scholarly journals Effect of disaster debris, floodwater pooling duration, and bridge damage on immediate post-tsunami connectivity

2021 ◽  
Vol 56 ◽  
pp. 102119
Author(s):  
Sabarethinam Kameshwar ◽  
Hyoungsu Park ◽  
Daniel T. Cox ◽  
Andre R. Barbosa
Keyword(s):  
Bridge Damage ◽  
Disaster Debris

scholarly journals Bridge Damage Detection Approach Using a Roving Camera Technique

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1246 ◽  
Author(s):  
Darragh Lydon ◽  
Myra Lydon ◽  
Rolands Kromanis ◽  
Chuan-Zhi Dong ◽  
Necati Catbas ◽  
...  
Keyword(s):  
Low Cost ◽  
Normal Operation ◽  
Laboratory Model ◽  
Traffic Patterns ◽  
Operational Conditions ◽  
Bridge Model ◽  
Detection Approach ◽  
Bridge Damage ◽  
Computer Vision Technology ◽  
Climate Events

Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

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.

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 A Decision-support Tool for Post-disaster Debris Operations

2015 ◽  
Vol 107 ◽  
pp. 154-167 ◽  
Author(s):  
Álvaro Lorca ◽  
Melih Çelik ◽  
Özlem Ergun ◽  
Pınar Keskinocak
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
Support Tool ◽  
Disaster Debris ◽  
Post Disaster

Sensitivity and analysis GPS signals based bridge damage using GPS observations and wavelet transform

Measurement ◽  
2011 ◽  
Vol 44 (5) ◽  
pp. 927-937 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Hui Li
Keyword(s):  
Wavelet Transform ◽  
Bridge Damage ◽  
Gps Observations

Bridge Damage Detection in Presence of Varying Temperature Using Two-Step Neural Network Approach

2021 ◽  
Vol 26 (6) ◽  
pp. 04021027
Author(s):  
Smriti Sharma ◽  
Subhamoy Sen
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Network Approach ◽  
Neural Network Approach ◽  
Bridge Damage

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

Bridge damage detection using the inverse dynamics optimization algorithm

2016 ◽  
pp. 2176-2181
Author(s):  
Yahya M. Mohammed ◽  
Nasim Uddin ◽  
E. O’brien
Keyword(s):  
Damage Detection ◽  
Optimization Algorithm ◽  
Inverse Dynamics ◽  
Bridge Damage

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.

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