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

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 sidespan 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.

Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration

“…Modeling the ambient influence on the structural health indicators is crucial for reliable structural health monitoring (Brownjohn, 2007; Giagopoulos et al., 2019; Kuok & Yuen, 2012; Li, Yi, Ren, Li, & Huo, 2014; Liu, Lu, Yin, & Noori, 2016; Mu, Yuen, & Kuok, 2016). Due to the nature of complex and volatile mechanism, constructing a representative parametric input–output relationship to describe the subtle environmental effects on materials properties and structural behavior under severe wind loading can be a nontrivial task (Catbas, Susoy, & Frangopol, 2008; Kuok & Yuen, 2016; Lam, Zhang, Ni, & Hu, 2017; Lei, Su, & Shen, 2013).…”

System Reliability Evaluation of Prefabricated RC Hollow Slab Bridges Considering Hinge Joint Damage Based on Modified AHP

“…Wang et al [31] proposed a semi-analytical method to assess the system reliability of a series bridge network subjected to non-stationary loads and investigated the sensitivity of system reliability to the load intensity, number of components and resistance correlation. Liu et al [32] proposed an adaptive support vector regression method for system reliability assessment and utilized a prestressed concrete (PC) cable-stayed bridge to verify the applicability of this method. Significant researches have been devoted to reliability evaluation of complex bridge system.…”

A convex model approach for structure non-probabilistic reliability analysis

“…From the investigation of the reliability by Liu et al, 20 Vapnik 21 and Li et al, 22 it is found that the application of SVM in reliability analysis needs to be researched deeply. In order to estimate the reliability of implicit performance function that encountered in structural safety assessment, in this article, the non-probabilistic reliability index of the bridge crane metal structure system is derived by convex model, and then, the sample of the stress and uncertainty parameters of the structural danger point can be obtained by experiment.…”