessment of Long-Span CFST Arch Bridges Based on Symmetrical Reliability Theory" Preprints. https://doi.org/10.20944/preprints202311.1293.v1
Abstract
The BP neural network and PSO algorithm are applied into analyzing the reliability of long-span Concrete Filled Steel Tubular (CFST) arch bridges. Firstly, using BP neural network to fit the structural performance function. And then the PSO method was used to calculate the reliability index. A long-span Concrete Filled Steel Tubular arch bridges reliability indices were calculated whether considering the geometric nonlinearity or not. The calculation and analysis results showed that the BP neural network and PSO algorithm compensated the deficiency of the traditional reliability analysis methods, improved the calculation accuracy, provided a new thought and means for the research on the reliability of long-span bridge structure, and well applied to the reliability analysis of long-span Concrete Filled Steel Tubular arch bridges. Additionally, the reliability analysis of long-span Concrete Filled Steel Tubular arch bridges on service limit state must consider the geometric nonlinearity effect, or the results will tend to be unsafe.
Keywords
CFST; BP neural network; PSO algorithm; symmetrical reliability; reliability index
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
