Journal
ROCK MECHANICS AND ROCK ENGINEERING
Volume 53, Issue 8, Pages 3521-3542Publisher
SPRINGER WIEN
DOI: 10.1007/s00603-020-02138-8
Keywords
Tunnel squeezing; Multi-classification; Dynamic prediction; Decision tree; Bayesian updating; Markov process
Funding
- National Natural Science Foundation of China [U19A20111, 41772329]
- Sichuan Science and Technology Program [2019YFG0460]
- China State Railway Group Corporation Limited [2017G008-F]
- Chengdu University of Technology Program for Young Teachers [10912-2019KY20-06354]
- Independent Research Project of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection [SKLGP2017Z001]
- EU [813137]
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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 An extensively well-documented database, which contains quantitative data from 154 squeezing sections with 95 unpublished inventories is established.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.
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