AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (2.4 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Enhanced dung beetle optimizer for Kriging-assisted time-varying reliability analysis

Yunhan Ling1Yiqing Shi1Huimin Hou1Lidong Pan1Hao Chen1Peixin Liang1Shiyuan Yang2( )Peng Nie3,4Jiahao Han3,4Debiao Meng3,4
China Academy of Machinery Beijing Research Institute of Mechanical & Electrical Technology Co., Ltd., Beijing 100083, China
INEGI, Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, China
Show Author Information

Abstract

During the engineering structure's operation, the mechanical structure's performance and loading will change with time, so the parameter uncertainty and structural reliability will also have dynamic characteristics. The time-varying reliability analysis method can more accurately evaluate structural reliability by fully using this dynamic uncertainty. However, the time-varying reliability analysis was mainly based on the spanning rate method, which was complex and difficult to obtain the final result. Therefore, this study proposed an enhanced dung beetle optimization (EDBO) assisted time-varying reliability analysis method based on the adaptive Kriging model. With the help of the adaptive Kriging model and the EDBO optimization algorithm, the efficiency of the time-varying reliability analysis method was improved. At the same time, to prevent prematurely falling into the local search trap, the method improved the uniformity of the sample by initializing the sample through improved tent chaotic mapping (ITCM). Next, the Gaussian random walk strategy was used to search the updated position, which further improved the accuracy of the reliability analysis results. Finally, the accuracy and effectiveness of the proposed time-varying reliability analysis method were verified by four mechanical structure model examples. From the calculation results, it can be seen that with the help of the new DBO optimization algorithm, the relative error of the proposed reliability analysis results was about 20%~30% lower than that of the traditional reliability analysis method. What's more, the calculation efficiency was higher than that of other reliability analysis methods.

CLC number: 60K10, 62N05, 90C23

References

【1】
【1】
 
 
AIMS Mathematics
Pages 29296-29332

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Ling Y, Shi Y, Hou H, et al. Enhanced dung beetle optimizer for Kriging-assisted time-varying reliability analysis. AIMS Mathematics, 2024, 9(10): 29296-29332. https://doi.org/10.3934/math.20241420

115

Views

1

Downloads

5

Crossref

4

Web of Science

5

Scopus

Received: 11 August 2024
Revised: 13 September 2024
Accepted: 19 September 2024
Published: 15 October 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)