Asset Details
Do you wish to reserve the book?
Simultaneous reliability and reliability-sensitivity analyses based on the information-reuse of sparse grid numerical integration
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Simultaneous reliability and reliability-sensitivity analyses based on the information-reuse of sparse grid numerical integration
Do you wish to request the book?
Simultaneous reliability and reliability-sensitivity analyses based on the information-reuse of sparse grid numerical integration
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Simultaneous reliability and reliability-sensitivity analyses based on the information-reuse of sparse grid numerical integration
2023
Overview
In this paper, a new method is put forward for simultaneous reliability and reliability-sensitivity analyses based on the information-reuse of sparse grid numerical integration (SGNI). First, the reliability analysis is conducted on the basis of fractional exponential moments-based maximum entropy method (FEM-MEM), where the SGNI is employed for FEM assessments. The reliability index can be evaluated by integrating over the distribution derived by FEM-MEM. Then, the reliability-sensitivity analysis is carried out by reusing the output samples in previous reliability analysis and updating the corresponding weights, where no additional model evaluations are required. Then, the FEM-MEM is applied again to derive the conditional distribution and reliability index. By comparing the conditional and original reliability indexes, one can define the reliability-sensitivity index to identify the importance of each random variable to reliability. Since only one-round of model evaluations are necessary in the proposed method, the computational efficiency is highly desirable. Four numerical examples are investigated to check the effectiveness of the proposed method, where pertinent results obtained from Monte Carlo simulations (MCS) and Sobol’s index are compared. The results demonstrate the proposed method is accurate and efficient for simultaneous reliability and reliability-sensitivity analyses.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
