Structural System Reliability Analysis for a Self-Anchored Suspension Bridge

2012 ◽  
Vol 166-169 ◽  
pp. 1868-1871
Author(s):  
Bao Chu Yu ◽  
Sheng Yong Li
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Suspension Bridge ◽  
Suspension Bridges ◽  
Complex Structures ◽  
Structural Elements ◽  
Structural System ◽  
Structural System Reliability ◽  
Bridge Structures ◽  
Bridge Reliability

A self-anchored suspension bridge is analyzed for its structural system reliability, and the bridge reliability is relatively high under vehicular load. Through seeking the distinct structural system fault modes the relatively weak structural elements can be identified, which can be the basis on its reasonable design and offer the reference to the analysis for the similar bridge structures. The results verify that the global -Unzipping method and the improved difference equivalent recursion algorithm is efficient and applicable for system reliability evaluation of the complex structures such as self-anchored suspension bridges.

Analysis of Dynamic Reliability on Large-Span Bridge

2013 ◽  
Vol 303-306 ◽  
pp. 2880-2884
Author(s):  
Ying Guo ◽  
Guang Yi Zhu ◽  
Shu Min Cong
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Calculation Model ◽  
Seismic Load ◽  
Bridge Structure ◽  
Structural System ◽  
Component Level ◽  
Dynamic Reliability ◽  
Large Span ◽  
Structural System Reliability

For the current study of large-span bridges are mostly in the component level, the real structural system reliability analysis is still relatively small, so based on the theory of dynamic reliability analysis of structure, the overall bridge structure system for the calculation model is established ,then reliability analysis under the multi-dimensional seismic load and the effect of dynamic flutter are performed, the results of sensitivity analysis with different parameters are obtained, so it is useful basis for the bridge design and evaluation.

An Structural System Reliability Calculation Method Based on Sample’s Weighting -Artificial Neural Network

2011 ◽  
Vol 243-249 ◽  
pp. 666-671
Author(s):  
Wei Min Chen ◽  
Zhi Gang Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Calculation Method ◽  
System Reliability ◽  
Weighting Coefficient ◽  
Structural System ◽  
Structural System Reliability ◽  
Reliability Calculation ◽  
Artificial Neural

This paper presents a new structural system reliability calculation method with artificial neural network adopt by the sample’s weighting and the artificial neural network, which can greatly reduce calculation work. Firstly, according to the reliability theory of total probability, with the concept of sample’s weighting coefficient, the minimum sample size can be obtained according to the numerical characteristic values of random variables and the minor sample t-distribution estimation under a certain expected value. And then, the optimize artificial neural network is set up with the limited training samples based on the analysis result of sample’s weighting coefficient, which has a highly nonlinear mapping relationship between the efficacy and response of the structural system reliability Analysis. By making use of the generalization capability of optimize artificial neural network, sufficient system response value is gained at random. Meanwhile, the weight coefficients of the random sample combinations are determined using the Bayes formula, and different sample combinations are taken as the input for system analysis. According to one-to-one mapping of system by artificial neural network between the input sample combination and the output coefficient, the reliability index of system can be calculated. At last the method provides a new attempt for S structural system reliability analysis and prove to be feasible and effective for practical experience in complex system, which not only makes the artificial neural network calculation more effective based on sample’s weighting coefficient, also makes full use of the merit of artificial neural network instead of the performance function.

Sign in / Sign up

Export Citation Format

Share Document