Health Monitoring of Bridges by Using the Available Data Based on Deep Learning

Document Type : Original Article

Authors

1 M.Sc., Faculty of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

2 Professor, Department of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

3 Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

Abstract

Damage caused by sudden and specific loads such as earthquakes causes undesirable changes in structural performance. Therefore, in recent years need for methods to identify damage in structures feel more than ever. Therefore, finding ways to identify damages and their location is one of the issues that has always been discussed in civil, mechanical, and aerospace engineering. The main idea in most of these methods is to use the responses that the structure gives to external factors based on the assumptions available, in most of these studies structures are considered as a dynamic system with unique mass, stiffness, and damping which in the case of structural damage, these parameters change and reflect changes in the structural responses. This response is often in the form of time signals containing structural properties that can be extracted and used to detect potential damage by examining them. The increasing advancement of machine learning science has not only provided the conditions for the improvement of engineering sciences but it has been such that today it is the computers that often do the most important calculations, One of the most widely used methods today is deep convolutional neural networks to detect the properties mentioned in structural signals.
This thesis attempts to design a one-dimensional convoluted network to detect a healthy state from unhealthy in which we investigate the effect of the normalizer function on the pre-processing phase is attempted and the effect of Stochastic decreasing gradient and Adam (two optimization functions) on the network learning process is also investigated. To evaluate the capability of the proposed method, data from the Yong cable bridge in China were used. Finally, it can be concluded that the results show satisfactory, accurate, and fast one-dimensional deep neural network (convolution) performance in the diagnosis of an unhealthy state.

Keywords

Main Subjects

References

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History

  • Receive Date: 21 February 2021
  • Revise Date: 12 July 2021
  • Accept Date: 04 August 2021

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