Structural Deterioration Detection Using Enhanced Autoregressive Residuals

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.

Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

“…As a result, correlations were found between the rotation angle of the column and the level of the damage in the heavy deformation stage. Monavari et al [115] developed an FEM by employing MatLab software on 3-story and 20-story RC frames to spot deterioration in buildings. The analysis accurately showed minor deterioration pre-damage conditions.…”

Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

“…Lakshmi and Rao [32] localized single and multiple damages in a simply supported beam and a three-story bookshelf benchmark structure using AR-ARX model and a new damage index based on the probability density function. Monavari et al [33] evaluated deterioration in 3-and 20-story reinforced concrete (RC) frames excited under ambient vibrations using Autoregressive Residuals. They presented a new criterion for estimating the best-fit model order of AR time-series called (BMO).…”

“…Therefore, in this study, the normalized signals are filtered by an IIR digital lowpass filter named Chebyshev type II with fast computing speed to dwindle the effects of highfrequency contents [36]. Importantly, this type of filter has been practiced in similar SHM purposes by other researchers [33,39,40].…”

“…Nevertheless, this study benefits from a novel method called Best Model Order (BMO) technique, which was introduced by Monavari et al [33]. Accordingly, the best-fit order not only leads to the least error but also similarly provides high enough complexity.…”

“…Moreover, deterioration is simulated by the continuous loss of cross-section of the left column with the annual deterioration rate (ADR) of 2 × 10 −3 . This gradual reduction of the cross-section is carried out during 50 years and in three distinct cases, as presented in Table .1 and Table .2 [33]. The impact of deterioration on the dynamic characteristics is perceptible by the power spectral density (PSD) versus normalized-frequency for the healthy and deteriorated structure, as presented in Figure.…”