Structural Deterioration Detection Using Enhanced Autoregressive Residuals
Article
Article Title | Structural Deterioration Detection Using Enhanced Autoregressive Residuals |
---|---|
ERA Journal ID | 3766 |
Article Category | Article |
Authors | Monavari, Benyamin (Author), Chan, Tommy H. T. (Author), Nguyen, Andy (Author) and Thambiratnam, David P. (Author) |
Journal Title | International Journal of Structural Stability and Dynamics |
Journal Citation | 18 (12), pp. 1-19 |
Article Number | 1850160 |
Number of Pages | 19 |
Year | 2018 |
Publisher | World Scientific Publishing |
Place of Publication | Singapore |
ISSN | 0219-4554 |
1793-6764 | |
Digital Object Identifier (DOI) | https://doi.org/10.1142/S0219455418501602 |
Web Address (URL) | https://www.worldscientific.com/doi/abs/10.1142/S0219455418501602 |
Abstract | This paper presents a study on detecting structural deterioration in existing buildings using ambient vibration measurements. Deterioration is a slow and progressive process which reduces the structural performance, including load-bearing capacity. Each building has unique vibration characteristics which change in time due to deterioration and damage. However, the changes due to deterioration are generally subtler than changes due to damage. Examples of deterioration include subtle loss of steel-concrete bond strength, slight corrosion of reinforcement and onset of internal cracks in structural members. Whereas damage can be defined as major sudden structural changes, such as major external cracks of concrete covers. Herein, a deterioration detection method which uses structural health monitoring (SHM) data is proposed to address the deterioration assessment problem. The proposed novel vibration-based deterioration identification method is a parametric-based approach, incorporated with a nonparametric statistical test, to capture changes in the dynamic characteristics of structures. First, autoregressive (AR) time-series models are fitted to the vibration response time histories at different sensor locations. A sensitive deterioration feature is proposed for detecting deterioration by applying statistical hypotheses of two-sample f-test on the model residuals, based on which a function of the resulting P-values is calculated. A novel AR model order estimation procedure is proposed to enhance the sensitivity of the method. The performance of the proposed method is demonstrated through comprehensive simulations of deterioration at single and multiple locations in finite element models (FEM) of 3 and 20-storey reinforced concrete (RC) frames. The method shows a promising sensitivity to detect small levels of structural deterioration prior to damage, even in the presence of noise. |
Keywords | deterioration detection, autoregressive residual, structural health monitoring, vibration-based method |
ANZSRC Field of Research 2020 | 400510. Structural engineering |
Byline Affiliations | Queensland University of Technology |
School of Civil Engineering and Surveying | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q4yx8/structural-deterioration-detection-using-enhanced-autoregressive-residuals
Download files
202
total views192
total downloads0
views this month0
downloads this month