A Bayesian statistical method for quantifying model form uncertainty and two model combination methods

Abstract

Apart from parametric uncertainty, model form uncertainty as well as prediction error may be involved in the analysis of engineering system. Model form uncertainty, inherently existing in selecting the best approximation from a model set cannot be ignored, especially when the predictions by competing models show significant differences. In this research, a methodology based on maximum likelihood estimation is presented to quantify model form uncertainty using the measured differences of experimental and model outcomes, and is compared with a fully Bayesian estimation to demonstrate its effectiveness. While a method called the adjustment factor approach is utilized to propagate model form uncertainty alone into the prediction of a system response, a method called model averaging is utilized to incorporate both model form uncertainty and prediction error into it. A numerical problem of concrete creep is used to demonstrate the processes for quantifying model form uncertainty and implementing the adjustment factor approach and model averaging. Finally, the presented methodology is applied to characterize the engineering benefits of a laser peening process.