Volume 14, Issue 2 (2-2024)
IJOCE 2024, 14(2): 211-228 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Zakian P, Zakian P. PREDICTION OF NATURAL FREQUENCIES FOR TRUSS STRUCTURES WITH UNCERTAINTY USING THE SUPPORT VECTOR MACHINE AND MONTE CARLO SIMULATION. IJOCE 2024; 14 (2) :211-228
URL: http://ijoce.iust.ac.ir/article-1-583-en.html
URL: http://ijoce.iust.ac.ir/article-1-583-en.html
1- Department of Civil Engineering, Faculty of Engineering, Arak University, Arak, Iran
2- Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
2- Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
Abstract: (3575 Views)
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.
Type of Study: Research |
Subject:
Applications
Received: 2024/04/12 | Accepted: 2024/02/21 | Published: 2024/02/21
Received: 2024/04/12 | Accepted: 2024/02/21 | Published: 2024/02/21
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
