Deep learning model for predicting tunnel damages and track serviceability under seismic environment

Jammu and Kashmir in the northwestern part of the Himalayan region is frequently triggered with moderate to large magnitude earthquakes due to an active tectonic regime. In this study, a mathematical formulation-based Seismic Tunnel Damage Prediction (STDP) model is proposed using the deep learning (DL) approach. The pertinency of the DL model is validated using tunnel damage data from historical earthquakes such as the 1999 Chi-Chi earthquake, the 2004 Mid-Niigata earthquake, and the 2008 Wenchuan earthquake. Peak ground acceleration (PGA), source to site distance (SSD), overburden depth (OD), lining thickness (t), tunnel diameter (CYRILLIC CAPITAL LETTER EF), and geological strength index (GSI) were employed as inputs to train the Feedforward Neural Network (FNN) for damage state prediction. The performance evaluation results provided a clear indication for further use in a variety of risk assessment domains. When compared to models based on historical data, the proposed STDP model produces consistent results, demonstrating the robustness of the methodology used in this work. All models perform well during validation based on fitness metrics. The "STD multiple graphs" is also proposed which provide information on damage indexing, damage pattern, and crack predictive specifications. This can be used as a ready toolbox to check the vulnerability in post-seismic scenarios. The seismic design guidelines for tunnelling projects are also proposed, which discuss the damage pattern and suggest mitigation measures. The proposed STDP model, STD multiple graphs, and seismic design guidance are applicable to any earthquake-prone tunnelling project anywhere in the world.