A data-driven physics-informed stochastic framework for hurricane-induced risk estimation of transmission tower-line systems under a changing climate

Abstract

Hurricanes are one of the most devastating natural hazards which result in significant damage to civil infrastructure. The transmission tower-line system is specifically highly vulnerable to hurricane effects. Recent studies have indicated that the collapse of the transmission tower-line system during hurricane events is mainly due to the joint occurrence of wind and rain. Therefore, it is important to assess the failure probabilities of these structures under wind and rain-induced loads. With climate change, those probabilities are expected to significantly change where the brunt of the damage and economic losses would be disproportionately borne by coastal communities. This study proposes a hybrid risk-responsive, data-driven and physics-informed framework which evaluates the hurricane-induced damage to transmission lines in vulnerable regions and communities under several climate scenarios. The framework is founded upon an advanced physics informed hurricane simulation model followed by a Gaussian kernel density technique to efficiently estimate the intensity measures and quantify the associated uncertainties accurately. The generated hazard intensities are coupled with fragility surfaces expressed in terms of wind speed and rain intensity. Then, the failure probabilities are obtained for the transmission tower-line system under the joint excitation and future climate scenario. The framework is applied to several coastal cities along the US east coast. It is shown that, due to the involved system nonlinearity, the obtained failure probabilities change drastically with the future climate scenario. In addition, rainfall-induced loads which significantly impact the failure probabilities of the transmission tower-line systems are not only function of the selected sites, but also depend on the considered limit state and climate scenario. Therefore, their consideration is important for the structural design and estimation of the system performance.