Abstract
For long-span bridges located in wind-prone regions, it is a trend to install in situ Wind and Structural Health Monitoring System (WASHMS) for long-term real-time performance assessment. One of the functions of the WASHMS is to provide information for the assessment of wind-induced fatigue damage. Considering the randomness of wind, it is more reasonable to describe wind-induced fatigue damage of bridge in a probabilistic way. This paper aims to establish a probabilistic fatigue model of fatigue damage based on Bayesian learning, and it is applied to a wind-excited long-span bridge installed with a WASHMS. Wind information recorded by the WASHMS is utilized to come up with the joint probability density function of wind speed and direction. A stochastic wind field and subsequently wind-induced forces are introduced into the health monitoring oriented finite element model (FEM) of the bridge to predict the statistics of stress responses in local bridge components. Bayesian learning approach is then applied to determine the probabilistic fatigue damage model. The Tsing Ma suspension bridge in Hong Kong and its WASHMS are finally utilized as a case study. It shows that the proposed approach is applicable for the probabilistic fatigue assessment of long-span bridges under random wind loadings.
Highlights
To meet social and economic needs of the community for efficient and convenient transportation systems, many longspan bridges have been built throughout the world, and the super long suspension bridges with main span length beyond 3000 meters are under consideration
Li et al [29] used the strain data recorded by the wind and structural health monitoring system (WASHMS) installed on the Tsing Ma Bridge to assess fatigue damage of the structural members at the strain gauge points for a single typhoon
This paper aims to establish a probabilistic fatigue model of fatigue damage based on Bayesian learning, and it is applied to a wind-excited long-span bridge with multiple types of sensors installed on it
Summary
To meet social and economic needs of the community for efficient and convenient transportation systems, many longspan bridges have been built throughout the world, and the super long suspension bridges with main span length beyond 3000 meters are under consideration. Frequent occurrence of the buffeting may cause fatigue damage of steel girders or other structural members of the long-span bridge. To protect such immense capital investments and ensure user comfort and bridge safety during the serviceability stage, structural health monitoring systems (SHMSs) have been installed on bridges to monitor their integrity, durability, and reliability. Li et al [29] used the strain data recorded by the wind and structural health monitoring system (WASHMS) installed on the Tsing Ma Bridge to assess fatigue damage of the structural members at the strain gauge points for a single typhoon. The Tsing Ma suspension Bridge in Hong Kong and the data recorded by the WASHMS installed in the bridge are utilized as a case study
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