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A new computational method for structural reliability with Big Data

A new computational method for structural reliability with Big Data

Fang, Yongfeng, Tao, Wenliang and Tee, Kong Fah ORCID: 0000-0003-3202-873X (2019) A new computational method for structural reliability with Big Data. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 21 (1). pp. 159-163. (doi:https://doi.org/10.17531/ein.2019.1.18)

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Abstract

A new computational method for structural reliability based on big data is proposed in this paper. Firstly, the big data is collected via structural monitoring and is analyzed. The big data is then classified into different groups according to the regularities of distribution of the data. In this paper, the stress responses of a suspension bridge due to different types of vehicle are obtained. Secondly, structural reliability prediction model is established using the stress-strength interference theory under the repeated loads after the stress responses and structural strength have been comprehensively considered. In addition, structural reliability index is calculated using the first order second moment method under vehicle loads that are obeying the normal distribution. The minimum reliability among various types of stress responses is chosen as the structural reliability. Finally, the proposed method has been validated for its feasibility and effectiveness by an example.

Item Type: Article
Uncontrolled Keywords: big data, reliability, probability, stochastic loads, structure
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 25 Mar 2019 10:21
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/23180

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