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Title: Variance-based sensitivity indices for stochastic models with correlated inputs

The goal of this article is the formulation of the principles of one of the possible strategies in implementing correlation between input random variables so as to be usable for algorithm development and the evaluation of Sobol’s sensitivity analysis. With regard to the types of stochastic computational models, which are commonly found in structural mechanics, an algorithm was designed for effective use in conjunction with Monte Carlo methods. Sensitivity indices are evaluated for all possible permutations of the decorrelation procedures for input parameters. The evaluation of Sobol’s sensitivity coefficients is illustrated on an example in which a computational model was used for the analysis of the resistance of a steel bar in tension with statistically dependent input geometric characteristics.
Authors:
 [1]
  1. Brno University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics Veveří St. 95, ZIP 602 00, Brno (Czech Republic)
Publication Date:
OSTI Identifier:
22391166
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1648; Journal Issue: 1; Conference: ICNAAM-2014: International Conference on Numerical Analysis and Applied Mathematics 2014, Rhodes (Greece), 22-28 Sep 2014; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; CORRELATIONS; EVALUATION; INDEXES; MATHEMATICAL MODELS; MECHANICAL STRUCTURES; MECHANICS; RANDOMNESS; SENSITIVITY ANALYSIS; STEELS; STOCHASTIC PROCESSES