Damage Detection of a Bridge Model After Simulated Ground Motion

2016 ◽  
pp. 195-203
Author(s):  
C. Rainieri ◽  
D. Gargaro ◽  
G. Fabbrocino ◽  
L. Di Sarno ◽  
A. Prota
Keyword(s):  
Damage Detection ◽  
Ground Motion ◽  
Bridge Model

Introduction of Shaking Table Test of Rigid Frame Bridge Model

2012 ◽  
Vol 256-259 ◽  
pp. 1492-1495
Author(s):  
Xiao Yu Yan
Keyword(s):  
Ground Motion ◽  
Soil Structure ◽  
Shaking Table Test ◽  
Shaking Table ◽  
Soil Structure Interaction ◽  
Structure Interaction ◽  
Rigid Frame ◽  
Bridge Model ◽  
Support Excitation ◽  
Rigid Frame Bridge

To investigate the seismic response of long-span rigid frame bridges with high-pier, the shaking table test of a 1/10 scaled rigid frame bridge model is introduced in this paper. Details about test equipment, model design, test arrangement, input ground motion waves and test principle are provided. The response of bridge model under the seismic excitation included the uniform excitation and the multi-support excitation is observed. The influence of the soil-structure interaction on the bridge is considered through the real-time dynamic hybrid testing method. The impact effect for different ground motion input during the test is discussed. The influence of multi-support excitation, soil-structure interaction and impact effect on structural seismic responses are studied based on the test results. The isolation effectiveness and the damping effect are discussed as well.

scholarly journals Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3371 ◽  
Author(s):  
Tao Yin ◽  
Hong-ping Zhu
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Damage Detection ◽  
Network Architecture ◽  
Structural Damage ◽  
Multiple Target ◽  
Structural Damage Detection ◽  
Bayesian Neural Network ◽  
Bridge Model ◽  
Hidden Layer

Excellent pattern matching capability makes artificial neural networks (ANNs) a very promising approach for vibration-based structural health monitoring (SHM). The proper design of the network architecture with the suitable complexity is vital to the ANN-based structural damage detection. In addition to the number of hidden neurons, the type of transfer function used in the hidden layer cannot be neglected for the ANN design. Neural network learning can be further presented in the framework of Bayesian statistics, but the issues of selection for the hidden layer transfer function with respect to the Bayesian neural network has not yet been reported in the literature. In addition, most of the research works in the literature for addressing the predictive distribution of neural network output is only for a single target variable, while multiple target variables are rarely involved. In the present paper, for the purpose of probabilistic structural damage detection, Bayesian neural networks with multiple target variables are optimally designed, and the selection of the number of neurons, and the transfer function in the hidden layer, are carried out simultaneously to achieve a neural network architecture with suitable complexity. Furthermore, the nonlinear network function can be approximately linear by assuming the posterior distribution of network parameters is a sufficiently narrow Gaussian, and then the input-dependent covariance matrix of the predictive distribution of network output can be obtained with the Gaussian assumption for the situation of multiple target variables. Structural damage detection is conducted for a steel truss bridge model to verify the proposed method through a set of numerical case studies.

Damage detection in a timber bridge model

2012 ◽  
pp. 939-943
Author(s):  
D Tran ◽  
S Venkatesan ◽  
S Fragomeni
Keyword(s):  
Damage Detection ◽  
Bridge Model

scholarly journals Collapse Analysis of a Two-Span Reinforced Concrete Bridge Model

2021 ◽  
Vol 11 (19) ◽  
pp. 8935
Author(s):  
Yale Li ◽  
Zhouhong Zong ◽  
Bingwen Yang ◽  
Zhanghua Xia ◽  
Yuanzheng Lin ◽  
...  
Keyword(s):  
Ground Motion ◽  
Seismic Isolation ◽  
Shaking Table Test ◽  
Structural Response ◽  
Shaking Table ◽  
Collapse Mechanism ◽  
Girder Bridges ◽  
Bridge Model ◽  
Collapse Resistance ◽  
Collapse Mode

