Abstract
A cable-supported bridge is usually a key junction of a highway or a railway that demands a higher safety margin, especially when it is subjected to harsh environmental and complex loading conditions. In comparison to short-span girder bridges, long-span flexible structures have unique characteristics that increase the complexity of the structural mechanical behavior. Therefore, the system safety of cable-supported bridges is critical but difficult to evaluate. This study proposes a novel and intelligent approach for system reliability evaluation of cable-supported bridges under stochastic traffic load by utilizing deep belief networks (DBNs). The related mathematical models were derived taking into consideration the structural nonlinearities and high-order statically indeterminate characteristics. A computational framework is presented to illustrate the steps followed for system reliability evaluation using DBNs. In a case study, a prototype suspension bridge is selected to investigate the system reliability under stochastic traffic loading based on site-specific traffic monitoring data. The numerical results indicated that DBNs provide an accurate approximation for the mechanical behavior accounting for structural nonlinearities and different system behaviors, which can be treated as a meta-model to estimate the structural failure probability. The dominant failure modes of the suspension bridge are the fracture of suspenders followed by the bending failure of girders. The degradation of suspenders due to fatigue-corrosion damage has a significant effect on the system reliability of a suspension bridge. The numerical results provide a theoretical basis for the design on cable replacement strategies.
Highlights
The current transportation market, driven by a steady increase in global, is growing rapidly driven by a steady increase in the global economy, especially in developing countries [1]
This study proposes a novel and intelligent approach for system reliability evaluation of cable-supported bridges under stochastic traffic load by utilizing deep belief networks (DBNs)
A DBN is a new type of generative neural networks which are constructed by stacking restricted Boltzmann machines (RBMs) that are usually trained based on the probability principle [29]
Summary
The current transportation market, driven by a steady increase in global, is growing rapidly driven by a steady increase in the global economy, especially in developing countries [1]. Due to the structural flexibility of cable-supported bridges, wind load and seismic effect are critical factors affecting bridge safety [7,8]. Lu et al [16] utilized a machine learning approach to estimate the system reliability of a cable-stayed bridge considering cable damage due to fatigue and corrosion effects. The second critical problem is how to search for the failure sequence of a system efficiently In this regard, the most commonly used approach is the β-bound approach that utilizes the reliability index range to determine the components that are likely to fail. This study proposes a novel and intelligent approach for system reliability evaluation of cable-supported bridges under stochastic traffic load by utilizing DBNs. Mathematical models for the system reliability of cable-supported bridges were derived taking into consideration structural nonlinearities and high-order statically indeterminate characteristics. The dominant failure mode of the suspension bridge was determined, and the effect of cable degradation due to fatigue-corrosion damage on the system reliability of the suspension bridge was investigated
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