Skip navigation

Statistical structural health monitoring using quantile regression

Statistical structural health monitoring using quantile regression

Tee, Kong Fah and Cai, Yuzhi (2012) Statistical structural health monitoring using quantile regression. In: 5th European Conference on Structural Control (EACS), 18-20 Jun 2012, Genoa, Italy.

Full text not available from this repository.

Abstract

Structural health monitoring is an important emerging engineering discipline in the UK and the world. Structural failure without warning is recognised as a significant hazard in the service life of a structure. Thus there is a need to provide a clear guidance in order to determine the cut-off line for operation, repair and maintenance. A quantile regression approach has been proposed for structural damage detection using vibration data (accelerations). This method is based on a sequence of quantile autoregressive time series models and the differences between two distributions associated with the residual series of the undamaged and damaged structures are studied at different quantile levels. This new approach is based on the information on damages at any quantile levels, not just at a mean level that is commonly used in the literature. In addition, it does not depend on the distribution of the error term. This is a very useful feature as in practice it can be very difficult to assume a proper distribution for the error term of the model. The performance of the developed method is investigated via extensive simulation studies to detect single-damage and multi-damage scenarios with input and output measurement noise. The numerical results have shown that the proposed method gives reasonably accurate damage identification.

Item Type: Conference or Conference Paper (Paper)
Uncontrolled Keywords: structural damage detection, quantile regression, autoregressive model, time series analysis, structural health monitoring
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Civil Engineering
Last Modified: 14 Oct 2016 09:24
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/9677

Actions (login required)

View Item View Item