Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

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:
TBM performance
Advance rate
XGBoost
Bayesian optimization
Predictive modeling
Online Access:http://www.sciencedirect.com/science/article/pii/S2467967420300507

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