Small probability failure structure optimization based onreliability index(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2021年1期
- Page:
- 50-58
- Research Field:
- 桥梁与隧道工程
- Publishing date:
Info
- Title:
- Small probability failure structure optimization based onreliability index
- Author(s):
- WU Jiadong1; YAN Donghuang1; XU Hongsheng1; CHEN Xingye1; LYU Wenshu2
- (1. School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, China;〖JP〗2. CCCC Water Transportation Planning and Design Institute Co., Ltd., Beijing 100020, China)
- Keywords:
- bridge engineering; structure optimization; small probability failure; reliability; RBF neural network; genetic algorithm
- PACS:
- -
- DOI:
- -
- Abstract:
- Aiming at the problem of reliability calculation and optimization of small probability failure structures with implicit function in practical engineering, a method was proposed for reliability calculation and optimization design of small probability failure structures. The RBF neural network was firstly adopted to construct the implicit function of the structure, and an adaptive random variable was introduced to improve the genetic algorithm, which was used to search the optimal solution and checking points of the reliability index, according to the geometric meaning of the reliability so as to solve the reliability index. Besides, genetic algorithm with adaptive random variables was employed as the main program, and the RBF neural network was called to construct the implicit relationship between optimization variables and structural reliability for the main program to optimize the structure. Aizhai Bridge steel truss was taken as an example. The results show that the introduction of adaptive random variables obviously improves the quality of the initial population and speeds up the convergence rate of genetic algorithm. In addition, based on the geometric meaning of the reliability, the relative error between the reliability method mentioned above and MonteCarlo method is only 0.33%. Also, the convergence speed and calculation accuracy of the improved genetic algorithm are obviously improved, which proved that the method has the advantages of strong robustness, fast calculation speed, strong applicability and high precision. At last, two optimization models are used to optimize steel trusses and their results show that the area and section height of steel trusses should be increased, while the section area of vertical and horizontal beams should be reduced appropriately. Moreover, the quality of the steel trusses is reduced by 14.2%, when ensuring the reliability of the structure and the reliability index of steel truss has been improved from 4.821 2 to 5.912 4 under the condition of good quality. 5 tabs, 8 figs, 26 refs.
Last Update: 2021-01-25