Support vector machines in reliability calculations of engineering structures

Support vector machines in reliability calculations of engineering structures

Paper in proceedings (conference paper)

In the paper, a metamodeling approach based on support vector regression is studied as a promising tool in the assessment of reliability level. The method consists of two steps: firstly, an approximation of the original limit state function is performed, and in the second step a failure probability or reliability index is calculated with a simpler, approximated function using traditional simulation techniques. Two problems with explicit limit state functions are used to study the effectivity of the method. In order to be as effective as possible with respect to computational effort, a stratified Latin Hypercube Sampling simulation method is utilized to properly select training set elements. The accuracy of the method is analyzed and compared with other surrogate modeling methods, namely the polynomial- and artificial neural network-based response surface method, achieving comparable results.

In the paper, a metamodeling approach based on support vector regression is studied as a promising tool in the assessment of reliability level. The method consists of two steps: firstly, an approximation of the original limit state function is performed, and in the second step a failure probability or reliability index is calculated with a simpler, approximated function using traditional simulation techniques. Two problems with explicit limit state functions are used to study the effectivity of the method. In order to be as effective as possible with respect to computational effort, a stratified Latin Hypercube Sampling simulation method is utilized to properly select training set elements. The accuracy of the method is analyzed and compared with other surrogate modeling methods, namely the polynomial- and artificial neural network-based response surface method, achieving comparable results.

Support vector machines;reliability analysis;failure probability;reliability index;surrogate model

Support vector machines;reliability analysis;failure probability;reliability index;surrogate model

ŠOMODÍKOVÁ, M.; LEHKÝ, D.

07.08.2025

CRC Press

London

9781003677895

Engineering Materials, Structures, Systems and Methods for a More Sustainable Future

1113

1118

6

https://www.taylorfrancis.com/chapters/edit/10.1201/9781003677895-187/support-vector-machines-reliability-calculations-engineering-structures-šomodíková-lehký

http://hdl.handle.net/11012/255574