A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Dynamic and Probabilistic Multi-class Prediction of Tunnel Squeezing Intensity
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 An extensively well-documented database, which contains quantitative data from 154 squeezing sections with 95 unpublished inventories is established.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.
Dynamic and Probabilistic Multi-class Prediction of Tunnel Squeezing Intensity
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.
Dynamic and Probabilistic Multi-class Prediction of Tunnel Squeezing Intensity
Chen, Yu (author) / Li, Tianbin (author) / Zeng, Peng (author) / Ma, Junjie (author) / Patelli, Edoardo (author) / Edwards, Ben (author)
2020
Article (Journal)
Electronic Resource
English
BKL:
38.58
Geomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB41
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