Title: Damage Identification of Structures Based on Pattern Classification Using Limited Number of Sensors Authors:

This paper proposes a nondestructive testing technique based on modal analysis in order to develop a new, efficient and simple damage detection method for civil structures. Structural identification for health monitoring involves comparison of changes in structural properties or response, and it can be viewed as pattern classification problems. However, a very large database is required to store training data for complicated damage cases if no technique to reduce the size is used. This paper presents an approach with the purpose of using the least possible sensors. The structural damage location and damage extent are identified respectively by two different methods of pattern classification, that are, the Parzen-window method for the structural damage location firstly and the feed-forward back-propagation neural network used to identify damage extent secondly. The results of numerical simulations show that our proposed approach can indeed identify the structural damage using small number of sensors. Finally, a series of vibration experiments for a 5-story shear frame structure were performed to verify the performance of our proposed approach.