Intelligent Early Warning and Control System for TBM Tunneling Based on Deep Learning
Background and Challenges
Traditional TBM risk control technologies suffer from fundamental deficiencies that make them difficult to adapt to the high-quality development requirements of the new era. When facing complex geological environments such as high ground stress, fractured zones, and alteration zones, construction decision-making still relies excessively on traditional empirical judgment and static numerical simulation, commonly exhibiting technical bottlenecks including delayed prediction, sluggish response, and low accuracy rates. Existing early warning systems lack real-time capability and intelligence, with risk identification accuracy of only 60-70% and response times lasting several hours, fundamentally failing to meet the stringent millisecond-level warning requirements for high-speed TBM tunneling. More critically, traditional methods cannot achieve deep fusion of multi-source heterogeneous data and intelligent modeling of physical mechanisms, resulting in severely insufficient prediction reliability under complex working conditions. This has become the greatest technical obstacle constraining the intelligent upgrading of China's tunnel engineering.
Revolutionary Solutions Through Artificial Intelligence
Artificial intelligence technology provides a revolutionary pathway to breakthrough the aforementioned technical bottlenecks. Intelligent prediction methods based on deep learning demonstrate disruptive advantages in prediction accuracy, response speed, adaptive capability, and cost-effectiveness, capable of elevating TBM risk prediction accuracy from the traditional 60-70% to over 85-95%, while compressing response time from hours to milliseconds, providing powerful technical assurance for achieving safe and efficient TBM tunneling. However, existing AI methods generally exhibit "black box" characteristics, lack physical mechanism constraints, and have insufficient generalization capability under complex geological conditions, making them difficult to meet practical engineering requirements.
Innovative CHG Algorithm Framework
Therefore, this project innovatively establishes a Comprehensive Hybrid Gaussian (CHG) algorithm, achieving breakthrough fusion of Gaussian process uncertainty quantification with cross-attention mechanisms, realizing a major technical breakthrough in TBM dual-modal dynamic early warning control. The project proposes for the first time the Failure and Performance Prediction Index (FPPI), deeply integrating the contact mechanics and fluid dynamics mechanisms in the TBM tunnel face cutting process, constructing an intelligent prediction framework combining physical constraints with data-driven approaches. The TBM tunneling dynamic early warning platform developed based on the CHG algorithm integrates TGS advanced geological radar prediction with real-time tunnel face image recognition, achieving a fundamental transformation from "passive emergency response" to "proactive prevention."
Practical Engineering Significance
The practical engineering significance of this platform is profoundly substantial:
Technical Level
The CHG algorithm achieves an ultra-high prediction accuracy of 98.2%, with response time controlled within 100 milliseconds, thoroughly resolving the core pain point of prediction lag in traditional methods.
Safety Level
The system can identify major risks such as tunnel face instability and surrounding rock collapse in advance, with warning time advanced by 30-60 minutes, securing precious time for emergency response and preventing major safety accidents at their source.
Economic Level
Through dynamic parameter optimization, TBM tunneling efficiency is improved by 15-25%. For a single large-scale project, construction period can be reduced by 2-4 months, saving economic losses of 10-50 million RMB.
Industrial Level
This technology breaks through foreign technical monopoly, filling the domestic gap in TBM intelligent early warning, providing critical technical support for promoting China's tunnel engineering transformation from a "manufacturing powerhouse" to an "intelligent manufacturing powerhouse."
https://www.doi.org/10.1007/s11709-025-1242-zStrategic Impact
More importantly, this platform constructs a replicable and scalable intelligent solution, providing safety assurance for over 8,000 kilometers of tunnel projects under construction nationwide, directly serving the national strategies of building a strong transportation nation and new infrastructure construction, with significant social value and strategic importance.
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Guan,Wencan
(He/Him)
PHD, University of Shanghai for Science and Technology
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Building Construction and Design
Technology and Engineering > Civil Engineering > Building Construction and Design
Underground Engineering and Tunnel Construction
Technology and Engineering > Civil Engineering > Geoengineering > Underground Engineering and Tunnel Construction
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