提出一种完全基于激励和结构响应实测数据的结构动力系统非线性恢复力识别方法，并通过在一个4层钢结构模型中引入具有非线性特性的磁流变阻尼器（MR）模拟非线性恢复力，基于此模型结构在不同的激励方式下的动力响应测量数据，验证了该方法的有效性。对于结构的各自由度均受到激励的情况，运用最小二乘拟合算法识别出等效线性系统的物理参数（质量、刚度和阻尼矩阵），进而得到模型结构振动过程中MR阻尼力随时间变化情况并与实验实测结果进行了比较。针对结构仅在有限自由度上受到激励的情况，对以上方法进行了改进，提出了一种非线性系统恢复力的非参数化识别方法，利用结构中弹性恢复力的对称关系，分步确定了结构各层间恢复力模型，从而得到MR恢复力的大小并与实测结果进行了比较。结果表明，基于时域实测信号的非线性系统恢复力识别法在完整激励和非完整激励下均能有效地识别结构的非线性恢复力特性。文章所述方法可以运用于工程结构在动力荷载作用下的损伤发生发展过程的监测与识别。

In this study, a general nonlinear restoring force (NRF) identification approach using structural dynamic response measurements and complete excitations is proposed at first. In this approach, the least-squares technique is employed to identify the parameters of an equivalent linear system of the nonlinear structure model based on the external excitations and the corresponding response measurements. The proposed approach is developed when the structure to be identified is incompletely excited. Both of the approaches are validated with a 4-story frame structure equipped with smart devices of magneto-rheological (MR) damper to simulate nonlinear performance. The identified NRF of the structure is compared with the test measurements. Results show that the proposed data-based approaches are capable of identifying the nonlinear restoring behavior of engineering structures and have the potential to be employed to evaluate the damage initiation and development procedure of engineering structures under dynamic loads.