scholarly journals Damage Identification for Prestressed Adjacent Box-Beam Bridges

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kenneth K. Walsh ◽  
Brendan T. Kelly ◽  
Eric P. Steinberg
Keyword(s):  
Damage Identification ◽  
Longitudinal Direction ◽  
Longitudinal Axis ◽  
Element Model ◽  
Modal Parameters ◽  
Civil Infrastructure ◽  
Response Data ◽  
Acceleration Data ◽  
Bridge Damage ◽  
Damage Types

Structural health monitoring (SHM) has gained considerable attention as a tool for monitoring the health of civil infrastructure. For bridge infrastructure, previous methods have focused on the detection of localized damage through modal parameters extracted from the longitudinal direction of the structure. This paper investigates a new damage detection method based on the change in the first vertical mode extracted from the transverse direction of the bridge. The mode is determined through application of modal curve fitting to frequency response functions (FRFs) that are formed using vertical response data obtained in the direction perpendicular to the bridge’s longitudinal axis. Using this method, both local damage and global damage in the bridge reveal themselves as having a localized effect on the bridge response. Furthermore, damage is revealed in such a way that it enables differentiation of the damage types. To demonstrate the effectiveness of the method, modal parameters were extracted from acceleration data obtained from a finite element model of a full bridge. Analysis of the modal parameters showed that the proposed approach could not only detect both local and global bridge damage, but could also differentiate between damage types using only one mode shape. The proposed method was compared to a previously developed SHM method.

Simply Supported Slab Bridge Damage Identification Based on Curvature Mode Method

2013 ◽  
Vol 351-352 ◽  
pp. 1657-1661
Author(s):  
Long Zhang
Keyword(s):  
Finite Element ◽  
Damage Identification ◽  
Three Dimensional ◽  
Element Model ◽  
Simply Supported ◽  
Mode Method ◽  
Dimensional Finite Element ◽  
Bridge Damage ◽  
Damage Condition ◽  
Three Dimensional Finite Element

This paper discusses the feasibility of damage identification on simply supported slab bridge by the curvature mode. Firstly, the theoretical basis of curvature modal method for damage identification is introduced. Then, it simulated the structural damages by the use of three-dimensional finite element model, and used curvature mode to identification them validly. Based on the finite element simulation results, the following conclusions can be drawn: the decline of the structural natural frequency can be used to judge if the simply supported slab bridge is damaged. But the declined value is small, it is almost impossible to achieve in the scene, and it also can not be accurately positioning and quantitative; curvature model is very sensitive to the partly change of structure. It cannot be affected by the interference of the other plates, when the damage condition was identified on one plate of the simply-supported slab bridge even if that plate is adjacent plate would not affect it.

scholarly journals Bridge damage identification using deep learning-based Convolutional Neural Networks (CNNs)

2021 ◽  
Author(s):  
Sandeep Sony
Keyword(s):  
Neural Network ◽  
Damage Identification ◽  
Severe Damage ◽  
Acceleration Time ◽  
One Dimensional ◽  
Damage Scenarios ◽  
Benchmark Data ◽  
Bridge Damage ◽  
Novel Method ◽  
Damage Types

In this paper, a novel method is proposed based on windowed-one dimensional convolutional neural network for multiclass damage detection using acceleration responses. The data is pre-processed and augmented by extracting samples of windows of the original acceleration time-series. 1D CNN is developed to classify the signals in multiple classes. The damage is detected if the predicted classification is one of the indicated damage levels. The damage is quantified using the predicted class probabilities. Various signals from the accelerometers are provided as input to 1D CNN model, and the resulting class probabilities are used to identify the location of the damage. The proposed method is validated using Z24 bridge benchmark data for multiclass classification for two damage scenarios. The results show that the proposed 1D CNN methods performs with superior accuracy for severe damage cases and works well with different type of damage types.

scholarly journals Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 422-445
Author(s):  
Md Riasat Azim ◽  
Mustafa Gül
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Numerical Study ◽  
Damage Index ◽  
Strain Analysis ◽  
Time History ◽  
Principal Component ◽  
Element Model ◽  
Railway Bridges ◽  
Truss Bridge

Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis.

