Cost Risk Analysis Framework (CRAFT): An Integrated Risk Analysis Tool and its Application in an Industry Use Case

Abstract

This report presents the initial application design of the Cost Risk Analysis Framework Tool (CRAFT). At the beginning, the concept of CRAFT was formulated from a need to perform economic risk analysis that can be quickly deployable through the nuclear industry to evaluate the risk associated to capital projects and to perform plant asset management. The design evolved into a framework that can integrate several forms of risk (e.g., not just economic, but also safety related) into a single analysis. In order to reduce the research, development and deployment time, we have decided to use the Risk Analysis Virtual ENvironment (RAVEN) statistical framework as a basis for the CRAFT architecture. In this report we summarize the most recent developments accomplished during the second part of FY18 for the two use cases of the Cost and Risk Categorization Applications path of the RISA project. These two use cases directly target plant health and plant capital SSC management. Regarding the plant health management use case, we have started to investigate methods to extract information from text-based data: text data mining. The Risk Informed Asset management is the main target of this report and several methods are here shown in order to solve the capital SSC replacement issue. This has been performed by presenting: several models designed to determine the effective cost through the lifetime of a capital SSCs, and the optimization algorithms that will be employed to determine the optimal replacement schedule of a given set of capital SSC. The interface between RAVEN and the PRA code SAPHIRE is the first step toward the integration of several plant risk models (e.g., economic and safety) and plant data (e.g., databases from the plant maintenance and diagnostics center) into a single analysis framework. In this respect, RAVEN is the ideal platform to connect several models and to manage data streaming among them.

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

“…(Moura et al 2017) applied unsupervised ML (clustering) to validate risk studies using information from past major accidents. (Mandelli et al 2018) developed a data-driven method for cost risk analysis via supervised ML (classification).…”