In this contribution, we develop and present a Bayesian probabilistic framework for the representation of complex systems and apply this to an industrial case of offshore environmental load modeling. Based on previous contributions on probabilistic modeling using Bayesian networks, we consider the case where both the model structure and its parameters are estimated from data. Gaussian process‐based discrepancy modeling is introduced to represent uncertainties associated with data, when data are produced by models themselves. Two approaches are then introduced on how to deal with multiple model candidates, that is, Bayesian model averaging and decision context‐specific model selection. The latter comprising the main novelty of this paper. Two examples are provided: (i) a principal example illustrating the simple but fundamental idea of context‐specific model building and (ii) an industrial‐scale example considering optimal ranking of evacuation options for platform personnel in the event of an emerging storm.
We introduce a new concept that enables a decision analyst to explore and quantify the benefits of decision alternatives that exceed the scope of a pre‐posterior decision or value of information analysis. This new concept, namely, the expected value of sample information and action analysis, facilitates to examine decision alternatives that become only possible with additional knowledge. The concept is introduced by taking basis in proof load testing as a source of (pre‐)posterior knowledge. Pre‐posterior decision analysis is necessary in order to optimize the structural design through proof loading information. The application of the common value of information analysis and the new value of information and action analysis are demonstrated in a case study.
Modeling of fatigue crack growth plays a key role in risk informed inspection and maintenance planning for fatigue sensitive structural details. Probabilistic models must be available for observable fatigue performances such as crack length and depth, as a function of time. To this end, probabilistic fracture mechanical models are generally formulated and calibrated to provide the same probabilistic characteristics of the fatigue life as the relevant SN fatigue life model. Despite this calibration, it is recognized that the rather complex fracture mechanical models suffer from the fact that several of their parameters are assessed experimentally on an individual basis. Thus, the probabilistic models derived for these parameters in general omit possible mutual dependencies, and this in turn is likely to increase the uncertainty associated with modeled fatigue lives. Motivated by the possibility to reduce the uncertainty associated with complex multi-parameter probabilistic fracture mechanical models, a so-called normalized fatigue crack growth model was suggested by Tychsen (2017). In this model, the main uncertainty associated with the fatigue crack growth is captured in only one parameter. In the present contribution, we address this new approach for the modeling of fatigue crack growth from the perspective of how to best estimate its parameters based on experimental evidence. To this end, parametric Bayesian hierarchical models are formulated taking basis in modern big data analysis techniques. The proposed probabilistic modeling scheme is presented and discussed through an example considering fatigue crack growth of welds in K-joints. Finally, it is shown how the developed probabilistic crack growth model may be applied as basis for risk-based inspection and maintenance planning.
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