Smart Structures and Systems

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Extended artificial neural network for estimating the global response of a cable-stayed bridge based on limited multi-response data

  • Namju Byun (Future and Fusion Laboratory of Architectural, Civil and Environmental Engineering, Korea University) ;
  • Jeonghwa Lee (Future and Fusion Laboratory of Architectural, Civil and Environmental Engineering, Korea University) ;
  • Keesei Lee (Department of Urban Infrastructure Research, Seoul Institute of Technology) ;
  • Young-Jong Kang (School of Civil, Environmental and Architectural Engineering, Korea University)
  • Received : 2023.04.28
  • Accepted : 2023.10.10
  • Published : 2023.10.25
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Abstract

A method that can estimate global deformation and internal forces using a limited amount of displacement data and based on the shape superposition technique and a neural network has been recently developed. However, it is difficult to directly measure sufficient displacement data owing to the limitations of conventional displacement meters and the high cost of global navigation satellite systems (GNSS). Therefore, in this study, the previously developed estimation method was extended by combining displacement, slope, and strain to improve the estimation accuracy while reducing the need for high-cost GNSS. To validate the proposed model, the global deformation and internal forces of a cable-stayed bridge were estimated using limited multi-response data. The effect of multi-response data was analyzed, and the estimation performance of the extended method was verified by comparing its results with those of previous methods using a numerical model. The comparison results reveal that the extended method has better performance when estimating global responses than previous methods.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (MIST) [grant No. 2020R1A2C2014450] and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [grant No. 2022R1I1A1A01053382].

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