An improved adaptive sequential nonlinear LSE with unknown inputs (ASNLSE-UI) approach was proposed to real-time-track the structural damage when it occurs for structural safety and management after emergency event. Experimental studies are presented to verify the capability of the improved ASNLSE-UI approach. A series of tests using a small-scale 3-story base-isolated building have been performed. White noise and earthquake excitations, applied to the base of the model, have been used. To simulate structural damages during the test, an innovative device is designed and manufactured to reduce the stiffness of some stories. With the measured response data of different damage scenarios, the improved ASNLSE-UI approach is used to track the variation of structural physical parameters. Besides, the unknown inputs are simultaneously identified. Experimental results demonstrate that the improved ASNLSE-UI approach is capable of tracking the variation of stiffness parameters leading to the detection of structural damages. 1. Introduction One objective of the structural health monitoring system is to track the structural damage when it occurs for structural safety and management after emergency event [1, 2]. When a structural element is damaged, such as cracking or incompletion, generally the stiffness of the damaged element is reduced [3]. Hence, the structural damage can be reflected by the changes of parametric values of the damaged element. During a severe dynamic event, such as earthquake, and typhoon, a structure may be damaged, leading to the stiffness reductions of the damaged elements; thus the measured vibration data contains the information of damage events. In this regard, data analysis techniques for real-time damage tracking, based on vibration responses measured by sensors, have received considerable attention [4, 5]. Various approaches for structural parameter identification and damage tracking have been reported [6–13]. In these traditional approaches, all the external excitations should be measured by sensors. In practical applications, however, external inputs, such as seismic excitations and wind loads, may be not measured or even may be unmeasurable. Therefore, it is highly desirable to achieve parameter identification and damage tracking without using the excitation information [14, 15]. In the area of system identification with unknown inputs, some frequency domain approaches and time domain approaches have been developed. The frequency domain approaches, such as frequency domain decomposition [16] and mode decomposition [17], mainly
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