Ensemble learning-based seismic vulnerability evaluation of near-fault horseshoe-shaped tunnels

Abstract

Near-fault pulse-like ground motions, characterized by high-energy velocity pulses, pose a significant threat to the seismic safety of underground structures. However, traditional seismic fragility analysis often relies on incremental dynamic analysis using scaled ground motions, which may distort the inherent distance-dependent spectral features of near-fault records. This study proposes a data-driven seismic fragility assessment framework for horseshoe-shaped tunnels utilizing Multiple Stripes Analysis (MSA) and ensemble learning architectures. A high-fidelity numerical model of the horseshoe-shaped tunnel is established, and MSA is adopted with unscaled ground motion records to preserve the inherent physical integrity of pulse-like signals. Based on this model, a large-scale dataset is generated through nonlinear dynamic simulations. Three distinct ensemble learners, Random Forest, Extreme Gradient Boosting, and Categorical Boosting (CatBoost), are developed as surrogate models to predict damage measures (DM) based on ground motion intensity measures (IMs). Results demonstrate that the CatBoost model achieves superior predictive performance, effectively capturing the complex nonlinear mapping between ground IMs and DM. Shapley Additive Explanations analysis quantifies the contributions of various IMs, revealing that velocity-based and energy-based indicators exhibit higher importance under pulse-like motions. Finally, seismic fragility curves are derived, showing good consistency with numerical results. The proposed framework provides a reliable and computationally efficient tool for the rapid seismic risk screening of tunnels in near-fault regions.

Graphical abstract

Keywords

near-fault pulse-like ground motion / ensemble learning / horseshoe-shaped tunnel / Shapley Additive Explanation / seismic fragility analysis

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The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn