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Sensitivity and Reliability Analysis of a Self-Anchored Suspension Bridge
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.
Sensitivity and Reliability Analysis of a Self-Anchored Suspension Bridge
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.
Sensitivity and Reliability Analysis of a Self-Anchored Suspension Bridge
Li, Jianhui (Autor:in) / Li, Aiqun (Autor:in) / Feng, Maria Q. (Autor:in)
Journal of Bridge Engineering ; 18 ; 703-711
21.07.2012
92013-01-01 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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