Evolution of a Bridge Damage-Detection Algorithm

2013 ◽  
Vol 2331 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Brent Phares ◽  
Ping Lu ◽  
Terry Wipf ◽  
Lowell Greimann ◽  
Junwon Seo
Keyword(s):  
Damage Detection ◽  
Detection Algorithm ◽  
Bridge Damage

Field Validation of a Statistical-Based Bridge Damage-Detection Algorithm

2013 ◽  
Vol 18 (11) ◽  
pp. 1227-1238 ◽  
Author(s):  
Brent Phares ◽  
Ping Lu ◽  
Terry Wipf ◽  
Lowell Greimann ◽  
Junwon Seo
Keyword(s):  
Damage Detection ◽  
Detection Algorithm ◽  
Field Validation ◽  
Bridge Damage

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.

Investigation of Sensor Placement in Lamb Wave-Based SHM Method

2012 ◽  
Vol 518 ◽  
pp. 174-183 ◽  
Author(s):  
Pawel Malinowski ◽  
Tomasz Wandowski ◽  
Wiesław M. Ostachowicz
Keyword(s):  
Wave Propagation ◽  
Damage Detection ◽  
Guided Waves ◽  
Sensor Placement ◽  
Detection Algorithm ◽  
Piezoelectric Sensors ◽  
Optimal Placement ◽  
Laser Vibrometer ◽  
Contact Method ◽  
Monitoring Method

In this paper the investigation of a structural health monitoring method for thin-walled parts of structures is presented. The concept is based on the guided elastic wave propagation phenomena. This type of waves can be used in order to obtain information about structure condition and possibly damaged areas. Guided elastic waves can travel in the medium with relatively low attenuation, therefore they enable monitoring of extensive parts of structures. In this way it is possible to detect small defects in their early stage of growth. It is essential because undetected damage can endanger integrity of a structure. In reported investigation piezoelectric transducer was used to excite guided waves in chosen specimens. Dispersion of guided waves results in changes of velocity with the wave frequency, therefore a narrowband signal was used. Measurement of the wave field was realized using laser scanning vibrometer that registered the velocity responses at points belonging to a defined mesh. An artificial discontinuity was introduced to the specimen. The goals of the investigation was to detect it and find optimal sensor placement for this task. Determination of the optimal placement of sensors is a very challenging mission. In conducted investigation laser vibrometer was used to facilitate the task. The chosen mesh of measuring points was the basis for the investigation. The purpose was to consider various configuration of piezoelectric sensors. Instead of using vast amount of piezoelectric sensors the earlier mentioned laser vibrometer was used to gather the necessary data from wave propagation. The signals gather by this non-contact method for the considered network were input to the damage detection algorithm. Damage detection algorithm was based on a procedure that seeks in the signals the damage-reflected waves. Knowing the wave velocity in considered material the damage position can be estimated.

FOG-based Bridge Damage Detection through Deep CNN

2021 ◽  
Author(s):  
Sheng Li ◽  
Yan Yang ◽  
Lina Yue ◽  
Wenbin Hu ◽  
Fang Liu ◽  
...  
Keyword(s):  
Damage Detection ◽  
Bridge Damage ◽  
Deep Cnn

scholarly journals Bridge damage detection using rotation measurements – Experimental validation

2020 ◽  
Vol 135 ◽  
pp. 106380 ◽  
Author(s):  
F. Huseynov ◽  
C. Kim ◽  
E.J. OBrien ◽  
J.M.W. Brownjohn ◽  
D. Hester ◽  
...  
Keyword(s):  
Damage Detection ◽  
Experimental Validation ◽  
Bridge Damage

scholarly journals Modal-based vibrothermography using feature extraction with application to composite materials

2019 ◽  
Vol 19 (4) ◽  
pp. 967-986 ◽  
Author(s):  
Xintian Chi ◽  
Dario Di Maio ◽  
Nicholas AJ Lieven
Keyword(s):  
Feature Extraction ◽  
Composite Materials ◽  
Damage Detection ◽  
Infrared Camera ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Modal Testing ◽  
Theoretical Development ◽  
Operational Environment ◽  
Practical Applications

This research focuses on the development of a damage detection algorithm based on modal testing, vibrothermography, and feature extraction. The theoretical development of mathematical models is presented to illustrate the principles supporting the associated algorithms, through which the importance of the three components contributing to this approach is demonstrated. Experimental tests and analytical simulations have been performed in laboratory conditions to show that the proposed damage detection algorithm is able to detect, locate, and extract the features generated due to the presence of sub-surface damage in aerospace grade composite materials captured by an infrared camera. Through tests and analyses, the reliability and repeatability of this damage detection algorithm are verified. In the concluding observations of this article, suggestions are proposed for this algorithm’s practical applications in an operational environment.

Nondestructive Damage Detection of Bridges: A Status Review

2001 ◽  
Vol 4 (2) ◽  
pp. 75-91 ◽  
Author(s):  
Xiaotong Wang ◽  
Chih-Chen Chang ◽  
Lichu Fan
Keyword(s):  
Damage Detection ◽  
Rapid Development ◽  
Low Frequency ◽  
Basic Research ◽  
Digital Signal ◽  
Point Of View ◽  
Detection Methods ◽  
Measured Signal ◽  
Destructive Testing ◽  
Bridge Damage

The recent advances in detecting and locating damage in bridges by different kinds of non-destructive testing and evaluation (NDT&E) methods are reviewed. From the application point of view, classifications for general bridge components and their damage types are presented. The relationships between damage, bridge components, and NDT&E techniques are summarized. Many useful WEB sources of NDT&E techniques in bridge damage detection are given. It is concluded that: (1) vibration-based damage detection methods are successful to a certain extent, especially when the overall damage is significant and, low frequency vibration can identify those areas where more detailed local inspection should be concentrated; (2) robust identification techniques that are able to locate damage based on realistic measured data sets still seem a long way from reality, and, basic research is still necessary in the mean time; (3) the rapid development of computer technology and digital signal processing (DSP) techniques greatly impacts upon the conventional NDT techniques, especially in control data processing and data displaying, as well as in simulation and modeling; (4) most of the NDT&E techniques introduced in this paper have their own practical commercial systems, but the effort required for combining the theoretical, experimental and engineering achievements, is still a challenging task when establishing the relationship between the unknown quantities and the measured signal parameters and specialised instruments have shown great advantages for doing some things more effectively than general ones; (5) in bridge damage detection, a problem usually requires the application of different NDT&E techniques; two or more independent techniques are needed to enable confidence in the results.

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