6 April 2012 Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage
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Abstract
This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.
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Hae Young Noh, Ram Rajagopal, Anne S. Kiremidjian, "Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage", Proc. SPIE 8345, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012, 834507 (6 April 2012); doi: 10.1117/12.915409; https://doi.org/10.1117/12.915409
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KEYWORDS
Autoregressive models

Detection and tracking algorithms

Computer simulations

Algorithm development

Earthquakes

Data modeling

Feature extraction

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