Development of a novel IoT on power structures in ice storms conditions

Abstract

Freezing rain events are typically considered as the primary cause of structural failure or loss of functionality of a wide range of structural systems in cold regions. Consequently, the primary concern of the recent studies is to look for a solution to predict either the potential hazards (e.g., ice accumulation and wind actions) or estimate freezing rain-induced risk. In the first step, the current study develops a Performance-Based Freezing Rain Engineering (PBFRE) framework for estimation of the potential risk during freezing rain events based on theory of the total probability. PBFRE framework is mainly decomposed in different modules, namely hazard analysis, structural characterization, interaction analysis, structural analysis, damage analysis, loss analysis, and decision-making. Then, it expresses source of uncertainties during freezing rain events and defines performance expectations. Additionally, the application of the PBFRE framework is then illustrated through risk assessment of a transmission towerline system prone to freezing rain events. Additionally, the effect of employing different empirical ice accretion prediction model is highlight. Moreover, to investigate the response of the iced system, a Finite Element model consisting of three transmission towers and some conductors are developed. Various challenges prevent the application of PBFRE framework at structural and spatial scales. For instance, the computational cost for solving the risk problem is intense and it increases dramatically when it comes to applying the PBFRE framework for designing extended networks at extensive geographical regions. In addition, climate change effects are changing the precipitation rate and intensity that eventually changes the climatological patterns and most of so far developed framework are not accounted for such changes. Therefore, in the PBFRE framework potentials are designed to leverage the capabilities of advanced machine learning algorithms for solving the required computational cost and Internet of Things (IoT) to account for variation in climatological patterns. The results revealed the importance of employing empirical model has a fundamental impact of design and maintenance of structural systems prone to freezing rain. In addition, integration of IoT principles results in significant change in extreme freezing rain prediction which could literally affects the megaproject costs. In addition, utilizing advanced machine learning algorithms led to immediate prediction and reduce the prediction time by more than 98%.

Date	2 Jan 2025
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Michel Kadoch (Supervisor)