Open Access
Retraction: Comparison of Structural Probabilistic and Non-Probabilistic Reliability Computational Methods under Big Data Condition
1 School of Physics and Electronic Information Engineering, Ningxia Normal University, Guyuan, 756000, China
2 School of Engineering, University of Greenwich, Kent, ME4 4TB, UK
3 School of Mechanical Engineering, Guizhou University of Engineering Science, Bijie, 551700, China
* Corresponding Author: Kong Fah Tee. Email:
Structural Durability & Health Monitoring 2025, 19(3), 771-771. https://doi.org/10.32604/sdhm.2024.061036
Issue published 03 April 2025
Abstract
This article has no abstract.The published article titled “Comparison of Structural Probabilistic and Non-Probabilistic Reliability Computational Methods under Big Data Condition” [1] has been retracted from Structural Durability & Health Monitoring (SDHM), Vol. 16, No. 2, 2022, pp. 129–143.
DOI: 10.32604/sdhm.2022.020301
URL: https://www.techscience.com/sdhm/v16n2/47591
This retraction follows a request by the authors, who acknowledged that the main content of the article had been previously submitted and published in Chinese Quarterly of Mechanics [2]. The current article is largely a translated version of the earlier work published in Chinese Quarterly of Mechanics, which violates SDHM’s policy on duplicate submissions.
As a result, the article is being retracted with the approval of the Editor-in-Chief and the Editorial Office of SDHM. All authors have agreed to the retraction of this article.
References
1. Fang Y, Tee KF. Comparison of structural probabilistic and non-probabilistic reliability computational methods under big data condition. Struct Durab Health Monit. 2022;16(2):129–43. doi:10.32604/sdhm.2022.020301.
2. Fang Y, Tao W, Gao Y. Comparison between the structural interval reliability and probabilistic reliability under the big data condition. Chin Quarter Mechanic. 2020;41(3):582–9. doi:10.15959/j.cnki.0254-0053.2020.03.020.
Cite This Article
Copyright © 2025 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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