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: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Tongji University
2020
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Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/91061/1/EdyTonnizamMohamad2020_EstimationoftheTBMAdvanceRateUnderHardRockConditions.pdf http://eprints.utm.my/id/eprint/91061/ http://dx.doi.org/10.1016/j.undsp.2020.05.008 |
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