Domenic Di Francesco
My research project
System effects in structural integrity management
The project is developing a generic Bayesian decision analysis framework to quantify the expected value of information from inspections on a structure. The influence of selected system effects on the analysis is being investigated. These include:
Dependencies in degradation modelling
Fatigue is the primary damage mechanism that is considered. Bayesian data analysis is being used to fit predictive models that coherently and consistently account for variability within and dependency between model parameters. In addition, the effect of dependency between uncertain inputs is also being considered.
Improved characterisation of imperfect information
The project is developing improved methods of characterising inspection activities in terms of the precision, bias and relevance of the information that they provide, as well as the risks and expected costs of completion. This will allow a more comprehensive evaluation regarding the extent to which they facilitate improved risk management strategy.
Spatial dependencies
Where dependencies can be quantified between structural condition at different locations on a structure, the project will quantify the additional value of information that results from any inference (updating) that can be completed at locations other than those directly inspected.
My publications
Publications
Engineers perform fatigue assessments to support structural integrity management. Given that the purpose of these calculations is linked to problems of decision making under various sources of uncertainty, probabilistic methods are often more useful than deterministic alternatives. Guidance on the direct probabilistic application of procedures in existing industrial standards is currently limited and dependencies between marginal probabilistic models are generally not considered, despite their potential significance being acknowledged. This paper proposes the use of Bayesian data analysis as a flexible and intuitive approach to coherently and consistently account for uncertainty and dependency in fatigue crack growth rate models. Various Bayesian models are established and presented, based on the same data as the existing models in BS 7910 (a widely used industrial standard). The models are compared in terms of their out of sample predictive accuracy, using methods with a basis in information theory and cross-validation. The Bayesian models exhibit an improved performance, with the most accurate predictions resulting from multi-level (hierarchical) models, which account for variation between constituent test datasets and partially pool information.