The continuous girder bridge is the main type of small- and medium-sized bridges; however, it has poor collapse resistance and suffers frequent earthquake damage. In order to grasp its collapse mechanism and clarify the internal and external factors affecting its collapse resistance, a 1:3-scaled, two-span bridge model subjected to shaking table test research was taken as the research object. The factors such as seismic characteristics, multi-directional seismic coupling, span, pier height, and structural system type were analyzed to determine the influences on the collapse mode of the bridge. The numerical results showed that different ground motion characteristics led to different collapse modes. Vertical ground motion had little effect on the structural response of the bridge. The change of span and pier height significantly changed the collapse resistance. A seismic isolation design could improve the anti-collapse performance, but the collapse mode varied with the system. The final anti-collapse design suggestions could provide reference for the seismic reinforcement of existing continuous girder bridges and the seismic design of continuous girder bridges that will be constructed.

Interpolation Damage Detection Method on a Suspension Bridge Model: Influence of Sensors Disturbances

2013 ◽  
Vol 569-570 ◽  
pp. 734-741 ◽  
Author(s):  
Marco Domaneschi ◽  
Maria Pina Limongelli ◽  
Luca Martinelli
Keyword(s):  
Damage Detection ◽  
Detection Method ◽  
Suspension Bridge ◽  
Longitudinal Axis ◽  
Noise Signal ◽  
Damage Scenarios ◽  
Spatially Correlated ◽  
Bridge Model ◽  
Structural Responses ◽  
Detection And Localization

In this paper the IDDM (Interpolation Damage Detection Method), recently proposed as a speedy damage detection and localization technique, is applied to the numerical model of a cable suspended bridge derived from the ANSYS model of the Shimotsui-Seto Bridge in Japan (940m length of the main span). The wind excitation is simulated as a spatially correlated process acting in the horizontal direction, transversal to the deck. The bridge is assumed to be monitored by sensors located at the nodes of the model along the longitudinal axis, and recording the absolute acceleration of the bridge deck in the transversal direction Noise in recorded responses can reduce the sensitivity of the method to damage. The influence of noise on the results of the damage detection method is herein investigated by adding a white-noise signal to the structural responses. The mutual relationship between level of noise, intensity of damage and lengths of recorded signals is also investigated with reference to various damage scenarios.

Damage detection in an experimental bridge model using Hilbert-Huang transform of transient vibrations

2011 ◽  
Vol 20 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Anshuman Kunwar ◽  
Ratneshwar Jha ◽  
Matthew Whelan ◽  
Kerop Janoyan
Keyword(s):  
Damage Detection ◽  
Hilbert Huang Transform ◽  
Bridge Model

Structural Damage Detection Based on an Improved Edge-Detection Technique

2013 ◽  
Author(s):  
Peng Chen ◽  
Guangda Hu ◽  
Soheil Nazarian ◽  
Guirong Yan
Keyword(s):  
Edge Detection ◽  
Damage Detection ◽  
Structural Damage ◽  
Computational Effort ◽  
Mode Shapes ◽  
Jump Discontinuities ◽  
Bridge Model ◽  
Polynomial Annihilation ◽  
Different Types ◽  
Edge Detection Method

To localize small damage from mode shapes, the polynomial annihilation edge detection method has been proposed and demonstrated its effectiveness on different types of structural components [7]. However, much computational effort involved in this approach lowers the damage detection speed. To alleviate this difficulty, in this paper, we improve the approach by first using the divided difference approach to identify the region(s) in which jump discontinuities are located, and then only applying the polynomial annihilation method to points in the identified region. In this way, the computational burden of this approach is significantly relieved, while the accuracy is still maintained. The improved approach has been validated by numerical simulations on a cable-stayed bridge model. This approach only requires post-damage mode shapes.

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