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| Conjugate gradient step length adjustment method for calculation of probabilistic performance measure |
| Received:August 31, 2017 Revised:November 29, 2017 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20170831002 |
| KeyWord:performance measure approach conjugate gradient step length adjustment |
| Author | Institution |
| 易平 |
大连理工大学 建设工程学部, 大连 |
| 谢东赤 |
大连理工大学 建设工程学部, 大连 |
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| Abstract: |
| The performance measure approach(PMA)is suitable for the assessment of probability constraints in probabilistic structural design optimization(PSDO)due to its stable and efficient characteristics.The advanced mean value(AMV)is often used to solve the probabilistic performance measure in PMA.However,for highly nonlinear performance function the iterative sequence of AMV formulation may yield a non-convergent solution such as periodic oscillation and chaos.In this paper,a new method,conjugate gradient step length adjustment method(CGS),is presented.This method is based on the RMIL conjugate search direction and the self-adaptive step length strategy.The new conjugate search direction accelerates the iterative process under the premise of ensuring the convergence.The self-adaptive step length strategy makes it unnecessary to obtain the prior information such as convexity or concavity and non-linearity of the performance function,and to determine an appropriate value of the step length.The step length is initially automatically selected by step-length-limiting criterion and is constantly adjusted during the iteration process until the final convergence.Several examples show that the proposed CGS method is more efficient and robust than other methods. |