Integrated Component and System–Based Framework for Fragility Assessment of Lattice Transmission Towers under Combined Wind and Ice Loading

Fragility assessment of lattice towers under climatic hazards, particularly under combined wind and ice loading, is crucial for analyzing the vulnerability, risk, and resilience of power transmission lines. However, few probabilistic studies have developed comprehensive structural models that account for the complexity of tower connections and key uncertainties. Moreover, conventional approaches often rely on simplified limit-state criteria and approximate probabilistic methods. This study proposes a fragility assessment framework with two objectives: employing a detailed numerical model to better predict failure capacity, and establishing an efficient method for estimating failure probability. Geometric and mechanical imperfections are incorporated as sources of uncertainty within a numerical model including connection details (complex model). Tower collapse probability is assessed using an integrated component and system–based framework, in which critical members are identified using a realistic limit-state function, and progressive collapse analysis, assisted by machine learning, simulates failure propagation and overall structural collapse. The framework was applied to a tower-line system using a simplified model, with regression-based relationships linking its outputs to those of the complex model. Dynamic analyses under combined wind and ice loading were performed, and the bivariate fragility assessment—through fragility surfaces and curves—captured the effects of wind speed, wind attack angle, and ice thickness. The results underscore the importance of considering combined hazards, demonstrating that the approach effectively reflects interactions among multiple loading intensities. Compared with conventional methods, the proposed framework improves failure probability estimation by accounting for key structural uncertainties, incorporating member-level behavior, and evaluating their contributions to overall system failure via progressive collapse analysis.