Performance of global metamodeling techniques in solution of structural reliability problems

Solution of structural reliability and uncertainty propagation problems can be a computationally intensive task, since complex mechanical models have to be solved thousands or millions of times. In this context, surrogate models can be employed in order to reduce the computational burden. This article compares the performance of three global surrogate modeling techniques in the solution of structural reliability problems. The paper addresses artificial neural networks, polynomial chaos expansions and Kriging metamodeling. Analytical and numerical problems of increasingly complexity are addressed, including an eight-hundred bar, 3D steel lattice tower. Implementation strategies concerning data mapping and optimization of Kriging hyper parameters are proposed and discussed. Advantages and limitations of each technique are addressed. Results show that the three techniques explored herein are reliable tools for approximating the response of complex mechanical models.