Simultaneous identification of bridge structural parameters and vehicle loads

Feng, Dongming; Sun, Hao; Feng, Maria Q.

doi:10.1016/j.compstruc.2015.05.017

Cited by 111 publications

(72 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There have been considerable efforts toward the development of efficient numerical VBI models to predict the dynamic bridge responses . As shown in Figure , a VBI system can be modeled by a simply supported single‐span or continuous multi‐span bridge subjected to a moving vehicle, which is represented by a 4‐DOF system .…”

Section: Dynamics Of Vehicle–bridge Interaction Systemmentioning

confidence: 99%

Output-only damage detection using vehicle-induced displacement response and mode shape curvature index

Feng

2016

Struct. Control Health Monit.

Self Cite

View full text Add to dashboard Cite

SUMMARYMost of the existing bridge superstructure damage detection methods are based on acceleration or strain response due to ambient excitation. This paper proposes a bridge damage detection procedure that utilizes vehicle-induced displacement response without requiring prior knowledge about the traffic excitation and road surface roughness. This study is partially motivated by the recent advances in convenient measurement of structural displacements enabled by video-based sensors. Vehicle-bridge interaction analysis shows that when subjected to moving vehicles, the bridge displacement response is dominated by the first-order mode component. This justifies the proposed damage detection method that only requires the first-order mode shape curvature. While conventionally the mode shapes are extracted based on operational modal analysis, the first-order mode shape is extracted by directly analyzing the power spectral density functions of measured bridge displacement responses under vehicle excitations. Numerical simulations are carried out to investigate the feasibility and performance of the proposed damage detection method using three damage scenarios including damage at single, double, and multiple locations, each involving several extents of damage defined by the reduction in element stiffness. The results reveal that all the damage cases can be successfully identified. Furthermore, the damage detection performance is evaluated for cases involving different classes of road surface roughness and less measurement points. The study demonstrates the potential of the proposed model-free and time-efficient method for damage detection of bridge structures.

show abstract

Section: Dynamics Of Vehicle–bridge Interaction Systemmentioning

confidence: 99%

Output-only damage detection using vehicle-induced displacement response and mode shape curvature index

Feng

2016

Struct. Control Health Monit.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In fact, the state of the art of these methods has been reviewed in a number of works . According to most of these works, system identification methods can be classified as parametric and non‐parametric (genetic algorithms , evolutionary strategy , neural networks or least‐squares estimation ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of measurement and simulation errors in structural system identification by observability techniques

Lei

Galant

Nogal

et al. 2016

Struct. Control Health Monit.

View full text Add to dashboard Cite

This is the peer reviewed version of the following article: [Lei, J., Lozano-Galant, J. A., Nogal, M., Xu, D., and Turmo, J. (2017) Analysis of measurement and simulation errors in structural system identification by observability techniques. Struct. Control Health Monit., 24: . doi: 10.1002/stc.1923.], which has been published in final form at http://onlinelibrary.wiley.com/wol1/doi/10.1002/stc.1923/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.During the process of structural system identification, errors are unavoidable. This paper analyzes the effects of measurement and simulation errors in structural system identification based on observability techniques. To illustrate the symbolic approach of this method a simply supported beam is analyzed step-by-step. This analysis provides, for the very first time in the literature, the parametric equations of the estimated parameters. The effects of several factors, such as errors in a particular measurement or in the whole measurement set, load location, measurement location or sign of the errors, on the accuracy of the identification results are also investigated. It is found that error in a particular measurement increases the errors of individual estimations, and this effect can be significantly mitigated by introducing random errors in the whole measurement set. The propagation of simulation errors when using observability techniques is illustrated by two structures with different measurement sets and loading cases. A fluctuation of the observed parameters around the real values is proved to be a characteristic of this method. Also, it is suggested that a sufficient combination of different load cases should be utilized to avoid the inaccurate estimation at the location of low curvature zones.Peer ReviewedPostprint (author's final draft

show abstract

“…where b x k|k−1 is the estimate of x k given for Y k − 1 with associated error covariance P x k|k−1 , and p(u k ) is a Gaussian prior distribution with the mean 0 and associated input covariance P u k . Substituting Equation 23 into Equation 20 and combining all constants into a single constant β, then the posterior…”

Section: Update Stagementioning

confidence: 99%

“…In the algebraic representation, transformation from the input force into the observed response is carried out through a matrix filled with system Markov parameters, namely the characteristic matrix. 23 Force identification problem normally establishes the estimation of an unknown input from the known observed response and the characteristic matrix. Solution of the algebraic equation in terms of the least-square error generally needs to calculate a Moor-Penrose pseudo inverse.…”

Section: Introductionmentioning

confidence: 99%

Bayesian framework for simultaneous input/state estimation in structural and mechanical systems

Valikhani

Younesian

2019

Struct Control Health Monit

View full text Add to dashboard Cite

Summary Real‐time input/state estimation by means of the observed response has drawn considerable attention in the structural, dynamic, and control systems. In this line, a minimum‐variance unbiased (MVU) estimator has been utilized to develop three‐step filters that sometimes experience instability and divergence, especially for the input estimation in structural dynamics. To solve this challenge, this study is aimed at to estimate both the input and state simultaneously for a linear system. A Bayesian framework is developed, in which the prior information of the unknown input in terms of the a priori probability density function is incorporated in estimation process. Formulation of the estimation is presented in the state‐space representation in presence and absence of the direct feedthrough. Three‐step filters including input estimation, state prediction, and the updating are developed for the system having model uncertainty and noisy observed data. In addition to the estimation, another novel feature of this study is to predict the steady states of the filters in the closed‐form representation. We also establish sufficient conditions for convergence and stability of the proposed filters. At the end, the efficiency, robustness, and advantage of the approach is verified through numerical studies for different systems. Results for the input and state estimation are compared with real values according to the MVU estimator for the observed noisy data sets.

show abstract

Simultaneous identification of bridge structural parameters and vehicle loads

Cited by 111 publications

References 38 publications

Output-only damage detection using vehicle-induced displacement response and mode shape curvature index

Output-only damage detection using vehicle-induced displacement response and mode shape curvature index

Analysis of measurement and simulation errors in structural system identification by observability techniques

Bayesian framework for simultaneous input/state estimation in structural and mechanical systems

Contact Info

Product

Resources

About