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Integrated Intelligent Method for Displacement Prediction in Underground Engineering

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Abstract

Considering the complicated monotonously increasing character of the displacement series in underground engineering, the original displacement sequence is divided into two components: the displacement trend sequence and the displacement deviation sequence. This study proposes a new, integrated intelligent method for displacement prediction in underground engineering which is the combining of the grey system method and the evolutionary neural network. The architecture and algorithmic parameters of the neural network simultaneously evolve by immunized evolutionary programming and MBP algorithm. In this method, the grey system method is used to predict the displacement trend sequence, which is one simple monotonous sequence; the evolutionary neural network is used to predict the displacement deviation sequence, which is one very complicated time series. The applications in various real underground engineering examples prove that the approximation sequence and the generalization predication sequence of the integrated intelligent method all coincide well with the measurement displacement sequence. The robustness, performance and applicability of the newly integrated intelligent method are far superior to those of the traditional method. Therefore, this method is an excellent means to predict the measurement displacements of underground engineering.

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Acknowledgements

The financial supports from The Fundamental Research Funds for the Central Universities under Grant Nos. 2014B17814, 2016B10214, 2014B07014 and B15020060 are all gratefully acknowledged.

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing, 210098, People’s Republic of China

    Wei Gao

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  1. Wei Gao
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Correspondence to Wei Gao.

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Gao, W. Integrated Intelligent Method for Displacement Prediction in Underground Engineering. Neural Process Lett 47, 1055–1075 (2018). https://doi.org/10.1007/s11063-017-9685-4

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