摘要
采用小波分析对获得的结构动力响应进行小波分解,根据各种响应信号对损伤的灵敏度选择损伤特征,从而识别结构多次出现损伤的时刻,实现对结构损伤时刻的监控;对结构第1层加速度响应信号做小波包分解,得到各频段能量的特征向量,作为特征参数输入到BP神经网络中实现结构多处损伤位置和程度识别。模拟算例表明,小波分析和BP神经网络联合运用能准确地诊断结构多处损伤的时刻、位置和程度,具有一定的可行性。
This paper decomposes the dynamics response of a structure using wavelet analysis method,and the damage character is selected by comparing the damage susceptibility of every response signal. By using wavelet packet decomposing, the quantity of energy over frequency band of the acceleration response of the first story is chosen as the characteristic vector to identify damage, and is used as input variable for BP neural network. The damage time and location in a shear-type structure are monitored using wavelet analysis and BP neural network. Numerical example shows that the position and degree of damage are accurately identified as well as the damage moment,which proves the proposed method is feasible.
出处
《四川建筑科学研究》
北大核心
2006年第3期67-71,共5页
Sichuan Building Science
关键词
损伤监测
小波分析
神经网络
结构健康监测
damage monitoring
wavelet analysis
neural network
structural health monitoring
