Slurry Shield Cutterhead Torque Characterization Using AI Machine Learning and Mechanics-Based Modeling - NAT2024
- Organization:
- Society for Mining, Metallurgy & Exploration
- Pages:
- 9
- File Size:
- 725 KB
- Publication Date:
- Jun 23, 2024
Abstract
This paper documents an effort to explain cutterhead torque behavior observed during slurry pressure balance TBM tunneling through soft ground on the Los Angeles Clearwater project. AI machine learning and mechanics model-based learning was used to characterize cutterhead torque behavior including the TBM operating and ground parameters that influence cutterhead torque. Also, key parameters were from the slurry circuit data, including fines content, in-situ density, pore water fraction and solids content were included. The paper details what was learned using these new data-driven approaches as well as limitations with AI machine learning.
Citation
APA: (2024) Slurry Shield Cutterhead Torque Characterization Using AI Machine Learning and Mechanics-Based Modeling - NAT2024
MLA: Slurry Shield Cutterhead Torque Characterization Using AI Machine Learning and Mechanics-Based Modeling - NAT2024. Society for Mining, Metallurgy & Exploration, 2024.