Volume 13, Issue 1 (1-2023)
IJOCE 2023, 13(1): 73-92 |
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:
Ghorbanzadeh M, Homami P, Shahrouzi M. A TRUST-REGION SEQUENTIAL QUADRATIC PROGRAMMING WITH NEW SIMPLE FILTER AS AN EFFICIENT AND ROBUST FIRST-ORDER RELIABILITY METHOD. IJOCE 2023; 13 (1) :73-92
URL: http://ijoce.iust.ac.ir/article-1-542-en.html
URL: http://ijoce.iust.ac.ir/article-1-542-en.html
1- Civil Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran
Abstract: (6255 Views)
The real-world applications addressing the nonlinear functions of multiple variables could be implicitly assessed through structural reliability analysis. This study establishes an efficient algorithm for resolving highly nonlinear structural reliability problems. To this end, first a numerical nonlinear optimization algorithm with a new simple filter is defined to locate and estimate the most probable point in the standard normal space and the subsequent reliability index with a fast convergence rate. The problem is solved by using a modified trust-region sequential quadratic programming approach that evaluates step direction and tunes step size through a linearized procedure. Then, the probability expectation method is implemented to eliminate the linearization error. The new applications of the proposed method could overcome high nonlinearity of the limit state function and improve the accuracy of the final result, in good agreement with the Monte Carlo sampling results. The proposed algorithm robustness is comparatively shown in various numerical benchmark examples via well-established classes of the first-order reliability methods. The results demonstrate the successive performance of the proposed method in capturing an accurate reliability index with higher convergence rate and competitive effectiveness compared with the other first-order methods.
Type of Study: Research |
Subject:
Optimal analysis
Received: 2022/11/20 | Accepted: 2022/11/19 | Published: 2022/11/19
Received: 2022/11/20 | Accepted: 2022/11/19 | Published: 2022/11/19
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
