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A Hybrid Model Integrating Principal Component Analysis, Fuzzy C-Means, and Gaussian Process Regression for Dam Deformation Prediction

  • Research Article-Civil Engineering
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Abstract

Dam behavior prediction model is a fundamental component of dam structural health monitoring systems. As the most intuitive monitoring indicators, deformation is commonly used to reflect the dam behavior change. The selection of input variables and training samples determines the performance of dam deformation predictive models. In this paper, a novel hybrid model integrating principal component analysis (PCA), fuzzy C-means (FCM), and Gaussian process regression (GPR) are proposed to predict dam deformation. Specifically, PCA is utilized to extract the main information of original thermometer data as temperature variables, while FCM is used to divide the samples into several categories according to the similarity of the environmental monitoring data. Then, the samples in each category are used to train GPR models with five commonly used covariance functions based on influencing factors, respectively. In the test phase, FCM is used to determine what category the samples in the test set belong to, and then, the corresponding trained GPR model is utilized to predict dam deformation. The proposed hybrid model is fully demonstrated and validated by monitoring data collected from a multiple-arch concrete dam in long-term service. Various benchmark models with or without FCM analysis are selected as comparison models. Experimental results show the proposed novel model outperforms the other comparison methods in terms of all evaluation indicators. This indicates fuzzy clustering analysis can effectively improve the performance of the prediction model, and the proposed hybrid model can predict future dam deformation with high accuracy and efficiency.

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The data used to support the findings of this study are included within the supplementary information file(s).

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Acknowledgements

This research has been supported by the National Key Research and Development Program, Grant/Award Number: China 2018YFC1508603, and the National Natural Science Foundation of China, Grant/Award Numbers: 51579086, 51739003. The data preparation work from Associate Professor Bo Chen and the support from Anhui Reservoir Management Office are grateful.

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Authors and Affiliations

  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210024, China

    Yangtao Li, Tengfei Bao, Xiaosong Shu, Zhixin Gao & Kang Zhang

  2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210024, China

    Yangtao Li, Tengfei Bao, Xiaosong Shu, Zhixin Gao & Kang Zhang

  3. College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, 443002, China

    Tengfei Bao

  4. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK

    Zexun Chen

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  1. Yangtao Li
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Correspondence to Tengfei Bao.

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Li, Y., Bao, T., Shu, X. et al. A Hybrid Model Integrating Principal Component Analysis, Fuzzy C-Means, and Gaussian Process Regression for Dam Deformation Prediction. Arab J Sci Eng 46, 4293–4306 (2021). https://doi.org/10.1007/s13369-020-04923-7

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  • DOI: https://doi.org/10.1007/s13369-020-04923-7

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