Predicting landslide failure time is a critical issue in geotechnical engineering. Traditional methods often rely on the empirical power law of material failure to deterministically predict this time, which depends heavily on the accurate selection of precursor time series and the precise identification of the onset of the acceleration (OOA) deformation stage. In this paper, we present an innovative approach that couples the Moving Average Convergence and Divergence (MACD) method with the Bayesian update method, and derive a new model for calculating landslide failure time. The MACD method is employed to divide creep landslide displacement into three distinct deformation stages, accurately pinpointing the OOA point. Following this, we introduce the novel calculation model to analyze landslide displacement time series after the OOA point. Finally, the Bayesian update method, combined with the Markov Chain Monte Carlo (MCMC) method, is employed to probabilistically predict landslide failure time. Taking the Wolongsi, Xintan and Dexing Open-pit mine landslides as examples, the proposed method is employed to divide the three deformation stages and predict the landslide failure time. Moreover, the predicted failure time is in good agreement with the actual failure time, indicating the proposed model's ability to accurately predict landslide failure time.

中文翻译：

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基于移动平均收敛散耦合的贝叶斯更新方法滑坡破坏时间预测


预测滑坡破坏时间是岩土工程中的一个关键问题。传统方法往往依靠材料失效的经验幂律来确定性地预测这个时间，这在很大程度上取决于前驱体时间序列的准确选择和加速 （OOA） 变形阶段开始的精确识别。在本文中，我们提出了一种创新方法，将移动平均收敛和发散 （MACD） 方法与贝叶斯更新方法相结合，并推导出一种计算滑坡破坏时间的新模型。采用 MACD 方法将蠕变滑坡位移分为 3 个不同的变形阶段，准确定位 OOA 点。在此之后，我们引入了新的计算模型来分析 OOA 点之后的滑坡位移时间序列。最后，采用贝叶斯更新方法结合马尔可夫链蒙特卡洛 （MCMC） 方法对滑坡破坏时间进行概率预测。以卧龙寺、新坛和德兴露天矿滑坡为例，采用所提方法划分三个变形阶段，预测滑坡破坏时间。此外，预测的破坏时间与实际破坏时间吻合较好，表明所提模型能够准确预测滑坡破坏时间。