Identification And Modeling Of Staycable Tension And Vehicle Loads Of Long-span Cable-stayed Bridges

With rapid development of Chinese economics, traffic volume keeps increasing. The magnitude and frequency of vehicle loads on bridges are rising, which causes serious damages to bridges. There are many collapse accidents of bridges in last decade. It’s urgent to identify vehicle loads and to investigate and update their statistical characteristics. The development and application of structural health monitoring system make it possible to monitoring and identifying vehicle loads on bridge real-time. Meanwhile, up to date and larger amount of data are available from structural health monitoring systems which is a solid foundation of the extreme value research of vehicle loads. The previous researches of moving loads identification paid much attention to the problem on beam bridges and slab bridges. However, there was lack of information on the problem of moving force identification on cable-stayed bridges, and the vehicle loads position must be determined in advance to carry out identification. The existing cable force identification methods were limited to determine the mean value of cable tension during a certain period. To solve these problems, this dissertation is devoted to a relatively systematic study on vehicle loads, with emphasis on, identification of time-varying cable force caused by traffic loads, identification of moving forces and their locations, and vehicle loads modeling and extreme value prediction on the cable-stayed bridge.The main research works are outlined as following:First, the identification method of time-varying cable force using extended Kalman filter is proposed, and it is verified through numerical simulation and model experiment in laboratory. Taking the support displacement into consideration, the equation of motion in augmented state space and measurement equation is derived. Based on that, cable time-varying tension identification algorithm is developed based on measured acceleration of the stay cable using extended Kalman filter. The precision and robustness of proposed method is investigated by numerical simulation based on one cable on Nanjing Yangtze River No.3Bridge. Furthermore, a model experiment is conducted to verify the accuracy of proposed method.Second, an identification approach of vehicle loads on cable-stayed bridge is developed based on monitored vibration of one stay cable. The process equation and measurement equation of the augmented state variable including vehicle weight speed and arrival time is derived, based on which, the identification method of vehicle weight, speed and arrival time is presented. In order to demonstrate the accuracy of proposed method, the numerical simulation is conducted based on Nanjing Yangtze River No.3Bridge.Third, an identification approach of moving forces and their locations on the cable-stayed bridge is established based on the monitored cable forces. The relationship equation between vehicle loads and cable forces of stay cables is established first, and then the Tikhonov regularization is adopted to overcome the ill-poseness of the equation. Furthermore, the error bound of the identification result is investigated using interval analysis theory.An identification approach of moving forces and their spatial distributions on cable-stayed bridges is derived from sparse signal reconstruction theory based on monitored cable tension. The vehicle loads and cable tension of stay cables is connected by a linear equation, and then the sparse signal reconstruction theory is introduced to solve this seriously underdetermined equation, from which the vehicle loads and their spatial distributions can be identified. The numerical simulation based on Nanjing Yangtze River No.3Bridge is conducted to verify the accuracy and robustness of the proposed method.Finally, based on the above research on vehicle loads identification, using the field data from structural health monitoring system on Nanjing Yangtze River No.3Bridge, the probability distribution of the inter-arrival-time in normal status and in dense status are statistically analyzed separately. And the extreme value distribution theorem for any homogeneous renewal process is derived. The probability distributions of vehicle weights in normal status and dense status are investigated separately. And based on the formula derived above, the extreme value distributions of vehicle loads in normal status and dense status are predicted. The stochastic model of the tail part of vehicle loads is investigated, and one semi-parametric probability model with high accuracy is established. Using this tail model, the weekly extreme value distribution is predicted, and then it is compared with in-field data to verify the accuracy of the proposed model and method.