Abstract
When assessing the reliability of a system, a mathematical model is often defined to replicate the system’s behavior. The inputs of the system are then gathered into two categories, random inputs and deterministic inputs. The failure of the system depends on both categories and here we focus on the influence of the deterministic inputs. Local failure probability sensitivity analysis consists in computing the derivatives of the failure probability with respect to these deterministic parameters and is a fundamental step in reliability-based design optimization. These sensitivities also provide valuable insights into how specific model parameters affect the failure probability, allowing engineers and designers to make informed decisions about adjusting those parameters to enhance reliability. This article explores various techniques developed in the literature for assessing the sensitivity of failure probability with respect to distribution or design parameters. Depending on the nature of the deterministic parameters and the selected input space, different methods are available. The statistical characteristics of the resulting estimates as well as their computational cost are discussed here, for comparison purpose.
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