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Gaussian process regression-based forecasting model of dam deformation

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Abstract

The displacement at various measurement points is a critical indicator that can intuitively reflect the operational properties of a dam. It is important to analyse displacement monitoring data in a timely manner and make reliable predictions of dam safety. This paper proposes a GPR-based model for dam displacement forecasting. The input variables of the monitoring model consider hydraulic factors, thermal factors and irreversible factors, and the output variables are the observed displacements of the dam. An example analysis based on the proposed method is performed on a prototype gravity dam, and the performance of different simple/combined covariance functions is investigated to obtain the optimal choice. Compared to multiple linear regression, radial basis function network (RBFN) and support vector machine (SVM) methods, the results indicate that the GPR-based model with a combined covariance function significantly improves the prediction accuracy. The proposed model can effectively overcome the over-learning and poor robustness issues of approaches such as RBFN and SVM. In addition, the GPR-based forecasting model has the advantages of simplicity in the training process and the capacity to provide a probabilistic output.

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Abbreviations

AANN:

Auto-associative neural network

AI:

Artificial intelligence

ANN:

Artificial neural network

BP:

Back propagation

CI:

Confidence interval

FEM:

Finite element method

GP:

Gaussian process

GPR:

Gaussian process regression

HTT:

Hydrostatic-thermal-time model

HST:

Hydrostatic-season-time model

IP:

Inverted plumb lines

LINone:

Linear with a bias covariance function

MAE:

Mean absolute error

MAXE:

Maximum absolute error

ML:

Machine learning

MLR:

Multiple linear regression

NARX:

Nonlinear autoregressive exogenous model

NN:

Neural network

NNone:

Neural network covariance function

PLS:

Partial least squares regression

prodNS:

NNone * SEiso

prodRN:

RQiso * NNone

prodRS:

RQiso * SEiso

R 2 :

Determination coefficient

RBFN:

Radial basis function network

RCCD:

Roller-compacted concrete dam

RMSE:

Root mean square error

RQiso:

Rational quadratic covariance function with an isotropic distance measure

RUL:

Remaining useful life

SEiso:

Squared exponential covariance function with isotropic distance measure

SR:

Stepwise regression

sumRQ:

RQiso + SEiso

sumNS:

NNone + SEiso

sumRN:

RQiso + NNone

SVM:

Support vector machine

SVR:

Support vector regression

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Acknowledgements

This research has been greatly supported by the National Key Research and Development Plan (No. 2018YFC0407102). Project of the research on long term monitoring and safety evaluation of concrete dams based on BIM (DJ-ZDXM-2018-02).

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Authors and Affiliations

  1. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, Jiangsu, China

    Chaoning Lin, Siyu Chen, Xiaoqing Liu & Siling Liang

  2. College of Agricultural Engineering, Hohai University, Nanjing, 210098, Jiangsu, China

    Tongchun Li & Siyu Chen

  3. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing, 210098, Jiangsu, China

    Tongchun Li

  4. College of Civil Engineering, Fuzhou University, Fuzhou, 350108, Fujian, China

    Chuan Lin

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  1. Chaoning Lin
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Correspondence to Tongchun Li.

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Lin, C., Li, T., Chen, S. et al. Gaussian process regression-based forecasting model of dam deformation. Neural Comput & Applic 31, 8503–8518 (2019). https://doi.org/10.1007/s00521-019-04375-7

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