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Performance of soft computing techniques for GNSS data processing and point displacement modeling for suspension bridge

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Abstract

Bridges are playing a major role in the socioeconomic development of any country over the world. Suspension bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance, and deformation prediction for these types of structures is important and vital. Time of observations for the purpose of structural deformation can vary from a few hours, days to several months, or even years. This paper investigates an integrated monitoring system using GNSS observations for studying the deformation behavior and points displacements prediction for suspension highway bridge, taking into consideration the effect of wind, temperature, humidity, and traffic loads during the operational and short-term measurements. Due to the complexity of the mathematical processing of large GNSS monitoring data for obtaining reliable results, adequate model of several alternatives should be chosen. One of the main objectives of this paper is to investigate the optimum predictive soft computing model for processing GNSS observations and points displacement prediction. Several mathematical models and two cases of data amount (66.67% and 50% of all available data) for dynamic and kinematic state are applied and compared for prediction of suspension bridge displacement with confidence interval with a probability ρ = 0.95, Δ = ±2σ. The resulting point displacement values by applying ANNs and ANFIS, which used a confidence interval with a probability of ρ = 0.95, Δ = ± 2σ when using 66.67% of all data, are more accurate and reliable than any other applied methods; therefore, ANNs and ANFIS can provide a significant improvement of understanding and predicting the structure deformation values where conventional mathematical modeling techniques were not as accurate or capable especially in dynamic prediction of displacements.

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Acknowledgements

The authors want to thank Dr. Mosbeh Rashed Mosbeh, Public Works Engineering Department, Faculty of Engineering, Mansoura University for providing the data (observations) of the studied suspension bridge.

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Authors and Affiliations

  1. Public Works Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Ashraf A. A. Beshr

  2. Civil Engineering Department., Higher Institute for Engineering and Technology, Kafr El-Sheikh, Kafr El-Sheikh, Egypt

    Ashraf A. A. Beshr & Fawzi H. Zarzoura

  3. Department of Physical Geodesy and Remote Sensing, Siberian State University of Geosystems and Technologies, Novosibirsk, 630108, Russia

    Boris T. Mazurov

Authors
  1. Ashraf A. A. Beshr
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  2. Fawzi H. Zarzoura
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  3. Boris T. Mazurov
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Correspondence to Ashraf A. A. Beshr.

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The authors declare that they have no competing interests.

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Responsible Editor: Abdullah M. Al-Amri

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Beshr, A.A.A., Zarzoura, F.H. & Mazurov, B.T. Performance of soft computing techniques for GNSS data processing and point displacement modeling for suspension bridge. Arab J Geosci 14, 1057 (2021). https://doi.org/10.1007/s12517-021-07037-y

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  • DOI: https://doi.org/10.1007/s12517-021-07037-y

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