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A platform for research: civil engineering, architecture and urbanism
Active learning-based optimization of structures under stochastic excitations with first-passage probability constraints
Abstract In various efforts to manage the risk of structural failures caused by stochastic excitations such as wind and earthquake loads, the first-passage probability often needs to be calculated as a reliability constraint. Estimating the first-passage probability with low computational costs is essential, especially in structural optimization requiring iterative reliability calculations for design alternatives. This paper presents a new active learning-based framework for reliability-based design optimization (RBDO) of structures under stochastic excitations. A mixture-distribution-based formulation of the first-passage probability is utilized to handle the high-dimensional sequences of stochastic excitations during the optimization. The design parameter sensitivity of the first-passage probability is introduced to use a gradient-based optimizer in the RBDO iterations. These procedures employ heteroscedastic Gaussian process-based surrogates of the logarithmic responses. An active-learning scheme identifies the best training point to reduce the computational costs in the first-passage probability calculations and optimization. The numerical examples dealing with the optimal design of an eight-story building system and a tower structure subjected to stochastic wind loads demonstrate the accuracy and efficiency of the proposed method.
Highlights Optimal design of structures subjected to stochastic excitations is identified. The mixture-based probability equation can handle high-dimensional uncertainties. Formula of first-passage probability sensitivity is derived for optimization. Active learning strategies enhance the computational efficiency of optimization. The viscous damper design is optimized against stochastic wind excitations.
Active learning-based optimization of structures under stochastic excitations with first-passage probability constraints
Abstract In various efforts to manage the risk of structural failures caused by stochastic excitations such as wind and earthquake loads, the first-passage probability often needs to be calculated as a reliability constraint. Estimating the first-passage probability with low computational costs is essential, especially in structural optimization requiring iterative reliability calculations for design alternatives. This paper presents a new active learning-based framework for reliability-based design optimization (RBDO) of structures under stochastic excitations. A mixture-distribution-based formulation of the first-passage probability is utilized to handle the high-dimensional sequences of stochastic excitations during the optimization. The design parameter sensitivity of the first-passage probability is introduced to use a gradient-based optimizer in the RBDO iterations. These procedures employ heteroscedastic Gaussian process-based surrogates of the logarithmic responses. An active-learning scheme identifies the best training point to reduce the computational costs in the first-passage probability calculations and optimization. The numerical examples dealing with the optimal design of an eight-story building system and a tower structure subjected to stochastic wind loads demonstrate the accuracy and efficiency of the proposed method.
Highlights Optimal design of structures subjected to stochastic excitations is identified. The mixture-based probability equation can handle high-dimensional uncertainties. Formula of first-passage probability sensitivity is derived for optimization. Active learning strategies enhance the computational efficiency of optimization. The viscous damper design is optimized against stochastic wind excitations.
Active learning-based optimization of structures under stochastic excitations with first-passage probability constraints
Kim, Jungho (author) / Yi, Sang-ri (author) / Song, Junho (author)
Engineering Structures ; 307
2024-03-13
Article (Journal)
Electronic Resource
English
TE-6-6 First passage probability of structures under non-Gaussian stochastic behavior
| British Library Conference Proceedings | 2007