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Managing Squeezing Rock Mass with TBM Data Analysis: Rail Link Rishikesh – Karnaprayag (India)

Managing Squeezing Rock Mass with TBM Data Analysis: Rail Link Rishikesh – Karnaprayag (India)

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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Authors

Georg H. Erharter , Sumit Jain, Øyvind Dammyr, Sjur Beyer, Rajinder Kumar Bhasin

Abstract

The 125.2 km rail link Rishikesh–Karnaprayag in the Lesser Himalayas of India represents a benchmark in mechanized tunnelling through complex geology. This paper focuses on Tunnel 8, a 14.58 km section excavated primarily using two single-shield hard rock tunnel boring machines (TBM) under challenging conditions characterized by tectonically deformed, partly water-bearing phyllites and high in-situ stresses, thus squeezing rock mass conditions were at hand. Both TBMs were equipped with advanced instrumentation, including a Void Measuring System (VMS) for real-time monitoring of shield gap size and tunnel wall deformation rates - an unprecedented application in single-shield TBM tunnelling. An integrated, near–real-time data analysis framework was developed to continuously assess TBM operational parameters, enabling proactive control and optimization of excavation performance. Within that framework, the VMS data was used to run a novel analytical model-based squeezing risk monitoring system which integrates gross TBM advance rate, tunnel wall deformation rate, shield length, and shield gap size. Additionally, tunnel seismic prediction was employed to characterize the geology ahead of the face, and supervised machine learning algorithms were implemented for rapid interpretation and visualization, facilitating informed decision-making by TBM operators. The project demonstrates how advanced monitoring systems and data-driven tunnelling can significantly enhance TBM performance and risk management in geotechnically adverse conditions. Key operational insights and recommendations are provided to guide future large-scale TBM projects in similar geological environments.

DOI

https://doi.org/10.31223/X5JN0X

Subjects

Geotechnical Engineering

Keywords

TBM excavation, Squeezing Ground, Hard Rock, Data analysis, machine learning

Dates

Published: 2025-10-24 20:55

Last Updated: 2025-10-24 20:55

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The code and data of the project cannot be shared openly due to confidentiality reasons.