DATA-CENTRIC STRUCTURAL INTEGRITY ASSESSMENT AND RISK-INFORMED ASSET MANAGEMENT USING OPERATIONAL DATA AND PROBABILISTIC UPDATING

The UK EASICS (Establishing Advanced Modular Reactor Structural Integrity Codes and Standards) programme has developed guidance to inform the development of codes and standards for application to the structural integrity assessment of future high-temperature designs [1]. A key aspect of the EASICS guidance describes the use of probabilistic methods in the context of a data-centric approach that can use data from all stages of the product lifecycle from design and manufacture through operation, maintenance and decommissioning to update the structural integrity assessment. This paper provides a summary of the EASICS data-centric approach before focusing on the operational phase of the product lifecycle and the use of data derived from plant monitoring and In-Service Inspection. The numerical analysis of a prototypical geometry with a postulated initial defect and set of simulated operational histories is used to demonstrate how such data can be used to manage the future inspection strategy on a risk-informed basis. A starting point of a required number of operational events, but unknown and arbitrary future operational sequence of events is considered. The structural reliability is calculated and updated through-life using the Monte Carlo random sampling method in conjunction with a selection of probabilistic updating techniques, including Bayesian inference.