Reliability-based design optimization using Kriging and support vector machines

2014 ◽  
pp. 3305-3312 ◽  
Author(s):  
Sylvain Lacaze ◽  
Samy Missoum
Keyword(s):  
Support Vector Machines ◽  
Design Optimization ◽  
Support Vector ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Vector Machines

Optimal Reliability-Based Design Using Support Vector Machines and Artificial Life Algorithms

2007 ◽  
pp. 59-79
Author(s):  
Jorge Hurtado
Keyword(s):  
Support Vector Machines ◽  
Earthquake Engineering ◽  
Artificial Life ◽  
Numerical Models ◽  
Optimal Solution ◽  
Support Vector ◽  
Practical Application ◽  
Reliability Based Design ◽  
Vector Machines ◽  
Reliability Based Optimization

Reliability-based optimization is considered by many authors as the most rigorous approach to structural design, because the search for the optimal solution is performed with consideration of the uncertainties present in the structural and load variables. The practical application of this idea, however, is hindered by the computational difficulties associated to the minimisation of cost functions with probabilistic constraints involving the computation of very small probabilities computed over implicit threshold functions, that is, those given by numerical models such as finite elements. In this chapter, a procedure intended to perform this task with a minimal amount of calls of the finite element code is proposed. It is based on the combination of a computational learning method (the support vector machines) and an artificial life technique (particle swarm optimisation). The former is selected because of its information encoding properties as well as for its elitist procedures that complement hose of the a-life optimisation method. The later has been chosen du to its advantages over classical genetic algorithms. The practical application of the procedure is demonstrated with earthquake engineering examples.

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