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Comparison of hierarchical clustering based deformation prediction models for high arch dams during the initial operation period

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Abstract

The purpose of this study is to compare the prediction performances of different commonly used data mining models for high arch dam deformation prediction during the initial operation period. Four machine learning modeling techniques, namely random forest (RF), least squares support vector machines (LS-SVM), simple boosted regression tree (SBRT), kernel extreme learning machine (K-ELM), and modified statistical models are used. With the help of hierarchical clustering on principal component analysis, the typically measured temperatures representing the unsteady dam temperature field are selected to reduce dimensionality, avoid over-fit, and facilitate explanation. The practical term for the time-dependent effect, reflecting both the exponential growth and the recovery term, is also introduced. These models are constructed, and their prediction and explanatory capabilities to model dam deformation are compared by dividing time series into training and test sets. Four different training and test combinations are investigated. The traditional hydrostatic–seasonal–time (HST) model is not enough to predict this nonlinear deformation. The SBRT, the modified HST, and its improved HTTTS (hydrostatic–thermal by measured temperature–time–season model) models show good performance. Relative influence and partial dependence plot are employed to understand the relationship between the deformation and the predictor variables. The results show that several factors, including reservoir thermal stratification, concrete temperature rise, valley contraction, and non-monotonic time-dependent effect, affect the deformation of high arch dams during the initial operation period.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China (NSFC) (SN: 51879169), the National Key R & D Program of China (SN: 2018YFC0406705). The observation data provided by the Changjiang Three Gorges Project Development Corporation are gratefully acknowledged.

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  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, No. 223 Guangzhou Road, Nanjing, 210029, China

    Jiang Hu & Fuheng Ma

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  1. Jiang Hu
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  2. Fuheng Ma
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Correspondence to Jiang Hu.

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Hu, J., Ma, F. Comparison of hierarchical clustering based deformation prediction models for high arch dams during the initial operation period. J Civil Struct Health Monit 11, 897–914 (2021). https://doi.org/10.1007/s13349-021-00487-8

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  • DOI: https://doi.org/10.1007/s13349-021-00487-8

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