Reliability-based load-carrying capacity assessment of bridges using structural health monitoring and nonlinear analysis

Jamali, Shojaeddin; Chan, Tommy H.T.; Nguyễn, Andy; Thambiratnam, David

doi:10.1177/1475921718808462

Cited by 32 publications

(14 citation statements)

References 20 publications

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“…The proposed method in the current study is experimentally validated using sensor data from a test on a large-scale BGB structure. The BGB structure was constructed to study the pre-stress force identifications in pre-stressed concrete BGBs 53 , then load-carrying capacity assessment 54 The experiment is conducted in three different structural state conditions. In the first state (test 01), the BGB model is under baseline conditions.…”

Section: Case Studiesmentioning

confidence: 99%

Structural Deterioration Localization Using Enhanced Autoregressive Time-Series Analysis

Monavari

Chan

Nguyễn

et al. 2020

Int. J. Str. Stab. Dyn.

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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.

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Section: Case Studiesmentioning

confidence: 99%

Structural Deterioration Localization Using Enhanced Autoregressive Time-Series Analysis

Monavari

Chan

Nguyễn

et al. 2020

Int. J. Str. Stab. Dyn.

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“…A mesh size of 50 mm was determined to be fit enough by considering the experimental displacement at the mid-zone in failure mode. More details of the analysis can be found in Jamali et al (2018). In this study, four natural frequencies of the FEM-first vertical bending, second vertical bending, first lateral bending, and third vertical bending modes-were selected and used to update the FEM because similar mode shapes and natural frequencies were extracted from the measured data.…”

Section: Numerical Modelmentioning

confidence: 99%

Vibration-based Bayesian model updating of civil engineering structures applying Gaussian process metamodel

Moravej

Chan

Nguyen

et al. 2019

Advances in Structural Engineering

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Structural health monitoring plays a significant role in providing information regarding the performance of structures throughout their life spans. However, information that is directly extracted from monitored data is usually susceptible to uncertainties and not reliable enough to be used for structural investigations. Finite element model updating is an accredited framework that reliably identifies structural behavior. Recently, the modular Bayesian approach has emerged as a probabilistic technique in calibrating the finite element model of structures and comprehensively addressing uncertainties. However, few studies have investigated its performance on real structures. In this article, modular Bayesian approach is applied to calibrate the finite element model of a lab-scaled concrete box girder bridge. This study is the first to use the modular Bayesian approach to update the initial finite element model of a real structure for two states—undamaged and damaged conditions—in which the damaged state represents changes in structural parameters as a result of aging or overloading. The application of the modular Bayesian approach in the two states provides an opportunity to examine the performance of the approach with observed evidence. A discrepancy function is used to identify the deviation between the outputs of the experimental and numerical models. To alleviate computational burden, the numerical model and the model discrepancy function are replaced by Gaussian processes. Results indicate a significant reduction in the stiffness of concrete in the damaged state, which is identical to cracks observed on the body of the structure. The discrepancy function reaches satisfying ranges in both states, which implies that the properties of the structure are predicted accurately. Consequently, the proposed methodology contributes to a more reliable judgment about structural safety.

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“…These include detection of gradual deterioration due to continuous changes in loading (trend), daily and seasonal environmental effects, 32 sudden damage occurrences from accidents 33 and extreme events. 4 Such analyses are relevant for has condition assessment, 15 reliability estimation, 34 prognosis, 35 and damage detection. 16,27,36…”

Section: Introductionmentioning

confidence: 99%

A dynamic harmonic regression approach for bridge structural health monitoring

Buckley

Pakrashi

Ghosh

2021

Structural Health Monitoring

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Structural damage in a bridge is defined as a significant deviation in the structural response from its standard operating conditions, not explainable by variations in external environmental and operational effects. However, environmental effects such as temperature fluctuations can cause significant seasonal variations in the structural response of a bridge and can mask its changes due to structural damage. This poses a challenge for structural health monitoring of bridges where reliable diagnosis of damage or deterioration is often related to isolation of the responses. To address it, a statistical damage-detection methodology is introduced where strain data are modelled using a dynamic harmonic regression time-series model. Prediction intervals of multi-step ahead forecasts from the dynamic harmonic regression model are then used as statistical control limits within which the observed phenomenon should fall under standard operating conditions. This single recursive structural health monitoring framework for automatic fitting and multi-step ahead forecasting of 1-min interval time-series strain data includes recorded temperature values and diurnal trends as regressors in the model to account for environmental variations. The potential of this method as a robust automatic structural health monitoring strategy is demonstrated on strain data sampled at 1-min interval from a full-scale damaged pre-stressed concrete bridge – before, during and after repair. The proposed method can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state.

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Reliability-based load-carrying capacity assessment of bridges using structural health monitoring and nonlinear analysis

Cited by 32 publications

References 20 publications

Structural Deterioration Localization Using Enhanced Autoregressive Time-Series Analysis

Structural Deterioration Localization Using Enhanced Autoregressive Time-Series Analysis

Vibration-based Bayesian model updating of civil engineering structures applying Gaussian process metamodel

A dynamic harmonic regression approach for bridge structural health monitoring

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