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Prediction of long-term maximum settlement deformation of concrete face rockfill dams using hybrid support vector regression optimized with HHO algorithm

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Abstract

Excessive settlement deformation is one of the primary defects of concrete face rockfill dams (CFRDs). Intelligent monitoring and prediction of settlement deformation is a crucial aspect of dam health monitoring (DHM); however, the design of prediction models is a subsisting challenge. This paper presents an innovative algorithm combining the Harris hawk optimization (HHO) algorithm and support vector regression (SVR) for DHM modeling and sensitivity analysis of the parameters for optimizing the HHO algorithm. An analysis of measured data of different subarea monitoring points of the Jishixia CFRD proved that the monitoring model developed can effectively simulate the settlement deformation of the dam. Finally, this model was used to predict the long-term maximum settlement of Jishixia Dam. The prediction results showed the long-term maximum settlement value of the dam to be 503 mm, accounting for only 0.488% of the maximum dam height, which is comparatively low. The internal settlement deformation of the dam tended to stabilize with time, indicating that its long-term operation was safe and reliable. This model provides a new method for predicting and analyzing the long-term maximum settlement of dams, and can also provide a reference for deformation prediction modeling of other structures.

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Acknowledgements

The research described in this paper was funded by the National Natural Science Foundation of China (Grant Nos. 51979224).

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National Natural Science Foundation of China, 51979224, Yanlong Li.

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  1. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an, 710048, Shaanxi, China

    Yanlong Li, Qiaogang Yin, Ye Zhang & Wen Qiu

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Li, Y., Yin, Q., Zhang, Y. et al. Prediction of long-term maximum settlement deformation of concrete face rockfill dams using hybrid support vector regression optimized with HHO algorithm. J Civil Struct Health Monit 13, 371–386 (2023). https://doi.org/10.1007/s13349-022-00641-w

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