Towards TBM Automation: On-The-Fly Characterization and Classification of Ground Conditions Ahead of a TBM Using Data-Driven Approach

Towards TBM Automation: On-The-Fly Characterization and Classification of Ground Conditions Ahead of a TBM Using Data-Driven Approach

Pre-tunneling exploration for rock mass classification is a common practice in tunneling projects. This study proposes a data-driven approach that allows for rock mass classification. Two machine learning (ML) classification models, namely random forest (RF) and extremely randomized tree (ERT), are...

Full description

Bibliographic Details
Main Authors: Sylvanus Sebbeh-Newton, Prosper E.A. Ayawah, Jessica W.A. Azure, Azupuri G.A. Kaba, Fauziah Ahmad, Zurinahni Zainol, Hareyani Zabidi
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Applied Sciences
Subjects:
tunnel boring machine (TBM)
rock classification
Japanese highway classification system (JH System)
random forest (RF)
extremely randomized trees (ERT)
Online Access:https://www.mdpi.com/2076-3417/11/3/1060

Internet

https://www.mdpi.com/2076-3417/11/3/1060

Similar Items