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Challenges and Opportunities of Data Driven Advance Classification for Hard Rock TBM excavations

Challenges and Opportunities of Data Driven Advance Classification for Hard Rock TBM excavations

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1007/s00603-025-04542-4. This is version 3 of this Preprint.

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Authors

Georg H. Erharter , Paul Johannes Unterlaß, Nedim Radončić, Thomas Marcher, Jamal Rostami

Abstract

Excavation with tunnel Boring Machines (TBMs) is a widely used method of tunneling in all ground types including soil and rock today. The paper addresses the shift from traditional subjective methods to data-driven approaches for advance classification of TBMs in hard rock tunnel excavation. By leveraging continuous TBM operational data, these methodologies offer more objective, transparent, continuous and reproducible assessments of excavation conditions. The challenges include the need for sophisticated computational tools to interpret complex interactions between rock mass, TBM machinery, and logistics that are sensitive to the whol...  more

DOI

https://doi.org/10.31223/X5V99P

Subjects

Applied Statistics, Civil and Environmental Engineering, Civil Engineering, Earth Sciences, Geology, Geotechnical Engineering

Keywords

TBM tunnelling, Hard Rock TBM, TBM performance analysis, advance classification, data preprocessing, data driven classification

Dates

Published: 2024-09-21 06:34

Last Updated: 2025-04-03 05:40

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License

CC BY Attribution 4.0 International