scholarly journals Bridge damage detection using ambient traffic and moving force identification

2015 ◽  
Vol 22 (12) ◽  
pp. 1396-1407 ◽  
Author(s):  
Eugene OBrien ◽  
Ciaran Carey ◽  
Jennifer Keenahan
Keyword(s):  
Damage Detection ◽  
Force Identification ◽  
Moving Force ◽  
Bridge Damage

Investigating the Use of Moving Force Identification Theory in Bridge Damage Detection

2013 ◽  
Vol 569-570 ◽  
pp. 215-222 ◽  
Author(s):  
Ciaran H. Carey ◽  
Eugene J. O'Brien ◽  
Jennifer Keenahan
Keyword(s):  
Damage Detection ◽  
Interaction Model ◽  
Force Identification ◽  
Moving Force ◽  
Damage Location ◽  
Damage Indicator ◽  
Direct Measurements ◽  
Bridge Damage ◽  
The Mean ◽  
Identification Theory

This paper investigates the use of Moving Force Identification as a method of bridge damage detection. It identifies changes in the predicted axle force histories that occur as a result of loss in bridge element stiffness, i.e. as a result of bridge damage. A 2-dimensional Vehicle-Bridge Interaction model is used in numerical simulations to assess the effectiveness of the method in detecting changes in stiffness. Fleets of similar vehicles are simulated and the mean force pattern is used as the damage indicator. Results show that the method is more sensitive to damage than direct measurements of displacement. The paper also explores the use of the force history as an indicator of damage location.

scholarly journals A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage

2021 ◽  
Vol 11 (16) ◽  
pp. 7271
Author(s):  
Shuo Wang ◽  
Eugene J. OBrien ◽  
Daniel P. McCrum
Keyword(s):  
Interaction Model ◽  
Sensitivity Study ◽  
Identification Algorithm ◽  
Force Identification ◽  
Damage Level ◽  
Moving Force ◽  
Weigh In Motion ◽  
Vehicle Velocity ◽  
Gross Vehicle Weight ◽  
Bridge Damage

This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level.

scholarly journals Computer Vision-Based Bridge Damage Detection Using Deep Convolutional Networks with Expectation Maximum Attention Module

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 824
Author(s):  
Wenting Qiao ◽  
Biao Ma ◽  
Qiangwei Liu ◽  
Xiaoguang Wu ◽  
Gang Li
Keyword(s):  
Damage Detection ◽  
Complex Structure ◽  
Surface Damage ◽  
Break Point ◽  
Convolutional Networks ◽  
Concrete Crack ◽  
Current State ◽  
Bridge Damage ◽  
Bridge Inspections ◽  
Impact Problems

Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.

Moving force identification based on redundant concatenated dictionary and weighted l1-norm regularization

2018 ◽  
Vol 98 ◽  
pp. 32-49 ◽  
Author(s):  
Chu-Dong Pan ◽  
Ling Yu ◽  
Huan-Lin Liu ◽  
Ze-Peng Chen ◽  
Wen-Feng Luo
Keyword(s):  
L1 Norm ◽  
Force Identification ◽  
Moving Force
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