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http://dx.doi.org/10.7474/TUS.2021.31.6.494

A Study on the Prediction of Rock Classification Using Shield TBM Data and Machine Learning Classification Algorithms  

Kang, Tae-Ho (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Choi, Soon-Wook (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Lee, Chulho (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Chang, Soo-Ho (Construction Industry Promotion Department, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Tunnel and Underground Space / v.31, no.6, 2021 , pp. 494-507 More about this Journal
Abstract
With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.
Keywords
TBM; Rock; classification; Machine learning; Prediction;
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Times Cited By KSCI : 1  (Citation Analysis)
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