Resonant Ultrasound Spectroscopy (RUS) has been widely used to measure the elastic modulus of different materials. However, the accuracy of RUS is affected by many uncertainty factors in measurement, material and models. Determining the reliability and confidence level of RUS measurement requires comprehensive uncertainty quantification and error propagation analysis. To be able to simultaneously and accurately model the resonant effect of multiple conditions that are both material and geometry-based, the propagation of uncertainty, due to model, material and measurement “errors,” must be understood. IAI proposes to develop an innovative uncertainty quantification approach for RUS (UQ-RUS) based on Bayes’ Theorem and efficient high dimensional random space propagation and sampling algorithms. The approach will address the problem of isolating and quantifying uncertainty sources to the material moduli estimation process and result in a more reliable and confident characterization of material status. IAI will first demonstrate the feasibility of UQ-RUS with several representative random factors. Next, the UQ-RUS architecture will be further developed with the focus on optimizing the model and algorithms, performing comprehensive analysis of random parameters and validation tests. The approach will be useful in improving numerical simulation models used to quantify the variation in resonant ultrasound spectroscopy frequencies of Ni-base superalloy material subject to macro/microscopic damage, and will be useful for non-destructive inspection.