Model selection issue in calibrating reliability-based resistance factors based on geotechnical in-situ test data

This paper addresses the model selection issue often encountered in the process of calibrating reliability-based geotechnical resistance factors. A predictive model must be assumed for the purpose of calibrating resistance factors based on geotechnical in-situ test data. A question is raised by this research: which predictive model should we choose? What type of probability distribution model should we pick to model the model uncertainties? Those may be important questions to ask because the calibration results depend on the assumed predictive and probabilistic models. A full probabilistic framework is proposed in this research to resolve the model selection issue as well as to calibrate the geotechnical resistance factors. Two examples of geotechnical real dataset are used to illustrate the model selection issue and to demonstrate the use of the proposed methods. The proposed methods may contribute to code calibration based on geotechnical in-situ test data.