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...

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Bibliographic Details
Main Authors: Jian Zhou, Yingui Qiu, Shuangli Zhu, Danial Jahed Armaghani, Manoj Khandelwal, Edy Tonnizam Mohamad
Format: Article
Language:English
Published: Elsevier 2021-10-01
Series:Underground Space
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2467967420300507