Landslide dams are a common hazard which threaten downstream human settlement or infrastructure, as their collapse may result in a flash flood. The danger is compounded by the amount of water accumulated; therefore, estimation of the time to failure becomes crucial for assessing engineering risk mitigation procedures. Dam dimension indices, descriptive multivariate analysis and logistic regression have been used to produce a static image of the dam conditions, estimating the probability of failure, but they provide no information of when a failure might occur. We propose a Bayesian model to predict the time to failure of landslide dams, based on imputing missing dam and reservoir measurements via an analysis of their covariate structure. The resulting data is then used in a Bayesian survival model which links the (censored) failure time to dam and reservoir variables. A case study on heterogeneous Italian events is presented, on which our model is tested. Results show that it is possible to produce a probabilistic model of time to failure. The length and height of dams, and the catchment area behind them, are identified as the most important covariates controlling the time to failure. The addition of area-based intercepts confirms the robustness of the methodology. Examples of potential results (forecasting) and possible generalizations of the model are presented.

中文翻译：

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滑坡坝的贝叶斯寿命分析

山体滑坡水坝是威胁下游人类住区或基础设施的常见危害，因为它们的倒塌可能导致山洪暴发。积水量增加了危险；因此，估计失效时间对于评估工程风险缓解程序至关重要。大坝尺寸指数、描述性多变量分析和逻辑回归已被用于生成大坝条件的静态图像，估计失败的概率，但它们没有提供何时可能发生失败的信息。我们提出了一个贝叶斯模型来预测滑坡大坝的失效时间，基于通过分析它们的协变量结构来估算缺失的大坝和水库测量值。然后将所得数据用于贝叶斯生存模型，该模型将（审查的）破坏时间与大坝和水库变量联系起来。介绍了一个关于异质意大利事件的案例研究，我们的模型在其上进行了测试。结果表明，可以生成失效时间的概率模型。大坝的长度和高度，以及它们后面的集水区，被认为是控制破坏时间的最重要的协变量。添加基于区域的截距证实了该方法的稳健性。介绍了模型的潜在结果（预测）和可能的概括示例。大坝的长度和高度，以及它们后面的集水区，被认为是控制破坏时间的最重要的协变量。添加基于区域的截距证实了该方法的稳健性。介绍了模型的潜在结果（预测）和可能的概括示例。大坝的长度和高度，以及它们后面的集水区，被认为是控制破坏时间的最重要的协变量。添加基于区域的截距证实了该方法的稳健性。介绍了模型的潜在结果（预测）和可能的概括示例。