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: |
Tongji University,
2020.
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| Subjects: | |
| Online Access: | Get fulltext |