Feasibility investigation of a bridge damage identification approach using vehicle-induced vibration response

2014 ◽  
pp. 420-425
Author(s):  
X He ◽  
T Matsumoto ◽  
T Hayashikawa ◽  
F Catbas ◽  
H Hattori ◽  
...  
Keyword(s):  
Damage Identification ◽  
Vibration Response ◽  
Bridge Damage ◽  
Identification Approach

scholarly journals Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2005
Author(s):  
Veronika Scholz ◽  
Peter Winkler ◽  
Andreas Hornig ◽  
Maik Gude ◽  
Angelos Filippatos
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Damage Identification ◽  
Vibration Response ◽  
Damage Initiation ◽  
Matrix Cracks ◽  
Operational Life ◽  
Multiple Data Sets ◽  
Fully Connected

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.

A two-step method for beam bridge damage identification based on strain response reconstruction and statistical theory

2020 ◽  
Vol 31 (7) ◽  
pp. 075008
Author(s):  
Suxia Zhang ◽  
Zhiyu Wang ◽  
Zhengkun Jian ◽  
Guangyao Liu ◽  
Xijun Liu
Keyword(s):  
Statistical Theory ◽  
Damage Identification ◽  
Strain Response ◽  
Step Method ◽  
Bridge Damage ◽  
Response Reconstruction ◽  
Beam Bridge

scholarly journals Damage Identification of Multi-span Bridge Structure Based on the Recognition of Influence Line

2021 ◽  
Vol 233 ◽  
pp. 03002
Author(s):  
Zhang Yunkai ◽  
Xie Qingli ◽  
Li Guohua ◽  
Ye Yuntao
Keyword(s):  
Information Fusion ◽  
Damage Identification ◽  
Sensory Information ◽  
Bridge Structure ◽  
Damage Site ◽  
Damage Location ◽  
Influence Line ◽  
Before And After ◽  
Bridge Damage ◽  
Combining Information

The stress and deflection effects of the line changes before and after the bridge damage are used as indicators to evaluate the bridge damage and the initial damage site. Then a method of combining information is proposed to improve the accuracy of the damage site. Three-span continuous reinforced concrete was used in the analysis. According to the test, the effectiveness of damage identification based on the damage change of the influence line and the feasibility of the damage location method based on multi-sensory information fusion are confirmed.

scholarly journals Identification of Structural Damage in Bridges Using High-Frequency Vibrational Responses

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ivana Mekjavić
Keyword(s):  
High Frequency ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Damage Model ◽  
Reinforced Concrete Beam ◽  
Computational Technique ◽  
Damage Scenarios ◽  
Identification Approach ◽  
Girder Bridge

The present research aims to develop an effective and applicable structural damage detection method. A damage identification approach using only the changes of measured natural frequencies is presented. The structural damage model is assumed to be associated with a reduction of a contribution to the element stiffness matrix equivalent to a scalar reduction of the material modulus. The computational technique used to identify the damage from the measured data is described. The performance of the proposed technique on numerically simulated real concrete girder bridge is evaluated using imposed damage scenarios. To demonstrate the applicability of the proposed method by employing experimental measured natural frequencies this technique is applied for the first time to a simply supported reinforced concrete beam statically loaded incrementally to failure. The results of the damage identification procedure show that the proposed method can accurately locate the damage and predict the extent of the damage using high-frequency (here beyond the 4th order) vibrational responses.

Bridge Damage Identification Using Artificial Neural Networks

2018 ◽  
Vol 23 (11) ◽  
pp. 04018084 ◽  
Author(s):  
Jordan C. Weinstein ◽  
Masoud Sanayei ◽  
Brian R. Brenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Damage Identification ◽  
Bridge Damage ◽  
Artificial Neural
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