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摘要:
水库大坝是重大生命线工程，地震发生后如何快速有效地进行水库大坝的震害评估，对抢险方案制定和灾后修复重建意义重大。为了快速准确地对遭受地震侵袭的水库大坝破坏程度进行评估，选取汶川8.0级大地震各水库大坝的震损详情，结合大坝的结构特点和地震强度构建了评估指标体系和数据集，使用k邻近插补法对样本的缺失值进行了处理，并判断样本特征相关性，提出了一种基于梯度提升树算法的水库大坝震害快速评估模型。使用网格搜索（grid search，简称GS）、粒子群搜索（particle swarm optimization，简称PSO）、贝叶斯搜索（Bayesian optimization，简称BO）和超带搜索（hyperband search，简称HS）4种超参数优化方法对梯度提升树（gradient boosting decision tree，简称GBDT）回归算法进行了参数优化，根据各模型的性能指标（决定系数
R 2、均方根误差RMSE 、平均绝对误差MAE ）进行了对比，并对最优模型的特征重要性进行了排序。结果表明：BO-GBDT模型能以最短耗时以及较高精度对水库大坝震害程度进行评估，其决定系数R 2高达0.99，特征重要性分数表明最大缝宽是影响最大的因素。使用该模型与基于改进经验统计模型的土坝震害评估模型评估结果对比，准确度有进一步提高，验证了该模型在水库大坝震后震害快速调查评估应用中的可靠性。研究成果为水库大坝的震害评估提供了参考依据。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
M s8.0 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 (R 2), 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
R 2 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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图 1 水库大坝震害快速评估流程图
震害等级1～5分别为基本完好（Ⅰ级）、轻微破坏（Ⅱ级）、中度破坏（Ⅲ级）、严重破坏（Ⅳ级）、毁坏（Ⅴ级）5 个等级，下同
Figure 1. Flowchart for rapid assessment of earthquake damage of reservoir dams
图 2 四川省水库大坝震害基本特征与震害分布
Figure 2. Basic characteristics and distribution of earthquake damage of reservoir dams in Sichuan Province
图 3 水库大坝震害评估指标体系
Figure 3. Indicator system for seismic damage assessment of reservoir dams
图 5 4种插值法的性能指标对比图
Figure 5. Comparison of performance metrics of four interpolation filling methods
图 7 主要变量相关性(a)、统计特征(b)及大坝震害等级占比(c)
Figure 7. Correlations (a) and statistical characteristics (b) of major variables, and dam damage levels as a percentage (c)
图 6 k邻近插补法不同k值下的样本训练性能指标
k值为填补缺失值的最近邻样本数量
Figure 6. Sample training performance metrics of k-neighborhood interpolation method with different values of k
图 8 3种机器学习算法在训练集上的表现
Figure 8. Performance of the three machine learning modes in the training set
图 9 4种超参数优化算法下模型的拟合曲线
Figure 9. Fitting curves of the model with four hyperparameter optimizations
图 10 4种超参数优化下模型的R2(a)、RMSE(b)和MAE(c)
Figure 10. R2 (a), RMSE (b) and MAE (c) of the models with four hyperparameter optimizations
图 11 4种超参数优化算法下GBDT的预测结果分布散点图
Figure 11. Prediction of GBDT with four hyperparameter optimization algorithms
图 13 GBDT分类模型(a)和BO-GBDT模型(b)预测结果混淆矩阵
Figure 13. Confusion matrix of GBDT classification model (a) and BO-GBDT model (b) prediction results
表 1 4种超参数优化算法参数设定、训练完成时间及最佳模型参数组合和交叉验证误差
Table 1. Parameter settings, training time, optimal parameter combinations and cross-validation errors for four optimization algorithms and models
