REAL-TIME ROCK MASS QUALITY PREDICTION USING ONLINE TRANSFER LEARNING IN TBM TUNNELLING

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Accurate and real-time perception of rock mass quality during Tunnel Boring Machine (TBM) excavation is essential for ensuring operational safety, optimizing support measures, and minimizing construction risks. This challenge becomes even more critical in newly constructed tunnels, where limited prior geological investigations restrict the ability to promptly evaluate surrounding rock conditions. To address this issue, this study proposes a physical feature–shared online transfer learning framework that enables cross-engineering rock mass quality prediction using monitored data from previously completed tunnels.

Framework Overview

The proposed framework integrates three core components:

1️⃣ Online Data Stream Processor

A real-time data acquisition and processing module was developed to handle continuous TBM monitoring data. This processor performs:

  • Real-time data collection

  • Data segmentation

  • Stream-based feature extraction

Importantly, it extracts physical shared features derived from the rock-cutting process, which are transferable across different tunnel projects due to their consistent physical meaning and accessibility.

2️⃣ Online Learning Module (LSTM-PSM)

An advanced classification module based on:

  • Long Short-Term Memory (LSTM) networks

  • Probabilistic State Machine (PSM)

was designed to estimate classification probability evolution in real time.

This hybrid LSTM-PSM model captures both temporal dependencies in TBM operational data and state transition characteristics of rock mass classifications, enabling dynamic and adaptive prediction.

3️⃣ Cross-Engineering Transfer Learning

The framework leverages completed tunnel data as a source domain and transfers knowledge to a new tunnel project (target domain), reducing reliance on extensive new-site training data.

Case Study Validation

The framework was validated using real-world engineering data:

  • Source domain: Songhua River Tunnel (YS, 19.77 km)

  • Target domain: Chaoer to Xiliao River Tunnel (YC, 2.27 km)

Field data from the longer, completed tunnel were used to train the transferable model, which was then applied to predict rock mass quality in the shorter, newly constructed tunnel.

Key Findings

The results demonstrate several significant advantages:

✔ Accurate Rock Mass Classification

The proposed online transfer learning framework accurately identifies:

  • Rock mass quality grades

  • State transition processes

in real-time excavation scenarios.

✔ Superior Physical Shared Features

Features derived from the rock-cutting process showed:

  • Strong similarity across different tunnels

  • High transferability

  • Ease of acquisition during TBM operation

This makes them ideal candidates for cross-project intelligent modeling.

✔ Improved Model Performance

The online LSTM-PSM module outperformed state-of-the-art offline transfer learning methods in:

  • Prediction accuracy

  • Model reliability

  • Adaptability to dynamic excavation conditions

Unlike offline methods, the proposed system continuously updates and adapts to evolving geological conditions.

Engineering Significance

This study introduces a novel approach for cross-engineering real-time rock mass quality perception. By combining physical feature extraction with online transfer learning, the framework:

  • Enhances rock mass stability assessment

  • Supports timely selection of support measures

  • Improves tunnelling safety and efficiency

  • Reduces dependence on extensive prior geological surveys

The approach represents a major step toward intelligent TBM excavation and digital tunnelling systems.

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#InfrastructureInnovation
#MachineLearning
#RockMechanics
#CivilEngineering
#EngineeringGeology
#ConstructionTechnology


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