Benefit of splines and neural networks in simulation based structural reliability analysis

By L. Schueremans, D. Gemert. 2005

172 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9899d4e639096ae4f8bd0b4172765a9154b9d239

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. A. A. Chojaczyk
2. J. Cardoso
3. Luis A. C. Neves
4. Â. Teixeira
5. Rui Li
6. Jian Wang
7. Victor Picheny
8. Carlos Soares

In Statistics and Probability

1. Rui Li
2. Jian Wang
3. Zhili Sun
4. Cao Tong
5. B. Echard
6. N. Relun
7. Nicolas Gayton
8. David Ginsbourger

Citations in the corpus

• Review and application of Artificial Neural Networks models in reliability analysis of steel structures (2015, methodology): “Schueremans and Gemert [44] proposed an interesting simulation method based on adaptive ANN model, improved and refined during the reliability analysis.”
• A sequential surrogate method for reliability analysis based on radial basis function (2017, methodology): “The common surrogate models include polynomial response surface method (PRSM) [48-49], radial basis function (RBF) [48-49], Kriging [50-52], support vector regression (SVR) [50-52] and artificial neutral net (ANN) [53].”
• A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2013, background): “This problem is selected because it presents a moderate number of random variables and is a classic illustration in the literature [6,10,35,36].”
• LIF: A new Kriging based learning function and its application to structural reliability analysis (2017, methodology): “Compared with polynomial models, machine learning methods (neural network [21,22], support vector machine (SVM) [23–25], Multi-Layer Perceptrons [26] and so on) are more suitable to matching performance function with highly nonlinear input-output relationships.”
• Sequential design of computer experiments for the estimation of a probability of failure (2010, methodology): “Echard et al. (2010a,b) used this benchmark to make a comparison among several methods proposed in Schueremans and Gemert (2005), some of which are based on the construction of a response surface.”