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Applications of Structural Health Monitoring (SHM) for the design and analysis of Offshore Wind Turbine (OWT) structures has gained much attention within the past few years. Being exposed to repeated cyclic wind and wave loads, OWTs are dynamically sensitive structures and can benefit from monitoring systems to predict time-dependent deterioration. This study focuses on the quantification of the value of SHM information on the maintenance of OWT structures, with emphasis on fatigue of welded joint. By utilizing the decision tree, structural reliability, SHM data, as well as the cost-benefit assessments, a value of information (VoI) analysis can be done to model the fundamental decision of whether the service life of an OWT foundation can be extended beyond the initial design life. The application is demonstrated on a butt weld of a monopile support structure of a 3 MW offshore wind turbine with a hub height of approximately 71m where the prior probability analysis is modelled as a probabilistic fatigue damage model based on S-N approach and designed wind data. The posterior probability of failure of welded joints is updated using the three-year measured oceanographic and one-year strain data. The expected value of SHM information can be found as the difference between the maximum utility obtained in posterior analysis with SHM information and the maximum utility obtained using only prior information. This work can provide insights on how much benefits can be achieved through SHM information, with practical relevance on reliability-based design optimization and fatigue life extension of OWT structures.
Quantification of the posterior utilities of SHM campaigns on an orthotropic steel bridge deck
(2019)
This paper contains a quantification and decision theoretical optimization of the posterior utilities for several options for monitoring campaigns on the particular case of fatigue life predictions of an orthotropic steel deck. The monitoring campaigns are defined by varying monitoring durations and phases. The decision analysis is performed with real data from the Structural Health Monitoring (SHM) of the Great Belt Bridge (Denmark) which, among others, consist of measured strains, pavement temperatures and traffic intensities. The fatigue loading prediction model is based on regression models linking daily averaged pavement temperatures, daily aggregated heavy-traffic Counts and derived S-N fatigue damages, all of them derived from the outcomes of different monitoring campaigns. A probabilistic methodology is utilized to calculate the fatigue reliability profiles of selected instrumented welded joints. The posterior utilities of SHM campaigns are then quantified by considering the structural fatigue reliability, various monitoring campaigns and the corresponding cost-benefit models. The decisions of identifying the optimal monitoring campaign and of extending the service life or not in conjunction with monitoring results are modelled. The optimal monitoring campaign is identified - retrospectively - by maximizing the expected benefits and minimize risks in dependency of the monitoring duration and the monitoring associated costs. The results, despite relying on a number of simplistic assumptions, pave the way towards the use of pre-posterior decision support to optimise the design of monitoring campaigns for similar bridges, with an overall goal to proof the cost efficiency of SHM approaches to civil infrastructure management.