Dynamic Characteristic Analysis of Irregularity under Turnout by Vehicle-Turnout Rigid-Flexible Coupling Model

2014 ◽  
Vol 945-949 ◽  
pp. 591-595 ◽  
Author(s):  
Meng Chen ◽  
Yan Yun Luo ◽  
Bin Zhang
Keyword(s):  
Finite Element ◽  
Longitudinal Direction ◽  
Element Model ◽  
Vertical Displacement ◽  
Coupling Model ◽  
Interaction Force ◽  
Vertical Acceleration ◽  
Characteristic Analysis ◽  
Flexible Coupling ◽  
Multi Body

Finite element model of track in frog zone is built by vehicle-turnout system dynamics. Considering variation of rail section and elastic support, bending deformation of turnout sleeper, spacer block and sharing pad effects, the track integral rigidity distribution in longitudinal direction is calculated in the model. Vehicle-turnout rigid-flexible coupling model is built by finite element method (FEM), multi-body system (MBS) dynamics and Hertz contact theory. With the regularity solution that different stiffness is applied for rubber pad under sharing pad of different turnout sleeper zone, analysis the variation of vertical acceleration of bogie and wheelset, rail vertical displacement and wheel-rail interaction force, this paper proves that setting reasonable rubber pad stiffness is an efficient method to solve rigidity irregularity problem.

Nature of the mammalian ciliary metachronal wave

1993 ◽  
Vol 75 (1) ◽  
pp. 458-467 ◽  
Author(s):  
L. B. Wong ◽  
I. F. Miller ◽  
D. B. Yeates
Keyword(s):  
Beat Frequency ◽  
Longitudinal Direction ◽  
Longitudinal Axis ◽  
Ciliary Beat Frequency ◽  
Metachronal Wave ◽  
Epithelial Tissues ◽  
Tracheal Epithelial ◽  
The Mean ◽  
Temporal And Spatial ◽  
Ciliary Beat

The temporal and spatial coordination of ciliary beat (metachronicity) is fundamental to effective mucociliary transport. Metachronal wave period (MWP) and ciliary beat frequency (CBF) of fresh excised sheep and canine tracheal epithelial tissues were measured with the use of a newly developed alternating focal spot laser light scattering system. MWP was determined from cross correlation of the heterodyne signals from the alternating focal spots. CBF was determined by autocorrelation of the heterodyne signals from each of the spots. MWP and CBF were measured in four sheep tracheal epithelial tissues with the use of longitudinal interfocal spot distances of 6 and 18 microns. In three canine tracheal epithelial tissues MWP and CBF were measured both longitudinally and circumferentially with interfocal spot distances of 5, 15, 65, 87, and 96 microns. For the sheep tracheal epithelial tissues the mean CBF was 5.9 +/- 0.4 Hz (mean of means; range 3.6 +/- 0.5 to 9.9 +/- 1.5 Hz), whereas the mean MWPs for 6- and 18-microns interfocal spot distances were 0.50 +/- 0.1 and 0.47 +/- 0.1 s, respectively. For the canine tracheal epithelial tissues the mean CBF was 4.0 +/- 0.2 Hz (2.0 +/- 0.8 to 7.2 +/- 3.2 Hz), whereas the mean longitudinal MWP was 1.5 s and the mean circumferential MWP was 2.1 s. Geometric combination of the MWP components leads to a derived MWP of 2.6 s with a propagation direction of 54 degrees with respect to the longitudinal axis of the trachea. MWP was found to be episode modulated with 12- to 20-min intervals in the longitudinal direction, but modulation was not as apparent in the circumferential direction. These data suggest that MWP and CBF are regulated by separate intracellular, intercellular, and intraciliary mechanisms.

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