Damage Classification on Bridges using Backpropagation Neural Network

Authors

  • Victoria Ivy Tansil Bina Nusantara University
  • Novita Hanafiah Bina Nusantara University
  • Alexander Agung Santoso Gunawan Bina Nusantara University
  • Dewi Suryani Bina Nusantara University

DOI:

https://doi.org/10.21512/emacsjournal.v3i2.7406

Keywords:

structural health monitoring system, bridge structure, backpropagation neural network

Abstract

Bridge structures can be damaged due to various factors such as pressure, vibration, temperature, etc. This study aims to detect damaged on bridges early so that accidents that can occur due to the damaged-on bridge can be avoided. The research method is divided into designing a model, building the model, and evaluating the model. The result of this research is a program that can classify healthy or damaged bridges using vibration data of tested points on bridges.

Dimensions

Plum Analytics

Author Biographies

Victoria Ivy Tansil, Bina Nusantara University

Computer Science Department, School of Computer Science

Novita Hanafiah, Bina Nusantara University

Computer Science Department, School of Computer Science

Alexander Agung Santoso Gunawan, Bina Nusantara University

Computer Science Department, School of Computer Science

References

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Bagavathiappan, S., Lahiri, B. B., Saravanan, T., Philip, J., & Jayakumar, T. (2013). Infrared thermography for condition monitoring–a review. Infrared Physics & Technology, 60, 35-55.

Farrar, C. R., & Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley and Sons.

Fassois, S. D., & Sakellariou, J. S. (2009). Encyclopedia of Structural Health Monitoring, Chapter 23: Statistical Time Series Methods for SHM. John Wiley and Sons.

Gunawan, F. E. (2017). Improving the Prediction Reliability of F-stat Method by using Linear Support Vector Machine for Structural Health Monitoring. Jakarta: Bina Nusantara University.

Hanus, M. J., & Harris, A. T. (2013). Nanotechnology innovations for the construction industry. Progress in materials science, 58(7), 1056-1102.

Kopsaftopoulos, F. P., & Fassois, S. D. (2010). Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods. Mechanical Systems and Signal Processing, 24(7), 1977-1997.

Rayata, F., Marveni, V. M., Putri, M., & Sarfriadi, R. S. (2014). Artificial Neural Network. Padang: Universitas Negeri Padang.

Suryanita, R., & Adnan, A. (2014). Early-warning system in bridge monitoring based on acceleration and displacement data domain. Transactions on Engineering Technologies, 157-169.

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Published

2021-05-31

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