Artificial neural network for vertical displacement prediction of a bridge from strains (part 1): Girder bridge under moving vehicles

Hyun Su Moon, Suyeol Ok, Pang jo Chun, Yun Mook Lim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A real-time prediction method using a multilayer feedforward neural network is proposed for estimating vertical dynamic displacements of a bridge from the longitudinal strains of the bridge when vehicles pass across it. A numerical model for an existing five-girder bridge spanning 36 m proved by actual experimental values was used to verify the proposed method. To obtain a realistic vehicle distribution for the bridge, vehicle type and actual headways of moving vehicles were taken, and the measured vehicle distribution was generalized using Pearson Type III theory. Twenty-five load scenarios were created with assumed vehicle speeds of 40 km/h, 60 km/h, and 80 km/h. The results indicate that the model can reasonably predict the overall displacements of the bridge (which is difficult to measure) from the strain (which is relatively easy to measure) in the field in real time.

Original languageEnglish
Article number2881
JournalApplied Sciences (Switzerland)
Volume9
Issue number14
DOIs
Publication statusPublished - 2019 Jul 1

Bibliographical note

Funding Information:
Acknowledgments: This research was supported by a grant (19CTAP-C152286-01) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government; and also supported by the EDucation-research Integration through Simulation On the Net (EDISON) Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2014M3C1A6038855).

Funding Information:
This research was supported by a grant (19CTAP-C152286-01) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government; and also supported by the EDucation-research Integration through Simulation On the Net (EDISON) Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2014M3C1A6038855).

Publisher Copyright:
© 2019 by the authors.

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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