To aid decision making about environmental systems under deep uncertainty, robustness metrics are commonly used to represent system performance over a number of scenarios. However, there are many robustness metrics and many ways of generating scenarios, making it difficult to know which to choose in order to quantify system robustness and to make robust decisions. To address this shortcoming, we introduce a generic guidance framework to assist with the identification of the most robust decision alternatives, as well as the RAPID (Robustness Analysis Producing Intelligent Decisions) software package which is a consistent and easy-to-use implementation of the framework. We illustrate the framework and software package on a hypothetical lake pollution problem, known as The Lake Problem, showing how the framework and software package apply to several situations where decision-makers may or may not know which scenarios or robustness metrics to use.@en

Multiple plausible future scenarios are being used increasingly in preference to a single deterministic or probabilistic prediction of the future in the long-term planning of water resources systems. These scenarios enable the determination of the robustness of a system—the consideration of performance across a range of plausible futures—and allow an assessment of which possible future system configurations result in a greater level of robustness. There are many approaches to selecting scenarios, and previous studies have observed that the choice of scenarios might affect the estimated robustness of the system. However, these observations have been anecdotal and qualitative. This paper develops a systematic, quantitative methodology for exploring the influence of scenario selection on the robustness and the ranking of decision alternatives. The methodology is illustrated on the Lake Problem. The quantitative results obtained confirm the qualitative observations of previous works, showing that the selection of scenarios is important, as it has a large influence on the robustness value calculated for each decision alternative. However, we show that it has a relatively small influence on how those decision alternatives are ranked. This implies that despite the difference in robustness values, similar decision outcomes will be reached in this case study, regardless of the basis on which the scenarios are obtained. It is also revealed that the impact of the scenarios on the robustness values is due to complex interactions with the system model and robustness metrics.

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Composite materials combine the advantages of lightweight and high strength. This is in particular of interest for the development of large constructions e.g. aircraft, space applications, wind turbines etc. One of the shortcomings of using composite materials is the complex nature of the failure mechanisms which makes it difficult to predict the remaining lifetime. Therefore, condition and health monitoring are important for using composite materials for the critical parts of a construction. Different types of sensors are used to monitor composite structures. These include ultrasonic, thermography, shearography and fiber optics. Optical fiber sensors can be surface mounted or embedded in the composite construction to provide the unique advantage of in-operation measurement of mechanical strain and other parameters of interest. This is identified as a promising technology for Structural Health Monitoring (SHM) or Prognostic Health Monitoring (PHM) of composite constructions. Among the different fiber optic sensing technologies, Fiber Bragg Grating (FBG) sensors are the most mature and widely used ones. FBG sensors can be realized in an array configuration with many FBGs in a single optical fiber. In the current project, different aspects of using embedded FBG for composite wind turbine monitoring are investigated. The demonstration of using FBG sensor array for temperature and strain sensing and monitoring of a 5.8 m long scale model of a glass fiber monopile is investigated. The results indicate that the embedded FBGs successfully show the temperature increase during curing of the resin. During the static and dynamic tests, the embedded FBG sensors provided accurate monitoring.@en