Dynamic and Probabilistic Multi-class Prediction of Tunnel Squeezing Intensity

Chen, Yu ORCID: 0000-0001-6617-2946, Li, Tianbin, Zeng, Peng, Ma, Junjie, Patelli, Edoardo ORCID: 0000-0002-5007-7247 and Edwards, Ben ORCID: 0000-0001-5648-8015 (2020) Dynamic and Probabilistic Multi-class Prediction of Tunnel Squeezing Intensity. ROCK MECHANICS AND ROCK ENGINEERING, 53 (8). pp. 3521-3542.

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Abstract

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 An extensively well-documented database, which contains quantitative data from 154 squeezing sections with 95 unpublished inventories is established.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.

Item Type:	Article
Uncontrolled Keywords:	Tunnel squeezing, Multi-classification, Dynamic prediction, Decision tree, Bayesian updating, Markov process
Depositing User:	Symplectic Admin
Date Deposited:	12 Jun 2020 08:52
Last Modified:	18 Jan 2023 23:49
DOI:	10.1007/s00603-020-02138-8
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3090066