Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesia...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2021-10-01
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Series: | Underground Space |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967420300507 |