Time-series coefficient-based deterioration detection algorithm

Monavari, B. and Chan, T. H. T. and Nguyen, A. and Thambiratnam, D. P. (2018) Time-series coefficient-based deterioration detection algorithm. In: 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII 2017): Structural Health Monitoring in Real-World Application, 5-8 Dec 2017, Brisbane, Australia.


Regardless how well structures were designed, all existing buildings deteriorate over time due to environmental effects, varying service loads and aging. Hence, it is importent to continuously track the deterioration condition of structures. Structural health monitoring (SHM) based assessment procedures can be employed to evaluate this. In this regard, vibration-based methods are amongst the most effective ones as they can be used in ambient vibration and operational loading conditions. Each building has a unique set of vibration characteristics which change with accumulated deterioration and damage. However, the changes due to deterioration are generally subtler than changes due to damage; consequently, more difficult to detect. Therefore, deterioration detection procedures need to be more accurate and sensitive to these changes. This paper presents a nonparametric statistical test to capture changes in dynamic characteristics of structures using SHM data. First, coefficients of an autoregressive (AR) time-series model are made to represent the vibration response data of a structure. Then, statistical hypotheses of Two-sample t-tests are performed and a function of the resulting P-values is used to detect changes in the structure due to deterioration. A novel AR model order estimation procedure was proposed in order to enhance the sensitivity of the method. The proposed vibration-based deterioration identification method was successfully verified utilizing sensor data from an experiment carried out on a bookshelf structure. Results show that the proposed methodology can clearly detect deterioration.

Statistics for USQ ePrint 36676
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. This conference is the largest conference in the field of Structural Health Monitoring. Its proceedings are indexed by Scopus and ranked in Scimago: https://www.scimagojr.com/journalsearch.php?q=International+Conference+on+Structural+Health+Monitoring+of+Intelligent+Infrastructure
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: 05 Jul 2019 02:09
Last Modified: 11 Feb 2020 23:54
Uncontrolled Keywords: structural health monitoring; damage detection; damage sensitive
Fields of Research (2008): 09 Engineering > 0905 Civil Engineering > 090506 Structural Engineering
Fields of Research (2020): 40 ENGINEERING > 4005 Civil engineering > 400510 Structural engineering
URI: http://eprints.usq.edu.au/id/eprint/36676

Actions (login required)

View Item Archive Repository Staff Only