Probabilistic Risk Assessment of Tunnel Seismic Damage Under Physically Based Non-Stationary Earthquakes

The seismic performance of tunnel structures is significantly influenced by the randomness of ground motions. Traditional probabilistic risk assessments, which rely on limited recorded ground-motion data, often suffer from small-sample bias and fail to capture the full distribution of seismic input. To overcome this limitation, this study employs a physically based stochastic ground-motion model to generate a large and statistically representative sample ensemble. A probabilistic seismic risk assessment framework is then developed using the stochastic finite element method, explicitly incorporating ground-motion uncertainty. Four statistical criteria, namely practicality, correlation, efficiency, and proficiency, are systematically applied to evaluate candidate intensity measures ( IM s) and identify the optimal one. Among all candidates, PGA exhibits the best overall performance, with the highest regression fitness ( R 2 = 0.873) and the lowest dispersion ( β D = 0.197), followed by PGV ( R 2 = 0.848, β D = 0.215). Fragility curves for different damage states are subsequently derived. Results indicate that structural responses vary considerably under stochastic ground-motion excitation, and the failure probability follows a typical S-shaped curve as intensity increases. Moreover, the failure probabilities for different damage states exhibit nonlinear growth at higher intensity levels. These findings provide a mathematical basis for probability-based seismic design and risk assessment of tunnel structures.