优化算法 优化算法参数 最佳模型参数组合 训练完成
时间/s交叉验
证误差网格搜索 迭代次数：256 迭代次数：80
最大树深度：40
最小分割节点：5
特征分割数：386.52 0.40 粒子群
搜索迭代次数：30
粒子群数量：30
随机种子数：42迭代次数：76
最大树深度：63
最小分割节点：7
特征分割数：126.52 0.39 贝叶斯
搜索迭代次数：30
初始化参数：30
随机参数集：1000
随机种子数：42迭代次数：74
最大树深度：62
最小分割节点：7
特征分割数：369.23 0.38 超带搜索 最大预算：81
保留参数组合：3
随机种子数：42迭代次数：86
最大树深度：95
最小分割节点：8
特征分割数：2462.35 0.36 
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表 2 部分水库大坝震害因子取值与震害等级预测结果
Table 2. Values of seismic damage factors and predicted seismic damage levels of some reservoir dams
序号 坝名 建成时间/年 地震烈度/(°) 坝类型 坝长/m 坝高/m 坝顶宽/m 震害等级 Y1 Y2 1 上坝水库 1978 8 1 120 28.0 8 3 3.06 3.27 2 龙泉水库 1957 8 1 174 18.9 3.5 2 2.08 2.45 3 团结水库（玉皇） 1977 7 1 168 30.05 4 3 2.99 3.33 4 红刺藤水库 1962 9 1 156 12.0 6 4 4.07 2.33 5 民乐水库 1957 9 1 167 10.0 2 4 3.94 4.04 6 众力水库 1969 9 1 1095 9.3 2 2 2.09 2.67 7 新坪水库 1959 7 1 106 17.4 2 3 3.03 3.33 8 太平水库 1959 6 1 294 10.2 3.6 3 2.99 3.58 9 庆丰水库 1958 7 2 247 15.1 3 3 2.95 2.72 10 跃进水库 1958 7 1 284 11.3 3 3 2.99 2.45 11 拦沟堰水库 1960 6 1 234 5.0 3 1 1.45 1.29 12 八一水库 1975 7 1 130 10.5 2.5 4 3.83 3.82 13 罗家湾水库 1957 7 1 246 12.8 3.7 3 2.96 2.99 14 团结水库（百善） 1971 7 1 118 11.2 1 3 3.14 3.28 15 一根松水库 1973 7 1 167 9.8 3.3 3 3.00 3.82 16 尖梁子水库 1972 7 1 167 10 1.5 3 2.99 2.45 17 长岭水库 1957 7 1 256 10.6 3.3 3 3.00 2.99 18 新桥水库 1956 7 1 107 17 3.2 3 2.91 2.23 19 五七水库 1979 8 1 110 21.6 2.8 3 2.98 2.43 20 观音堂水库 1958 8 1 148 9 4 4 4.03 4.07 21 吴家大堰水库 1978 8 1 124 8 4 4 3.94 4.07 22 狮儿河水库 1958 8 1 131 17.4 4 4 3.89 4.07 23 合作水库 1975 8 1 120 20 2.5 4 3.93 4.07 24 岐山水库 1959 8 1 314 21 4 4 3.95 4.07 25 大田水库 1956 8 1 100.3 7.55 3 4 3.81 4.07 26 幸福水库 1971 8 1 101 14 4.5 3 3.03 2.23 27 牛角埝水库 1974 8 1 37 6 7 3 2.87 2.43 28 上游水库 1973 7 1 470 22 54 3 2.96 2.45 29 洞子沟水库 1985 8 1 182 18 2.2 3 3.00 2.97 30 火烧坡水库 1976 8 1 45 9 4 3 3.14 2.97 31 漆树坝水库 1974 8 1 38 10 3 3 3.09 2.97 32 三要水库 1975 7 1 400 7.4 13.52 3 2.92 3.33 33 和平水库 1977 8 1 175 24.4 3 3 2.91 2.67 34 金花水库 1957 8 1 240 18.6 3.14 3 3.13 2.67 35 崇林水库 1977 8 1 150 10 4.5 4 3.89 2.67 36 向阳水库 1964 7 1 405 20 4 3 3.01 2.99 37 朝阳水库 1985 7 1 130 18 10 3 2.90 3.33 38 红旗水库 1959 7 1 420 11.4 3 2 2.03 2.45 注：坝类型1表示均质坝；坝类型2表示黏土心墙坝；Y1为BO-GBDT模型预测的震害结果，Y2为改进的大坝震害经验统计模型预测的震害结果[10]
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表 3 模型对比分析
Table 3. Comparison between different models
模型 震害等级正确分类的土坝 震害等级错误分类的土坝 平均误差 相关系数 数量/座 比例/% 数量/座 比例/% 改进的土坝震害经验统计模型 26 68.4 12 31.6 0.363 0.908 BO-GBDT模型 38 100 0 0 0.070 0.972
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