Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27136
Title: System identification and structural damage detection : EMD + HT approach
Authors: Xu, YL 
Issue Date: 2006
Source: Structural health monitoring and intelligent infrastructure - Proceedings of the 2nd International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2005, 2006, v. 1, p. 207-217 How to cite?
Abstract: Urban centers display complex interactions between diverse large civil engineering structures and harsh environments such as strong winds and severe earthquakes. The large stock of civil engineering structures under harsh environments will have such problems as physical aging and deterioration, functional obsolescence, high-cost of maintenance, and structural failure. There is a practical and urgent need for effective and reliable system identification and structural damage detection methods to evaluate the structures. This paper summaries research work recently carried out at The Hong Kong Polytechnic University in system identification and structural damage detection using empirical mode decomposition (EMD) and Hubert transform (HT) approach. The research work includes identification and characterization of turbulent winds over complex terrain using EMD, identification of varying dynamic characteristics oftall buildings and long span bridges subject to strong winds using EMD + HT, and damage detection of sudden change of structural stiffness using EMD. These research activities involve not only analytical studies but also laboratory investigations and field measurements. The research results show that the EMD + HT approach is a promising method in system identification and structural damage detection although full understanding of this method has not been reached yet.
Description: 2nd International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2005, Shenzhen, 16-18 November 2005
URI: http://hdl.handle.net/10397/27136
ISBN: 0415396506
9780415396509
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