Concrete bridge surface damage detection using a single‐stage detector

2019 ◽  
Vol 35 (4) ◽  
pp. 389-409 ◽  
Author(s):  
Chaobo Zhang ◽  
Chih‐chen Chang ◽  
Maziar Jamshidi
Keyword(s):  
Damage Detection ◽  
Surface Damage ◽  
Single Stage ◽  
Concrete Bridge

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.

Surface damage detection of steel plate with different depths based on Lamb wave

Measurement ◽  
2021 ◽  
pp. 110364
Author(s):  
Muping Hu ◽  
Jian He ◽  
Chen Zhou ◽  
Zeyu Shu ◽  
Wenping Yang
Keyword(s):  
Damage Detection ◽  
Lamb Wave ◽  
Steel Plate ◽  
Surface Damage ◽  
Different Depths

Rapid Surface Damage Detection Equipment for Subway Tunnels Based on Machine Vision System

2021 ◽  
Vol 27 (1) ◽  
pp. 04020047
Author(s):  
Zhen Huang ◽  
He-lin Fu ◽  
Xiao-dong Fan ◽  
Jun-hua Meng ◽  
Wei Chen ◽  
...  
Keyword(s):  
Machine Vision ◽  
Damage Detection ◽  
Vision System ◽  
Surface Damage ◽  
Machine Vision System ◽  
Subway Tunnels

scholarly journals Signal processing based damage detection of concrete bridge piers subjected to consequent excitations

2017 ◽  
Vol 19 (3) ◽  
pp. 2080-2089 ◽  
Author(s):  
Hosein Naderpour ◽  
Amir Ezzodin ◽  
Ali Kheyroddin ◽  
Gholamreza Ghodrati Amiri
Keyword(s):  
Signal Processing ◽  
Damage Detection ◽  
Bridge Piers ◽  
Concrete Bridge

Characterization and Localization of Sub-Surface Structural Features Using Non-Contact Tomography

2016 ◽  
Author(s):  
Sumit Gupta ◽  
Kenneth J. Loh
Keyword(s):  
Damage Detection ◽  
Composite Structures ◽  
Electrical Response ◽  
Surface Damage ◽  
Structural Features ◽  
Electrical Signal ◽  
Rotor Blades ◽  
Function Generator ◽  
Manufacturing Defects ◽  
Electrical Permittivity

The main objective of this work is to develop a non-contact, non-invasive, structural health monitoring technique for surface and sub-surface damage detection in structures such as composite helicopter rotor blades. In many cases, composite structures are prone to damage in the form of cracks, delamination, and manufacturing defects, which can propagate beneath structural surfaces and cause severe component or catastrophic structural failure. The damage detection technique in this study works on the principle of electrical capacitance tomography. Different patterns of electrical field are propagated in a pre-defined sensing area. Using measurements of electrical response along boundaries of the sensing area, the permittivity distribution within that space can be reconstructed. First, a series of numerical simulations was performed by altering the electrical permittivity at different locations to simulate damage. The shapes and locations of permittivity changes were captured by the proposed technique. Second, to demonstrate its validity, an experimental test setup was built with a set of boundary electrodes. The system was connected to a function generator that supplied an electrical signal and induced electrical fields between electrodes. Capacitance between pairs of electrodes were then measured, which were used as inputs for solving the inverse permittivity reconstruction problem. Various test cases with different objects placed in the sensing area were conducted for validating this technique. The preliminary results show that the system was able to reconstruct spatial permittivity distributions and detect the presence, shapes, and locations of objects, thereby suggesting potential for damage detection.

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