For advanced nuclear reactors, Probabilistic Risk/Safety Assessment (PRA/PSA) is one of the primary inputs to the risk-informed decision-making by the regulatory agency and the nuclear industry. The risk-informed analysis for advanced reactors is, however, challenging since (i) design-specific operating experience is quite limited or even unavailable and (ii) for the systems and phenomena that do not exist in the operating reactors, no consensus model that has been validated or peer reviewed and widely adopted by the community is available. To address these challenges, the authors’ group initiated a line of research to enhance PRA theories and methodologies for risk-informed analysis of advanced reactors. In their previous work, an Integrated Probabilistic Physics-of-Failure (I-PPoF) methodological framework was developed, where explicit models of physical degradation and maintenance performance were coupled using a renewal process model. I-PPoF helped address the first challenge (i.e., the lack of operating experience) by quantifying the PRA model based on modeling of the underlying phenomena associated with PRA inputs, instead of using a solely data-driven approach (as commonly done in PRA for operating reactors). This paper develops an algorithm to operationalize the I-PPoF methodological framework equipped with Probabilistic Validation (PV) to address the second challenge (i.e., validation) associated with risk-informed analysis of advanced reactors. This algorithm helps evaluate whether and how the structure and quantification of existing models, such as consensus models from conventional reactors and up-to-date models in academic literature, need to be updated for advanced reactors. The algorithm uses epistemic uncertainty as a measure of credibility, while sensitivity analyses are included to identify the most influential contributors to the output uncertainty, helping gradual and efficient improvements of realism and relevancy of the models that are needed for the I-PPoF methodological framework. Although the proposed algorithm is applicable for risk-informed analysis of diverse advanced reactors, this paper demonstrates its applicability to a case study of pipe failure rate estimation for advanced water-cooled reactor.