Prediction of BLEVE-induced response of road tunnel using Transformer network with modified self-attention (SAMT)

Abstract

Road tunnels might be exposed to Boiling Liquid Expansion Vapor Explosion (BLEVE) due to the transportation of liquified gas tankers passing through road tunnels. An efficient and accurate prediction of the response of road tunnels under internal BLEVEs can facilitate the reliable BLEVE-resistant design and risk assessment of road tunnels. This study introduces an advanced deep-learning model that employs a Transformer-based architecture with a modified self-attention mechanism, termed as Self-Attention Modified Transformer (SAMT), to predict BLEVE-induced support rotation of tunnel structure, which is a common criterion in assessing reinforced concrete structure damage to blast loads. Unlike the Transformer with the traditional self-attention mechanism, the proposed SAMT effectively aggregates global information across all variables while mitigating undue dependencies among uncorrelated variables. Consequently, the proposed SAMT is better suited for processing tabular data with uncorrelated variables. The feasibility and advantages of the proposed SAMT are verified by extensive data generated using calibrated numerical models of box-shaped road tunnels subjected to internal BLEVEs. By comparing the performance of the proposed SAMT with the non-modified Transformer network (FT-Transformer) as well as two other typical deep learning networks, i.e., Multi-layer Perceptron (MLP) and Residual Network (ResNet), it is found that the SAMT offers higher prediction accuracy and robustness than the other three models in predicting BLEVE-induced support rotations of box-shaped road tunnels. The study demonstrates that the proposed SAMT is an effective tool for the prediction of BLEVE-induced support rotations of road tunnels.