On Latin hypercube sampling for structural reliability analysis

By A. Olsson, G. Sandberg, O. Dahlblom. 2003

744 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b7b5a9959b49ca1c568e7f4bcfd745307295dce1

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. Qing Li
2. Guangyong Sun
3. Guangyao Li
4. Michael D. Shields
5. Jiaxin Zhang
6. Shu Yang
7. Chang Qi
8. Yi Min Xie

In Mechanics of Materials

1. Guangyong Sun
2. Qing Li
3. Guangyao Li
4. Michael D. Shields
5. Jiaxin Zhang
6. Guirong Liu
7. Trung Nguyen-Thoi
8. Hung Nguyen-Xuan

In Statistics and Probability

1. Qing Li
2. Michael D. Shields
3. Guangyong Sun
4. Jiaxin Zhang
5. Guangyao Li

Citations in the corpus

• The generalization of Latin hypercube sampling (2015, methodology): “Meanwhile, LHS has been applied to nearly every type of probabilistic analysis one can imagine, ranging from the estimation of reliability (probability of failure) [28,4,29,30] to coefficient estimation for polynomial chaos, neural network, and other types of surrogate models [8,31].”
• Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models (2013, background): “Of many different DOE methods available, Latin hypercube sampling (LHS) is considered rather effective on reducing computational cost (Olsson et al. 2003).”
• Review and application of Artificial Neural Networks models in reliability analysis of steel structures (2015, neutral): “[50].”