Sensitivity and Reliability Analysis of a Self-Anchored Suspension Bridge
Publication: Journal of Bridge Engineering
Volume 18, Issue 8
Abstract
This paper presents a sensitivity and reliability analysis of a self-anchored suspension bridge by applying a new hybrid method proposed by the authors based on integration of the Latin hypercube sampling technique (LHS), artificial neural network (ANN), first-order reliability method (FORM), Pearson’s linear correlation coefficient (PLCC), and Monte Carlo simulation with important sampling technique (MCS-IS). The framework consists of three stages of analysis: (1) selection of training, validation, and test datasets for establishing an ANN model by the LHS technique; (2) formulation of a performance function from the well-trained ANN model; and (3) sensitivity analysis using PLCC, identification of the most probabilistic failure point based on FORM, and estimation of the failure probability using the MCS-IS technique. Upon demonstration of its efficiency through analysis of a 12-story frame structure, the method is applied to sensitivity and reliability analysis of the Jiangxinzhou Bridge, a five-span self-anchored suspension bridge, in which both structural parameters and external loads are considered as random variables. The analysis identified a number of structural parameters, as well as external loads, that have a significant influence on structural serviceability and safety.
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Acknowledgments
This work was partially supported by China National Science Fund for Distinguished Young Scholars Grant No. 50725828.
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Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 18 • Issue 8 • August 2013
Pages: 703 - 711
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Apr 8, 2011
Accepted: Jul 5, 2012
Published online: Jul 21, 2012
Published in print: Aug 1, 2013
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