Application of artificial neural network to predict dynamic displacements from measured strains for a highway bridge under traffic loads

Hyun Su Moon, Young Kwang Hwang, Moon Kyum Kim, Hyeong Taek Kang, Yun Mook Lim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper addresses the experimental verification of vertical displacement predictions for a highway bridge under dynamic vehicle loads. In the structural health monitoring of bridge structures, the measurements of vertical displacements are relatively difficult than the measurements of axial strains, and the vertical displacements can give an intuitive information for monitoring the structural conditions. Therefore, an artificial neural network (ANN) was introduced for the accurate predictions of the vertical displacements from the axial strains. In the experiments, both the strains and displacements at the different locations were measured during 48 h for obtaining the training (Set 1) and testing (Set 2) data, which were utilized to develop and validate the ANN-based prediction model. The environmental effects such as temperature loads were eliminated from the experimental data using a calibration approach, and the pure contributions of the external vehicle loadings to the 3D highway bridge were considered. The validation results showed that the ANN-based model can accurately predict the vertical displacements. It is expected that the proposed ANN-based model, as a fast and accurate framework, can be utilized for various civil infrastructures during the structural health monitoring.

Original languageEnglish
Pages (from-to)117-126
Number of pages10
JournalJournal of Civil Structural Health Monitoring
Issue number1
Publication statusPublished - 2022 Feb

Bibliographical note

Funding Information:
This research was supported by a Grant (20CTAP-C152286-02) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean Government.

Publisher Copyright:
© 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality


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