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Volume 44 Issue 3
May  2025
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Article Navigation >  Bulletin of Geological Science and Technology  > 2025  >  44(3): 242-254
CHEN Xiangyu，GUO Yonggang，ZHOU Xingbo，et al. Rapid assessment of earthquake damage of reservoir dam based on BO-GBDT[J]. Bulletin of Geological Science and Technology，2025，44（3）：242-254 doi: 10.19509/j.cnki.dzkq.tb20240425
Citation: CHEN Xiangyu，GUO Yonggang，ZHOU Xingbo，et al. Rapid assessment of earthquake damage of reservoir dam based on BO-GBDT[J]. Bulletin of Geological Science and Technology，2025，44（3）：242-254 doi: 10.19509/j.cnki.dzkq.tb20240425
shu

Rapid assessment of earthquake damage of reservoir dam based on BO-GBDT

doi: 10.19509/j.cnki.dzkq.tb20240425
  • 1.

    Xizang Agriculture and Animal Husbandry University, Linzhi Tibet 860000, China

  • 2.

    General Institute of Water Conservancy and Hydropower Planning and Design, Ministry of Water Resources, Beijing 100120, China

More Information
  • Author Bio:

    E-mail：1835461087@qq.com

  • Corresponding author: E-mail：1960373107@qq.com
  • Received Date: 31 Jul 2024
  • Accepted Date: 28 Nov 2024
  • Rev Recd Date: 18 Sep 2024
    • Available Online: 05 Dec 2024
    Abstract
  • Objective

    Reservoir dams are critical infrastructure, and accurately assessing the extent of earthquake-induced damage is crucial for developing rescue operations and post-disaster restoration. This study aims to achieve rapid and accurate assessment of post-earthquake damage in reservoir dam.

    Methods

    Focusing on earthquake damage data from the Wenchuan Ms8.0 earthquake, this research integrates dam structural characteristics and seismic intensity parameters to establish an assessment index system and dataset. The study employs k-nearest-neighbor interpolation for missing values processing and feature correlation analysis. A rapid assessment model for reservoir dam earthquake damage is proposed using gradient boosting algorithm. To optimize parameters of the gradient boosted tree (GBDT) regression algorithm, four hyperparameter optimization methods are implemented: Grid search (GS), particle swarm optimization (PSO), Bayesian optimization (BO), and hyperband search (HS). The models are compared based on performance metrics, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), and the feature importance of the optimal models is ranked.

    Results

    The results demonstrate that the BO-GBDT model provides the most rapid and accurate assessment of earthquake damage to reservoir dams, achieving a high R2 of 0.99. Feature importance analysis shows that the maximum crack width is the most influential factor. The model demonstrates superior accuracy compared to earth dam damage assessment models based on improved empirical statistical methods, confirming its reliability for rapid post-earthquake damage evaluation of reservoir dams.

    Conclusion

    The research results provide a reference for the earthquake damage assessment of reservoir dams.

     

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