Bridges are a critical component of transportation infrastructure, playing a vital role in connectivity. The safe operation of bridges demands significant resource and capital investment, particularly as the operation phase is the most extended period in a bridge’s life cycle. Therefore, the efficient allocation of resources and funds is crucial for the maintenance and repair of bridges. This study addresses the need to predict changes in bridge condition over time. The commonly used state-based Markov chain method for bridge condition rating prediction is straightforward but limited by its assumptions of homogeneity and memorylessness. To improve upon this, we propose a novel method that integrates an Elman neural network with a Markov chain to predict the bridge condition rating. Initially, the ReliefF algorithm conducts a sensitivity analysis on bridge features to obtain the importance ranking of these features that affect the bridge condition. Next, six significant features are selected for data classification: bridge age, average daily truck traffic volume, material type, skew angle between bridges and roads, bridge deck structure type, and bridge type. The Elman neural network is then trained to train a prediction model for bridge condition ratings using the classified data, which can predict the condition levels of bridges. The Markov chain’s transition probability matrix is derived using a genetic algorithm to match the deterioration curve predicted by the Elman neural network. This proposed method, when applied to actual bridge data, demonstrates its effectiveness as evidenced by the condition rating of an actual bridge.

With the development of bridge structures towards being light weight and having a large span , the overall flexibility, and, hence, wind sensitivity, of the bridge increases. Flutter is one of the pivotal factors considered in the design and operation stage for long-span cable-stayed bridges due to its devastating impact, often intrigued by relatively low instability caused by wind speed. This paper presents a reliability theory-based numerical analysis on bridge flutter stability and its influence law of key parameters using a real bridge, the Xiangshan Harbor highway cable-stayed bridge in China. The analysis starts with creating a full scale of finite element model for the bridge in service to calculate the flutter derivative and time-dominated combining rational function in order to obtain the critical-flutter wind speed, and then the aerodynamic self-excited forces on the bridge and flutter time-history response are calculated to identify the flutter critical wind speed. Further, the influence of key parameters for flutter reliability, including the stiffness of the main girder, wire breaking rate, damping ratio and cable breakage location are analyzed comprehensively to achieve the change law of critical flutter wind speed with these parameters. Considering the uncertainty of the actual parameters, these parameters are taken as random variables, and the reliability index and failure probability of bridge flutter are calculated according to their probability distribution and the Latin hypercube sampling method. On this basis, a few suggestions are put forward for flutter risk-control during the service of this cable-stayed bridge, which can further enhance the design theory for long-span flexible bridges.

Long-span cable-stayed bridge is a typical wind-sensitive structure because of its light weight and great flexibility. The reliability evaluation of the cable-stayed bridge under wind action becomes a key issue during bridge design and operation. In this paper, taking Xiangshan Harbor highway cable-stayed bridge in Ningbo City in China as the engineering background, ANSYS 15.0 is used to build a finite element model of this bridge to explore its aerostatic stability and influencing parameters, then the static wind reliability of this bridge is analyzed. Firstly, considering the structural nonlinearity and the nonlinearity of static wind load comprehensively, the critical wind speed of aerostatic instability of the Xiangshan Harbor cable-stayed bridge is calculated by the internal and external double iteration method. Then the influence of the initial wind attack angle, steel wire breakage rate of the stay cables, static three-component force coefficients, and cable broken position on the static wind stability of this bridge are discussed. Thus, the variation rule of the critical aerostatic instability wind speed is obtained, and the parameter that has the greatest impact on the static wind stability of the cable-stayed bridge in service is determined. Finally, considering the randomness of these key parameters, they are treated as random variables, Latin hypercube sampling (LHS) method is used to obtain samples of random variables to calculate aerostatic instability wind speed for a given sample by self-written calculation program, and on this basis, static wind reliability analysis is conducted by the checking point method, which provides strong support for the static wind risk evaluation of the existing long-span cable-stayed bridge.