PREDICTION OF NATURAL FREQUENCIES FOR TRUSS STRUCTURES WITH UNCERTAINTY USING THE SUPPORT VECTOR MACHINE AND MONTE CARLO SIMULATION

The main objective of this paper is to optimize the size and layout of planar truss structures In this study, the support vector machine and Monte Carlo simulation are applied to predict natural frequencies of truss structures with uncertainties. Material and geometrical properties (e.g., elasticity modulus and cross-section area) of the structure are assumed to be random variables. Thus, the effects of multiple random variables on natural frequencies are investigated. Monte Carlo simulation is used for probabilistic eigenvalue analysis of the structure. In order to reduce the computational cost of Monte Carlo simulation, a support vector machine model is trained to predict the required natural frequencies of the structure computed in the simulations. The provided examples demonstrate the computational efficiency and accuracy of the proposed method compared to the direct Monte Carlo simulation in the computation of the natural frequencies for trusses with random parameters.

Keywords:

Machine learning , support vector machine , truss , random eigenvalue problem , uncertainty quantification , monte carlo simulation.