Tunnel squeezing is a time-dependent process that typically occurs in weak or over-stressed rock masses, significantly influencing the budget and time of tunnel construction. This paper presents a new framework to probabilistically predict the potential squeezing intensity and to dynamically update the prediction during construction based on the sequentially revealed ground information. An extensively well-documented database, which contains quantitative data from 154 squeezing sections with 95 unpublished inventories is established. A Decision Tree method is employed to train a probabilistic multi-classification model to predict the tunnel squeezing intensity. The trained classifier is then integrated with a Markovian geologic model, which features embedded Bayesian updating procedures, to achieve a dynamic prediction on the state probabilities of the geologic parameter within the model and the resulting squeezing intensity during excavation. An under-construction tunnel case—Miyaluo #3 tunnel—is used to illustrate the proposed framework. Results show that the Decision Tree classifier, as opposed to other black-box models, is easy to be interpreted. It provides reliable predictive accuracy while leading to insights into the understanding of the squeezing problem. The strength-stress ratio (SSR) is suggested to be the most important factor. Moreover, the implementation of the updating procedures is efficient since only a simple field test (e.g. Point Load index or Schmidt rebound index) is required. Multiple rounds of predictions within the updating process allow different levels of prediction, for example long-range, short-term, or immediate, to be extracted as useful information towards the decision-making of construction operations. Therefore, this framework can serve as a pragmatic tool to assist the selection of optimal primary-support and other construction strategies based on the potential squeezing risk.

中文翻译：

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隧道挤压强度的动态和概率多类预测

隧道挤压是一个与时间相关的过程，通常发生在弱或超应力岩体中，显着影响隧道施工的预算和时间。本文提出了一种新的框架，可以概率性地预测潜在的挤压强度，并根据顺序显示的地面信息在施工期间动态更新预测。建立了一个记录广泛的数据库，其中包含来自 154 个挤压部分和 95 个未公开清单的定量数据。采用决策树方法训练概率多分类模型来预测隧道挤压强度。然后将经过训练的分类器与马尔可夫地质模型集成，该模型具有嵌入式贝叶斯更新程序，实现对模型中地质参数状态概率的动态预测以及挖掘过程中产生的挤压强度。一个在建隧道案例——米亚罗#3 隧道——用于说明所提出的框架。结果表明，与其他黑盒模型相比，决策树分类器易于解释。它提供了可靠的预测准确性，同时有助于深入了解挤压问题。强度-应力比（SSR）被认为是最重要的因素。此外，更新程序的实施是有效的，因为只需要简单的现场测试（例如点载荷指数或施密特回弹指数）。更新过程中的多轮预测允许不同级别的预测，例如长期、短期、或立即提取，作为对施工作业决策有用的信息。因此，该框架可以作为一种实用工具，根据潜在的挤压风险，协助选择最佳的初级支撑和其他建设策略。