Damage Classification on Bridges using Backpropagation Neural Network
DOI:
https://doi.org/10.21512/emacsjournal.v3i2.7406Keywords:
structural health monitoring system, bridge structure, backpropagation neural networkAbstract
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.
Plum Analytics
References
Amir, A. (2013). Studi Keandalan Struktur Jembatan Sungai Tello (Lama) Berdasarkan Beban Lalu Lintas Umum dan Trailer Super Berat dengan Metode Moving Load. Makassar: Universitas Hasanuddin.
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Engineering, MAthematics and Computer Science (EMACS) Journal
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: Creative Commons Attribution-Share Alike (CC BY-SA)
