Bayesian probabilistic representation of complex systems: With application to wave load modeling

Abstract

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.

A consistent second‐order hydrodynamic model in the time domain for floating structures with large horizontal motions

“…The second example clearly illustrates the importance to include the large horizontal motions in both the first‐ and second‐order analyses. As a final note, the present hydrodynamic model or similar should be used together with stochastic approaches (e.g., Glavind et al., 2021; Naess & Moan, 2012) to accurately predict the wave loads and structural responses in random waves.…”

A probabilistic deep reinforcement learning approach for optimal monitoring of a building adjacent to deep excavation

“…Thus far, manual decisions based on expert knowledge and work experience have been made to generate monitoring plans for adjacent buildings. However, such a process is likely to suffer from randomness, subjectivity, and inflexibility (Adeli & Yeh, 1989; Amezquita‐Sanchez et al., 2016; Glavind et al., 2021; Pan & Zhang, 2022, 2023; Pan et al., 2019). In particular, the large amount of complexity and uncertainty in the excavation–structure interaction and construction process makes it difficult for stakeholders to accurately grasp the structural safety states of the adjacent building and adaptively control the potential risk in real time.…”