Multivariate logistic wind fragility functions of overhead distribution poles

Overhead distribution lines are highly vulnerable to extreme wind events, yet fragility modeling remains challenging due to multiple interacting failure mechanisms and strong dependence on asset and site characteristics. This study presents a physics-informed and scalable framework for modeling multivariate wind-induced fragility of overhead utility networks. The approach integrates stochastic wind simulation, generated to match the Kaimal turbulence spectrum and validated using spectral error metrics, with finite-element–based structural analysis to capture governing failure mechanisms, including pole rupture, pole overturning, and conductor tensile breakage. Mode-specific multivariate logistic regression models are calibrated as parameterized, closed-form surrogate fragility functions based on measurable asset, geotechnical, and hazard descriptors, allowing for direct application without the need for repeated finite-element simulations or statistical analysis. The resulting models demonstrate strong and stable predictive performance across pole classes and failure modes, with AUC values exceeding 0.97. Joint failure probability is quantified using both independence-based aggregation and a copula-based formulation that captures dependence between competing failure modes, enabling consistent risk aggregation from individual spans to feeder-level applications. By combining physics-based modeling with computational efficiency and closed-form fragility expressions, the proposed framework supports practical, risk-informed screening and planning of power distribution infrastructure under extreme wind hazards.