Structural Deterioration Localisation Using Enhanced Autoregressive Time-Series Analysis

Monavari, Benyamin and Chan, Tommy H. T. and Nguyen, Andy and Thambiratnam, David P. and Nguyen, Khac-Duy (2020) Structural Deterioration Localisation Using Enhanced Autoregressive Time-Series Analysis. International Journal of Structural Stability and Dynamics, 20 (10):2042013. 2042013-1-2042013-27. ISSN 0219-4554

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Abstract

Irrespective to how well structures were built, they all deteriorate. Herein, deterioration is defined as a slow and continuous reduction of structural performance, which if prolonged can lead to damage. Deterioration occurs due to different factors such as ageing, environmental and operational (E&O) variations including those due to service loads. Structural performance can be defined as load-carrying capacity, deformation capacity, service life and so on. This paper aims to develop an effective method to detect and locate deterioration in the presence of E&O variations and high measurement noise content. For this reason, a novel vibration-based deterioration assessment method is developed. Since deterioration alters the unique vibration characteristics of a structure, it can be identified by tracking the changes in the vibration characteristics. This study uses enhanced autoregressive (AR) time-series models to fit the vibration response data of a structure. Then, the statistical hypotheses of chi-square variance test and two-sample t-test are applied to the model residuals. To precisely evaluate changes in the vibration characteristics, an integrated deterioration identification (DI) is defined using the calculated statistical hypotheses and a Hampel filter is used to detect and remove false positive and negative results. Model residual is the difference between the predicted signal from the time series model and the actual measured response data at each time interval. The response data of two numerically simulated case studies of 3-storey and 20-storey reinforced concrete (RC) shear frames contaminated with different noise contents demonstrate the efficacy of the proposed method. Multiple deterioration and damage locations, as well as preventive maintenance actions, are also considered in these case studies. Furthermore, the method was successfully verified utilizing measured data from an experiment carried out on a box-girder bridge (BGB) structure.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Date Deposited: 23 Nov 2020 04:44
Last Modified: 01 Dec 2020 00:03
Uncontrolled Keywords: Deterioration identification; AR residual; SHM; Vibration-based, Statistical hypothesis
Fields of Research (2008): 09 Engineering > 0905 Civil Engineering > 090506 Structural Engineering
Fields of Research (2020): 40 ENGINEERING > 4005 Civil engineering > 400510 Structural engineering
Identification Number or DOI: https://doi.org/10.1142/S0219455420420134
URI: http://eprints.usq.edu.au/id/eprint/40121

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