Damage Location of Runyang Cable-stayed Bridge Based on BP Neural Network
The damage location of long span bridge remains a challenge. This paper alms to develop a damage location method based on BP neural network to diagnose the cable damage of a long span cablestayed bridge (Runyang North Bridge). First the damage patterns are defined based on plentiful dynamical calculation. The careful analysis of damage pattern reveals that the damage patterns caused by different damage location appear inherent distinctness, while the damage extent only linearly amplifies the damage pattern curves. And the fourth, sixth and seventh frequencies are canceled form the patterns because of the insensitiveness to cable damage. Then a Back- Propagation neural network is designed by trail and error to describe the 7 dimensions mapping space of damage pattern. Identification results prove that the properly organized Back-Propagation network could effectively grasp the damage pattern and identify the damage location correctly.
Cable-staved bridge Back-Propagation neural network Damage location
Jie Yang Aiqun Li Changqing Miao
College of Civil Engineering, Southeast University, Nanjing, China Department of Civil Engineering, College of Civil Engineering, Southeast University, Nanjing, China
国际会议
南京
英文
777-784
2007-10-16（万方平台首次上网日期，不代表论文的发表时间）