Structural Deterioration Localization Using Enhanced Autoregressive Time-Series Analysis

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 [Formula: see text]-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.

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

“…Moreover, using forced vibration testing on existing structures should be done through a well-controlled process as it may cause damage to the structure. Thus, the use of ambient vibration tests as a practical and relatively inexpensive way has increased over the past few years (52)(53)(54)(55).…”

“…As a result, the authors were successful in qualifying slight changes like deterioration on a high-rise FEM concrete building excited by real ambient excitations. AR model residuals can also be combined with test statistics such as the T-values of statistical hypothesis of chi-square variance test to locate crack-induced deterioration in a complex lab test of a box girder structure (54) Time series is one of the tools implemented in statistical pattern recognition applications for SHM (180). Since the method is based on a partial structural dynamics model, it can identify even a small number of variations (181).…”

“…It should be noted that methods based on extraction of features can identify damages independently via using different damages indices directly (54,(217)(218)(219) or by combining them with machine learning techniques for the purpose of pattern recognition (55). Herein, machine learning methods are applied to the features extracted from measured data to classify and predict the structural patterns obtained from sensors.…”

Engineering in heritage conservation

“…For the systems development, several studies have been published with regards to wireless sensors (Aflatooni et al , 2013; Nguyen et al , 2014a, b), including measurement/sensors development (Hussin et al , 2020; Li et al , 2015, 2019). Application covering both the structural performance monitoring and structural safety evaluations of SHM has been very promising (Jayasundara et al , 2020; Joseph et al , 2020; Monavari et al , 2020; Moravej et al , 2020; Randiligama et al , 2020; Wickramasinghe et al , 2020).…”

A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model

“…Since this method is based on a partial structural dynamics model, tit can identify even a small number of vibrations [15]. In this area, autoregressive (AR) models are investigated for damage and deterioration detection in buildings and bridges [16][17][18]. Autoregressive and moving average model (ARMA), as well as generalized autoregressive conditional heteroscedasticity model (GARCH), have proved to be beneficial for nonlinear damage identification in building specimens [19].…”