Bridge Dynamic Displacement Monitoring Using Geodetic Measurement

The paper analyzes the possibility to define a dynamic model associated to a bridge using the results of geodetic measurements made for displacement monitoring using modern calculation models. Combining the results obtained with two modern calculation models (Finite Element Method and Kalman Filter) we can define a complex model, able of meeting current risk management requirements. The paper aims to highlight the importance and usefulness of realizing the dynamic model of a bridge in order to test hypotheses regarding limit situations such as: testing the structure response in case of a flood or in the event of a major earthquake, determining the maximum admissible weight on the bridge without requiring interventions, determining the areas where the bridge requires consolidation work after a certain period of time or after natural phenomena etc.

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Real-time dynamic displacement monitoring with double integration of acceleration based on recursive least squares method

Measurement ◽

10.1016/j.measurement.2019.04.053 ◽

2019 ◽

Vol 141 ◽

pp. 460-471 ◽

Cited By ~ 7

Author(s):

Wenhao Zheng ◽

Danhui Dan ◽

Wei Cheng ◽

Ye Xia

Keyword(s):

Least Squares ◽

Real Time ◽

Least Squares Method ◽

Recursive Least Squares ◽

Displacement Monitoring ◽

Dynamic Displacement ◽

Time Dynamic ◽

Double Integration ◽

Recursive Least Squares Method

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Putting It Together: Fitting a Dynamic Model

10.1093/oso/9780192843470.003.0017 ◽

2021 ◽

pp. 283-294

Author(s):

Timothy E. Essington

Keyword(s):

Model Selection ◽

Dynamic Model ◽

Information Criterion ◽

Complex Model ◽

Error Model ◽

Parameter Estimates ◽

Gray Wolf ◽

Observation Error ◽

Worked Example ◽

Complex Population

The chapter “Putting It Together: Fitting a Dynamic Model” provides a synthesis of the material presented in the book, by presenting a worked example that embraces concepts of density dependence, complex model dynamics, parameter estimation, and model selection using the Akaike information criterion. The example chosen is the recovery of gray wolf (Canis lupus) population in Washington State since 2008. The chapter begins by explaining how to fit an observation error model. Next, it examines how to fit a process error model. It then discusses parameter estimates and model selection. The chapter concludes with discussion of how to determine whether the population given as an example exhibits complex population dynamics.

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Accuracy Enhancement of Videogrammetrey for Structural Dynamic Displacement Measurement with Adaptive Filtering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.578-579.1053 ◽

2014 ◽

Vol 578-579 ◽

pp. 1053-1058

Author(s):

Liang Zhong Qin ◽

Hua Fei Zhou ◽

Zi Ling Xie ◽

Cheng Yuan Lu

Keyword(s):

Adaptive Filter ◽

Digital Camera ◽

Displacement Component ◽

Displacement Measurement ◽

Displacement Monitoring ◽

Long Distance ◽

Structural Dynamic ◽

Primary Input ◽

Dynamic Displacement ◽

Dynamic Excitations

Displacement is a good descriptor of the structural behavior and safety status. However, measuring displacement of structures under dynamic excitations is still a challenging task. Videogrammetry shows great potential for dynamic displacement measurement, benefiting from its non-contact and long-distance characteristics. Nevertheless, its all-weather performance has to be fully evaluated before gaining wide applications. This study therefore carried out an investigation into the environmental effects of the all-weather videogrammetry for structural dynamic displacement monitoring. First, long-term outdoor dynamic displacement monitoring tests were carried out. Virtual structural displacement was generated by a motion simulation device and monitored by a commercialized industrial digital camera. The adaptive filter was then employed to filter out noises, which had the primary input of the major displacement component and the reference input of the minor displacement component. The results show that the adaptive filter is well capable of filtering out noises and the measurement accuracy of videogrammetry is significantly enhanced.

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The Non-Planar Nonlinear Dynamic Model of the Inclined Cable with Lumped Masses

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.353-356.3243 ◽

2013 ◽

Vol 353-356 ◽

pp. 3243-3247

Author(s):

Xue Jun He ◽

Shi Yun Zhang ◽

Kai Wei

Keyword(s):

Differential Equations ◽

Dynamic Model ◽

Nonlinear Dynamic ◽

Nonlinear Dynamic System ◽

Complex Model ◽

Nonlinear Dynamic Model ◽

External Excitation ◽

Order Model ◽

Inclined Cable ◽

Lumped Masses

A nonlinear dynamic model for non-planar vibration of inclined cable with three lumped masses was developed by Hamilton principle, which considered the influence of the lumped masses, the external excitation and the inclination angle of the cable. The partial differential equations of this system were discretized to four degree of freedom ordinary differential equations by Galerkin method. The results show that there were complex model coupling between in-planar and out-planar model, first and second order model in this nonlinear dynamic system.

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