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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Main Authors: | Zhou, Jian, Qiu, Yingui, Zhu, Shuangli, Armaghani, Danial Jahed, Khandelwal, Manoj, Mohamad, Edy Tonnizam |
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Format: | Article |
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KEAI Publishing Ltd
2021
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Online Access: | http://eprints.um.edu.my/28283/ |
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