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.
|Computer-Aided Civil and Infrastructure Engineering
|Number of pages
|Published - Jul 2022
Bibliographical noteFunding Information:
The authors kindly acknowledge the Danish Underground Consortium (Total E&P Denmark, Noreco & Nordsøfonden) for granting the permission to publish this work. A special thanks goes to Shell Research Ltd., Danish Hydraulic Institute, Total E&P, and Hans Fabricius Hansen (Haw Metocean) for their support in the project. This research has received funding from the Danish Hydrocarbon Research and Technology Centre (DHRTC) under the Structural Integrity and Lifetime Evaluation program.
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