Structural Engineering and Mechanics

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Damage detection in structures using modal curvatures gapped smoothing method and deep learning

  • Nguyen, Duong Huong (Department of Electrical energy, metals, mechanical constructions and systems, Faculty of Engineering and Architecture, Ghent University) ;
  • Bui-Tien, T. (University of Transport and Communications) ;
  • Roeck, Guido De (KU Leuven, Department of Civil Engineering, Structural Mechanics) ;
  • Wahab, Magd Abdel (Division of Computational Mechanics, Ton Duc Thang University)
  • Received : 2020.03.31
  • Accepted : 2020.09.11
  • Published : 2021.01.10
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Abstract

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

Keywords

Acknowledgement

This research is funded by National University of Civil Engineering (NUCE) under grant number 11-2020/KHXD-TĐ. The authors acknowledge the financial support of VLIRUOS TEAM Project, VN2018TEA479A103, 'Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures', funded by the Flemish Government.

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