Combination line sampling for structural reliability analysis

By Iason Papaioannou, Daniel Straub. 2021

87 citations recorded by the source corpus.

Abstract Line sampling is a method for efficient estimation of the failure (or rare event) probability. The method generates a set of samples on a hyperplane perpendicular to an important direction that points towards the failure domain, and estimates the probability of failure as a sample mean of one-dimensional probability integrals. The performance of the method strongly depends on the quality of the chosen important direction. Recently, an adaptive approach for adjusting the important direction during the simulation has been proposed, termed advanced line sampling (ALS). This contribution revisits the ALS method and shows that the ALS estimator can be viewed as a combination of estimators, each one corresponding to a direction in the adaptive sequence. We show that the combination implied by the original ALS is suboptimal and propose an alternative combination of estimators. The resulting method is termed combination line sampling (CLS). We demonstrate through three numerical examples that CLS outperforms the ALS estimator, in particular if the initially selected important direction is poor.

Subfields: Civil and Structural Engineering, Mechanics of Materials, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/74b3897a45d107414b18f7f23d109cf4d9ebbe71

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Kai Cheng
2. Xiaobo Zhang
3. Yanping Wang
4. Iason Papaioannou
5. Zhenzhou Lu

In Mechanics of Materials

1. Kai Cheng
2. Xiaobo Zhang
3. Yanping Wang
4. Zhenzhou Lu
5. Iason Papaioannou

In Statistics and Probability

1. Kai Cheng
2. Xiaobo Zhang
3. Yanping Wang
4. Zhenzhou Lu
5. Iason Papaioannou

Citations in the corpus

• Rare event estimation with sequential directional importance sampling (2022, methodology): “Numerical simulation methods include Monte Carlo simulation (MCS) and variance reduction methods such as importance sampling (IS) [18-20] and its enhanced versions [21-24], subset simulation (SuS) [15, 16, 25], thermodynamic integration and parallel tempering (TIPT) [26], line sampling (LS) [27-29],”