scholarly journals Statistical bridge damage detection using girder distribution factors

2016 ◽  
Vol 109 ◽  
pp. 139-151 ◽  
Author(s):  
Alexandra J. Reiff ◽  
Masoud Sanayei ◽  
Richard M. Vogel
Keyword(s):  
Damage Detection ◽  
Girder Distribution Factors ◽  
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.

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

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

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.

Bridge Damage Detection Using Quasi-Static Component of Moving Vehicle-Induced Dynamic Response

2020 ◽  
pp. 2042001
Author(s):  
Zhiwei Chen ◽  
Yigui Zhou ◽  
Wen-Yu He ◽  
Mengqi Liu
Keyword(s):  
Dynamic Response ◽  
Damage Detection ◽  
Detection Method ◽  
Damage Index ◽  
Frequency Component ◽  
Critical Signal ◽  
Moving Vehicle ◽  
Damage Scenarios ◽  
Static Component ◽  
Bridge Damage

The critical signal component extracted from the bridge response caused by a moving vehicle is normally used to construct damage index for damage detection. The dynamic response of bridges subjected to moving vehicle includes several components, among which the quasi-static component reflects the inherent characteristics of the bridge. In view of this, this paper presents a bridge damage detection method based on quasi-static component of the moving vehicle-induced dynamic response. First, damage-induced changes of the natural-frequency component, moving-frequency component and quasi-static component responses are investigated via a simply-supported beam bridge. The quasi-static component response is proved to be less sensitive to the moving velocity of the load and more suitable for damage detection. Subsequently, a quasi-static component response extraction method is proposed based on analytical mode decomposition (AMD) and moving average filter (MAF). The extracted quasi-static component response is further employed to localize and quantify damages. Finally, numerical simulations are conducted to examine the feasibility, accuracy and advantages of the proposed damage detection method. The results indicated that the proposed method performs well in different damage scenarios and is insensitive to the moving velocity of the load and road roughness.

Contribution to Bridge Damage Analysis

2014 ◽  
Vol 704 ◽  
pp. 435-441 ◽  
Author(s):  
Mohammed Lamine Moussaoui ◽  
Abderrahmane Kibboua ◽  
Mohamed Chabaat
Keyword(s):  
Finite Element ◽  
Reinforced Concrete ◽  
Damage Detection ◽  
Sparse Matrix ◽  
System Of Equations ◽  
Shape Functions ◽  
Time Step ◽  
Analytical Geometry ◽  
Matrix Algorithms ◽  
Bridge Damage

Structural damage detection has become an important research area since several works [2] were focused on the crack zones detection in order to foresee the appropriate solutions. The present research aims to carry out the reinforced concrete bridge damage detection with the finite element mathematical model updating method (MMUM). Unknown degrees of freedom dof are expanded from measured ones. The partitioned system of equations has provided a large sub-system of equations which can be solved efficiently by handling sparse matrix algorithms at each time step of the finite time centered space FTCS discretization. A new and efficient method for the calculation of the constant strain tetrahedron shape functions has been developed [1,3,4,5,6]. The topological and analytical geometry of the tetrahedron and its useful formulae enabled us to develop its shape functions and its corresponding finite element matrices. The global finite element matrices and sparse matrix computations have been achieved with a calculus source code. The reinforced concrete mixture has been modeled with the mixture laws [16] which led to its material properties matrix as an orthotropic case with 9 constants and 2 planes of symmetry from the generalizedHooke’slaw [1]. It is noticed that the material is made of steel, cement, gravels, sand and impurities. The data computations have been implemented with optimized cpu time and data storage using vectorial programming of efficient algorithms [11,12]. The sparse matrix algorithms used in this study are: solution of symmetric systems of equations UTDUd=R, multiplication, addition, transposition, permutation of rows and columns, and ordering of the matrices representations. All the sparse matrices are given in row-wise sparse format.

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