Interpretation of dam deformation and leakage with boosted regression trees
Interpretation of dam deformation and leakage with boosted regression trees
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DOI：
10.1016/j.engstruct.2016.04.012
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发表时间：
2016-07
影响因子：
5.5
通讯作者：
F. Salazar;M. Toledo;E. Oñate;B. Suárez
中科院分区：
文献类型：
--
作者：
F. Salazar;M. Toledo;E. Oñate;B. Suárez
预测模型是大坝安全评价的基础。它们传统上是基于简单的统计工具，如流体静力季节时间（HST）模型。众所周知，这些工具在准确性和可靠性方面存在局限性。近年来，机器学习及其相关技术作为HST的替代方案的应用实例越来越频繁。虽然它们被证明具有更高的灵活性和预测准确性，但它们也更难以解释。因此，绝大多数研究仅限于预测精度估计。在这项工作中，最流行的机器学习技术之一（提升回归树），被应用到模型8径向位移和4泄漏流在La Baells大坝。探讨了模型解释的可能性：计算每个预测因子的相对影响，并获得偏相关图。对这两个结果进行了分析，以得出有关大坝对环境变量的响应及其随时间的演变的结论。结果表明，该方法能有效地识别大坝性能变化，比简单的回归模型具有更高的灵活性和可靠性。
Predictive models are essential in dam safety assessment. They have been traditionally based on simple statistical tools such as the hydrostatic-season-time (HST) model. These tools are well known to have limitations in terms of accuracy and reliability. In the recent years, the examples of application of machine learning and related techniques are becoming more frequent as an alternative to HST. While they proved to feature higher flexibility and prediction accuracy, they are also more difficult to interpret. As a consequence, the vast majority of the research is limited to prediction accuracy estimation. In this work, one of the most popular machine learning techniques (boosted regression trees), was applied to model 8 radial displacements and 4 leakage flows at La Baells Dam. The possibilities of model interpretation were explored: the relative influence of each predictor was computed, and the partial dependence plots were obtained. Both results were analysed to draw conclusions on dam response to environmental variables, and its evolution over time. The results show that this technique can efficiently identify dam performance changes with higher flexibility and reliability than simple regression models.