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A platform for research: civil engineering, architecture and urbanism
Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach
https://orcid.org/0000-0002-6394-4899
Abstract This paper proposes an efficient solution for solving hybrid reliability problems involving random and interval variables. To meet this aim, using the soft Monte Carlo (SMC) method, a solution is proposed that breaks the random variables space into local 1-D coordinates and then, considers 1-D coordinate as an additional dimension of interval variables. Accordingly, using an optimization in increased interval variables space, the upper and lower bounds of failure probability for each 1-D problem are estimated. In addition, the total failure probabilities are presented as the mathematical expectation of the obtained probability bounds for 1-D coordinates. Then, it is shown that this approach is fit for application of univariate dimension reduction method to reduce the function calls of analysis in the optimization phase. This approach is validated by solving benchmark reliability problems as well as the application of the proposed method for solving real world engineering problems investigated by solving hybrid reliability analysis of reinforced concrete columns. It is shown that the proposed approach efficiently approximates the failure probability bound of problems with moderate nonlinear limit state functions with high accuracy.
Highlights Developing a robust hybrid reliability framework without an optimization block. Capturing both random and interval variables via coupling UDR and soft MCS methods. Accurate/efficient estimation of upper and lower bounds with hybrid reliability tool. Both mathematical and engineering problems are validated using hybrid technique.
Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach
https://orcid.org/0000-0002-6394-4899
Abstract This paper proposes an efficient solution for solving hybrid reliability problems involving random and interval variables. To meet this aim, using the soft Monte Carlo (SMC) method, a solution is proposed that breaks the random variables space into local 1-D coordinates and then, considers 1-D coordinate as an additional dimension of interval variables. Accordingly, using an optimization in increased interval variables space, the upper and lower bounds of failure probability for each 1-D problem are estimated. In addition, the total failure probabilities are presented as the mathematical expectation of the obtained probability bounds for 1-D coordinates. Then, it is shown that this approach is fit for application of univariate dimension reduction method to reduce the function calls of analysis in the optimization phase. This approach is validated by solving benchmark reliability problems as well as the application of the proposed method for solving real world engineering problems investigated by solving hybrid reliability analysis of reinforced concrete columns. It is shown that the proposed approach efficiently approximates the failure probability bound of problems with moderate nonlinear limit state functions with high accuracy.
Highlights Developing a robust hybrid reliability framework without an optimization block. Capturing both random and interval variables via coupling UDR and soft MCS methods. Accurate/efficient estimation of upper and lower bounds with hybrid reliability tool. Both mathematical and engineering problems are validated using hybrid technique.
Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach
https://orcid.org/0000-0002-6394-4899
Abstract This paper proposes an efficient solution for solving hybrid reliability problems involving random and interval variables. To meet this aim, using the soft Monte Carlo (SMC) method, a solution is proposed that breaks the random variables space into local 1-D coordinates and then, considers 1-D coordinate as an additional dimension of interval variables. Accordingly, using an optimization in increased interval variables space, the upper and lower bounds of failure probability for each 1-D problem are estimated. In addition, the total failure probabilities are presented as the mathematical expectation of the obtained probability bounds for 1-D coordinates. Then, it is shown that this approach is fit for application of univariate dimension reduction method to reduce the function calls of analysis in the optimization phase. This approach is validated by solving benchmark reliability problems as well as the application of the proposed method for solving real world engineering problems investigated by solving hybrid reliability analysis of reinforced concrete columns. It is shown that the proposed approach efficiently approximates the failure probability bound of problems with moderate nonlinear limit state functions with high accuracy.
Highlights Developing a robust hybrid reliability framework without an optimization block. Capturing both random and interval variables via coupling UDR and soft MCS methods. Accurate/efficient estimation of upper and lower bounds with hybrid reliability tool. Both mathematical and engineering problems are validated using hybrid technique.
Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach
Abdollahi, Azam (author) / Shahraki, Hossein (author) / Faes, Matthias G.R. (author) / Rashki, Mohsen (author)
Structural Safety ; 106
2023-09-07
Article (Journal)
Electronic Resource
English
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