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Deformation Forecast of Main Girder Enhanced by Stay Cable System with Unequal Interval Grey Model and Residual Composite Correction

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  • Gangnian Xu
  • Youzhi Wang
  • Shimin Wang
  • Quan Yuan

Abstract

This paper aims to achieve the accurate prediction of box girder deformation in SCS-enhanced PSC continuous box girder bridges. To this end, the authors proposed a box girder deformation prediction model based on unequal interval grey model (UIGM) and residual composite correction (RCC). Firstly, the tension forces of cables and the deformation data of box girder were regarded as the time sequence and the original unequal interval sequence, respectively, and the UIGM was constructed to predict the box girder deformation. Secondly, the sine function was constructed on the average features of the residual sequence waveform, and the periodic sequence function was generated by harmonic transform. The two functions were employed to extract the implicit periodic information and overcome the limitation of the UIGM (i.e., the UIGM only generates the change rate of a single index). Finally, the Markov chain method was adopted to treat the random fluctuations and further enhance the prediction accuracy and adaptability of the model. Then, the proposed UIGM-RCC was applied to predict the box girder deformation of Dongming Huanghe River Highway Bridge and forecast the settlement of a building mentioned in previous research. The results show that the proposed model can reflect the exact periodicity and random fluctuations of box girder deformation in SCS-enhanced PSC continuous box girder bridges. The research findings provide a meaningful reference for improving deformation prediction accuracy in SCS-enhanced structures.

Suggested Citation

  • Gangnian Xu & Youzhi Wang & Shimin Wang & Quan Yuan, 2018. "Deformation Forecast of Main Girder Enhanced by Stay Cable System with Unequal Interval Grey Model and Residual Composite Correction," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, June.
  • Handle: RePEc:hin:jnlmpe:6373086
    DOI: 10.1155/2018/6373086
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