基于AR-GP模型的结构损伤识别方法

Structural damage identification method based on AR-GP model

针对传统损伤识别方法不易区分多损伤状态以及难以辨别预测结果可靠性的问题,提出了一种基于自回归(autoregressive,AR)模型和高斯过程(Gaussian process,GP)的损伤识别方法。该方法利用AR模型回归拟合结构加速度响应数据,首次引入表征损伤位置信息与损伤状态信息的参数L1、L2,基于AR模型残差、系数分别建立了用于定位损伤位置和识别损伤程度的损伤敏感性特征,结合GP的分类与回归算法实现了多损伤定位及损伤程度的概率输出。通过某钢筋混凝土模型拱的数值仿真算例,验证了所提方法的有效性,并对基于AR模型残差和系数的识别结果进行了对比分析。结果表明,该方法能够识别多损伤状态,输出具有概率意义的预测结果,有助于判断结果的可靠性,并能够实现损伤预警,同时基于残差的损伤敏感性特征的识别精度与可靠度更高、抗噪性能更好,在10%噪声污染的情况下,识别结果的相对误差均值与离散系数均值仅为6.52%和0.19。

Aiming at the problem of traditional methods for damage identification being difficult to distinguish multi-damage states and the reliability of prediction results,a method for damage identification was proposed based on autoregressive(AR)model and Gaussian process(GP).It was shown that this method uses AR model to fit acceleration response data of a structure,and for the first time,parameters L1 and L2 are introduced to characterize the information about damage location and damage level;based on residual and coefficient of AR model,damage sensitivity features for locating damage position and identifying damage degree are established,respectively;GP’s classification algorithm is combined with its regression algorithm to realize multi-damage location and probability output of damage degree.The effectiveness of the proposed method was verified with a numerical simulation example of a reinforced concrete model arch,and the identification results based on AR model’s residual and coefficient were analyzed contrastively.The results showed that the proposed method can identify multi-damage states and output prediction results with probabilistic significance,which is helpful to judge the reliability of the results and realize damage early warning;the identification accuracy and reliability of damage sensitivity feature based on residual are higher with better anti-noise performance;in the case of 10%noise pollution,the relative error mean and discrete coefficient mean of the identification results are only 6.52%and 0.19,respectively.