Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate
Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is...
Main Authors: | Xu, Hai, Zhou, Jian, Asteris, Panagiotis G., Armaghani, Danial Jahed, Md. Tahir, Mahmood |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019
|
Subjects: | |
Online Access: | http://eprints.utm.my/88666/1/MahmoodMdTahir2019_SupervisedMachineLearningTechniquestothePrediction.pdf |
Similar Items
-
Tunnel boring machine performance prediction in tropically weathered granite through empirical and computational methods
by: Armaghani, Danial Jahed
Published: (2015) -
Predicting tunnel boring machine performance through a new model based on the group method of data handling
by: Koopialipoor, Mohammadreza, et al.
Published: (2019) -
Performance prediction of tunnel boring machine through developing a gene expression programming equation
by: Armaghani, D. J., et al.
Published: (2018) -
A gene expression programming model for predicting tunnel convergence
by: Hajihassani, Mohsen, et al.
Published: (2019) -
Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns
by: Sarir, Payam, et al.
Published: (2021)