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Deep learning model for predicting tunnel damages and track serviceability under seismic environment
Abdullah Ansari1, K S Rao1, A K Jain1
1Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016 India.
Modeling Earth Systems and Environment
|October 25, 2022
Summary
A new Seismic Tunnel Damage Prediction (STDP) model uses deep learning (DL) to forecast earthquake-induced tunnel damage. This robust tool aids in seismic risk assessment for tunnels globally.
Area of Science:
- Geotechnical Engineering
- Seismology
- Artificial Intelligence
Background:
- The Himalayan region, particularly Jammu and Kashmir, experiences frequent seismic activity due to its active tectonic setting.
- Tunnels in seismically active zones are vulnerable to damage from moderate to large magnitude earthquakes.
- Accurate prediction of seismic tunnel damage is crucial for infrastructure safety and risk management.
Purpose of the Study:
- To propose a novel mathematical formulation-based Seismic Tunnel Damage Prediction (STDP) model.
- To leverage deep learning (DL) approaches for predicting tunnel damage states under seismic loading.
- To develop practical tools and guidelines for seismic resilience in tunnelling projects.
Main Methods:
- Development of a deep learning (DL) model, specifically a Feedforward Neural Network (FNN).
- Training the FNN using historical earthquake data (1999 Chi-Chi, 2004 Mid-Niigata, 2008 Wenchuan).
- Utilizing input parameters including Peak Ground Acceleration (PGA), Source to Site Distance (SSD), Overburden Depth (OD), lining thickness (t), tunnel diameter (Ф), and Geological Strength Index (GSI).
Main Results:
- The proposed STDP model demonstrated consistent and robust performance in predicting tunnel damage states.
- Validation against historical data confirmed the model's reliability across various seismic events.
- Introduction of 'STD multiple graphs' for damage indexing, pattern analysis, and crack prediction, serving as a post-seismic vulnerability assessment toolbox.
Conclusions:
- The developed STDP model, along with STD multiple graphs and proposed seismic design guidelines, offers a valuable framework for assessing and mitigating seismic risks in tunnels.
- The methodology is applicable globally to any earthquake-prone tunnelling project.
- The study provides a robust, data-driven approach to enhance the seismic resilience of underground infrastructure.
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