Development of a baseline for structural health monitoring for a curved post-tensioned concrete box–girder bridge

Liu, Chengyin; DeWolf, John T.; Kim, Jeong-Ho

doi:10.1016/j.engstruct.2009.08.022

Cited by 37 publications

(15 citation statements)

References 11 publications

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“…In practice, the differences between the measured and numerically computed modal properties of a large structure such as bridges are due to a few parameters [12]. The main reason is that it is only possible to use a few lower modal frequencies for calibration of the FE model since the higher vibration modes are not excited with enough energy during the ambient vibration measurements [13]. The lower vibration modes of a large structure are hardly affected by small variations of the cross-sectional properties, small errors in the boundary conditions, or small local differences in the mass properties.…”

Section: Introductionmentioning

confidence: 99%

Finite Element model updating of a skewed highway bridge using a multi-variable sensitivity-based optimization approach

et al. 2012

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This paper presents the implementation of the Finite Element (FE) model updating for a skewed highway bridge using real-time sensor data. The bridge under investigation is a I-275 crossing in Wayne County, Michigan. The bridge is instrumented with a wireless sensory system to collect the vibration response of the bridge under ambient vibrations. The dynamic characteristics of the bridge have been studied through the field measurements as well as a high-fidelity FE model of the bridge. The developed finite element model of the bridge is updated with the field measured response of the bridge so that the FE computed and field measured modal characteristics of the bridge match each other closely. A comprehensive sensitivity analysis was performed to determine the structural parameters of the FE model which affect the modal frequencies and modal shapes the most. A multivariable sensitivity-based objective function is constructed to minimize the error between the experimentally measured and the FE predicted modal characteristics. The selected objective function includes information about both modal frequencies and mode shapes of the bridge. An iterative approach has been undertaken to find the optimized structural parameters of the FE model which minimizes the selected objective function. Appropriate constraints and boundary conditions are used during the optimization process to prevent non-physical solutions. The final updated FE model of the bridge provides modal results which are very consistent with the experimentally measured modal characteristics.

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Section: Introductionmentioning

confidence: 99%

Finite Element model updating of a skewed highway bridge using a multi-variable sensitivity-based optimization approach

et al. 2012

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“…Many methods have been applied to eliminate the influence of ambient factors on modal parameters. The commonly used methods are the Bayesian framework [11,12], time series analysis [13][14][15] and artificial neural network (ANN) [16][17][18]. Behmanesh et al [11] presented a hierarchical Bayesian framework in the absence of noise or model discrepancies to accurately identify parameters subjected to external actions.…”

Section: Introductionmentioning

confidence: 99%

“…Jesus et al [12] applied the Bayesian framework to the structural identification of a long suspension bridge by considering temperature and traffic load effects. Liu et al [15] established a structural health monitoring (SHM) benchmark database for a prestressed concrete box girder bridge, and a linear regression model between the first three modal frequencies and temperatures was built based on the monitored data. Li et al [16] studied the dependence of the modal frequency, modal shape and damping ratio on temperature and wind speed.…”

Section: Introductionmentioning

confidence: 99%

Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

Zhang

et al. 2020

Sensors

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The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.

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“…Multi-linear regression models provide a suitable option for most bridge types and environments, as shift changes in frequency due to sub-zero temperatures can be modelled. Generally, a regression error value of circa 5% is used to reduce the effect of erroneous data, as seen in [97]. Dervilis et al [98] employed a multivariate linear regression method called the Least Trimmed Square (LTS) estimator, which is fashioned upon the popular least squares approach, but incorporates an initial screening procedure called a Concentration step (C-step) [99].…”

Section: Regression Modelsmentioning

confidence: 99%

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

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Abstract:Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

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Development of a baseline for structural health monitoring for a curved post-tensioned concrete box–girder bridge

Cited by 37 publications

References 11 publications

Finite Element model updating of a skewed highway bridge using a multi-variable sensitivity-based optimization approach

Finite Element model updating of a skewed highway bridge using a multi-variable sensitivity-based optimization approach

Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

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