An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge

Yang Liu, Naiwei Lu, Xinfeng Yin and Mohammad Noori

Journal of Risk and Reliability, 2016, vol. 230, issue 2, 204-219

Abstract: Engineering structures are most statically indeterminate structures consisting of various types of components and their failure modes exhibit randomness under random loads. A new adaptive support vector regression method is proposed for structural system reliability assessment. Compared with traditional support vector regression, the proposed adaptive support vector regression method involves two updating procedures to estimate structural limit state functions. Three verification examples involving a nonlinear limit state function, a truss bridge structure, and a geometrically nonlinear suspended structure are provided to illustrate the accuracy and the efficiency of the adaptive support vector regression method. A pre-stressed concrete cable-stayed bridge is utilized to demonstrate the applicability of the proposed method. The verification studies show that the proposed adaptive support vector regression method is an efficient method with reasonable accuracy for problems where closed-form failure functions are not available and the failure sequences exist in the structural system. The main failure sequences of the cable-stayed bridge are identified. The application studies of the cable-stayed bridge indicate that (a) the foremost failure sequence is the strength failure of side-span cables followed by the bending failure of towers and (b) the secondary failure sequence is the strength failure of mid-span cables followed by bending failure of mid-span girders.

Keywords: Reliability engineering; sequential failures; structural reliability; system failure modeling; simulation; support vector regression; limit state function; failure sequence; Monte Carlo simulation; cable-stayed bridge (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:230:y:2016:i:2:p:204-219

DOI: 10.1177/1748006X15623869

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