基于导波奇异值向量的钢绞线应力检测方法研究

Research on the stress measurement method of steel strand based on singular value vector of guided wave

钢绞线是大跨度桥梁必不可少且最重要的受力构件之一,但受必需的防腐蚀措施影响,目前仍缺乏有效的在役桥梁钢绞线应力检测监测方法。超声导波在钢绞线中传播带有明显的应力特征,通过在时-频域内进行导波信号的小波包分解提取不同应力状态下小波包分解系数矩阵,并以系数矩阵的奇异值向量为特征参量,建立具有学习能力的支持向量回归模型检测钢绞线应力值。结果表明,导波的奇异值向量是有效的应力特征参量,逐级加载过程中奇异值向量距与钢绞线应力值呈单调线性变化关系;以奇异值向量构建的支持向量回归模型预测钢绞线应力,其结果确定系数达到0.9739,对比神经网络方法,支持向量回归模型应力预测结果更为稳定。

Steel strand is one of the most important and indispensable stress components in long-span bridges,however,there is still lack of effective method to detect and monitor the stress of the steel strand in existing bridges due to the influence of necessary anti-corrosion measures.The ultrasonic guided wave carries obvious stress characteristics in the propagation along the steel strand.Through the wavelet packet decomposition of the guided wave signal in the time-frequency domain,the coefficient matrix of wavelet packet decomposition is extracted under different stress states.Then,the singular value vector of the coefficient matrix is used as the characteristic parameter,the support vector regression model with learning ability is established to detect the stress value of the steel strand.The results show that the singular value vector of the guided wave is an effective characteristic parameter in presenting stress state,and the relationship between the singular value vector distance and steel strand stress value shows a monotonous linear law in stepwise loading process.While adopting the support vector regression model established with the singular value vector to predict the steel strand stress,the determination coefficient of the results reaches to 0.9739,the prediction result obtained using the support vector regression model is more stable than that using neural network method.