It is necessary to investigate techniques for monitoring structures under unknown earthquakes. For this purpose, an algorithm is proposed in this paper for the identification of structures and excitation of multi-story shear buildings with limited measurements of structural responses. The equation of motion of a multi-story shear building under ground motion is established in the absolute co-ordinate system, while the multi-story building is decomposed into substructures. A novel two-step Kalman estimator approach, which is not available in the previous literature, is proposed for the identification substructures and unknown ground motion with less computational effort. Then, for the purpose of intelligent structural monitoring, a new wireless sensor network is developed in this paper. The designed wireless sensor network has a two-level cluster-tree architecture. Hardware designs of the sensor unit and the cluster head are presented; especially the cluster head contains a low power digital signal processor with strong computing capacity. Thus, the wireless sensor network has the unique feature offers distributed computing at group level. Finally, the proposed algorithm is embedded into the wireless sensor network for intelligent structural monitoring and an experimental test shows the technique is effective for intelligent monitoring of multi-story buildings under unknown earthquakes. 1. Introduction In the past decades, many structural identification and structural damage detection algorithms have been proposed, for example, see [1, 2]. Since some structural external excitations such as earthquakes or wind forces cannot be accurately measured under actual operating conditions, it is necessary to investigate algorithms for structural identification and damage detection under unknown earthquake excitation. Also, in structural health monitoring, the knowledge of external excitation is very useful for the safety evolution of structures. Identification of structural parameters and unknown external excitation has been investigated by some researchers, for example, Wang and Haldar [3] developed iterative least squares approaches to identify simultaneously the structural parameters and ground motion of an earthquake. Chen and Li [4] proposed a statistical average algorithm for simultaneous estimation of structural parameters and earthquake-induced ground motion. Yang et al. presented several methods including extended Kalman filter with unknown excitation inputs (EKF-UI) [5] and the sequential nonlinear least square estimation (SNLSE) [6] for the
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