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Efficient reliability-based optimization of linear dynamic systems with random structural parameters
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DOI：10.1007/s00158-021-03011-0.png)
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Abstract
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This paper proposes a novel method to address the challenges associated with reliability-based design optimization (RBDO) of a class of problems, namely design of a linear system with random structural parameters subject to stochastic excitation. This method effectively estimates the failure probability as an explicit function of the design variables by representing the failure probability function (FPF) as a weighted average of sample values, which are generated by means of a single reliability analysis. The resulting estimation of the FPF is then used to decouple the target RBDO problem into a deterministic optimization problem, which can be solved by any appropriate deterministic optimization algorithm. In addition, a sequential approximate optimization framework is adopted to iteratively seek the solution of the RBDO problem. Several examples are provided to demonstrate the high accuracy and efficiency of the proposed method.
Keywords:
Reliability-based optimization
Linear dynamic system
Uncertain systems
Decoupling approach
Importance sampling
High dimensions
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