Comparison of Structural Probabilistic and Non-Probabilistic Reliability Computational Methods under Big Data Condition
- Yongfeng Fang1,3, Kong Fah Tee2,*
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:
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:
Received 16 November 2021; Accepted 25 February 2022; Issue published 25 April 2022
Abstract
In this article, structural probabilistic and non-probabilistic reliability have been evaluated and compared under big data condition. Firstly, the big data is collected via structural monitoring and analysis. Big data is classified into different types according to the regularities of the distribution of data. The different stresses which have been subjected by the structure are used in this paper. Secondly, the structural interval reliability and probabilistic prediction models are established by using the stress-strength interference theory under big data of random loads after the stresses and structural strength are comprehensively considered. Structural reliability is computed by using various stress types, and the minimum reliability is determined as structural reliability. Finally, the advantage and disadvantage of the interval reliability method and probability reliability method are shown by using three examples. It has been shown that the proposed methods are feasible and effective.
Keywords
Big data; structure; interval reliability; probabilistic reliability; reliability index
Cite This Article
Fang, Y., Tee, K. F. (2022). Comparison of Structural Probabilistic and Non-Probabilistic Reliability Computational Methods under Big Data Condition. Structural Durability & Health Monitoring, 16(2), 129–143.
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