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

• Main Authors: Zhou, Jian, Qiu, Yingui, Zhu, Shuangli, Armaghani, Danial Jahed, Khandelwal, Manoj, Mohamad, Edy Tonnizam
• Format: Article
• Published: KEAI Publishing Ltd 2021
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/28283/

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...