Failure Evaluation of Bridge Deck Based on Parallel Connection Bayesian Network: Analytical Model

Abstract

Failure is a major element that causes deterioration, which in turn affects the serviceability of long span bridges. Currently, the Bayesian network, which relates to probability statistics, is widely used for evaluating fatigue failure reliability. In particular, Bayesian network can not only calculate the fatigue failure at the system level, but also deduce the fatigue failure at the weld level. In this study, a system-level fatigue reliability evaluation model of a bridge deck (BD), which is seen as a parallel system, is proposed based on the Bayesian network. A fatigue probability reliability model of the BD was derived using the master S-N curve. In addition, the Monte Carlo (MC) method was applied to solve the multi-dimensional and complex analytical expressions in the Bayesian network. The applicability of the proposed model was demonstrated by three numerical case studies.

Identification of bridge cable force damage based on Bayesian inference

“…Then, the likelihood function of unknown parameter can be expressed by, 30,31 $$$$ p\left(x|\theta \right)=\prod \limits_{i=1}^Np\left({\omega}_i\mid \theta \right) $$$$ $$$$ {\varepsilon}_{\omega }={\omega}_i-{\omega}_i\left(\theta \right) $$$$ …”

“…In order to consider the influence of material physical property errors, environmental errors and finite element model calculation errors on cable force damage identification, Bayesian theorem can be used to calculate the uncertainty of errors. [30][31][32][33] For example, Chen et al 33 proposed a hierarchical Bayesian learning approach with sensitivity analysis for identifying structural damage with sparse features. The performance of the proposed method is demonstrated by two numerical examples and an experimental verification.…”

Damage Evaluation of Bridge Hanger Based on Bayesian Inference: Analytical Model

“…MCMC method, which contains Monte Carlo integration and Markov chain, is increasingly adopted to solve the complicated, intractable, and multidimensional posterior integration in the Bayesian method (Geyer [38]; Andrieu et al [39]). In the Bayesian method, the probabilistic inference needs the calculation of complex integrals or summations over very large outcome spaces, which can be expressed by (Ding et al [40])…”

Assessment of Performance Indicators of a Large-Span Reinforced Concrete Arch Bridge in a Multi-Hazard Environment

“…On the other hand, bridges with large spans or structures of high strategic importance usually have a longer service life and an extended assessment that includes not only a visual inspection but also several in-situ and laboratory tests, structural health monitoring, and optionally, a numerical analysis of the structural assessment, remaining service life and life cycle cost [1]. However, most of the published research papers address only one or a few aspects: e.g., structural assessment of bearing capacity in the combination of numerical analysis and experimental testing [4,5], experimental testing of a specific structural element or detail [6][7][8][9], evaluation and effectiveness of non-destructive testing [3,9,10], analytic models on failure modes [11], and numerical and experimental analysis of the main degradation mechanisms [12][13][14]. A comprehensive assessment of road bridges is included in [1], but in this case, the assessment is based on visual inspection and standard bridges of small and medium spans are analyzed.…”