中文摘要：

对环境激励的响应信号采用数据驱动随机子空间算法得到随机状态空间模型的状态矩阵A，并作为损伤敏感特征。为克服状态矩阵A的多样性问题，构造了非奇异线性变换矩阵T，将状态矩阵转换为能控标准型，同时将状态矩阵分解成，n个子系统（m为测点数）。为克服测试误差、噪声、环境因素的影响，引入统计模式识别技术，对每一个子系统的状态向量构造Mahalanobis距离判别函数，并定义口损伤指标，对结构进行损伤识别及损伤定位。数值实验及预应力简支梁实验验证了该方法的有效性。

英文摘要：

In this paper, the data-driven stochastic subspace identification （SSI） algorithm is used to identify the state matrix A as a damage-sensitive feature. In order to overcome the variety of the state matrix A, a non-singularity transposition matrix T is build. By using matrix T, through which, matrix A is converted to its observability standard matrix. Then, matrix A is decomposed into rn entries subsystems （rn is the measure point number）. In order to overcome the effects of computing model err, noise, environmental variability on the measured dynamics response of structures, the statistical pattern recognition paradigm is introduced to the thesis, and the Mahalanobis distance decision functions of the damage-sensitive feature vector are adopted, and the fl Novelty Index （NI） is defined. The efficiency of the method is verified using vibration measured data obtained from simulated simple beams and pre-stressed concrete beams which were tested in the laboratory under ambient excitations.