Model Selection Issue in Calibrating Reliability-based Resistance Factors Based on In-situ Test Data
This paper addresses the model selection issue often encountered in the process of calibrating reliability-based resistance factors. As well known, a predictive model must be assumed for the purpose of calibrating resistance factors based on in-situ test data. A critical 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 are important questions to ask because the calibration results seriously 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 resistance factors. Two examples of real dataset are used to illustrate the model selection issue and to demonstrate the use of the proposed methods. The proposed methods lead to reasonable conclusions and may contribute code calibration based on in-situ test data.
J.Ching M.-T.Yen H.-D.Lin
National Taiwan University of Science and Technology
国际会议
上海
英文
2007-10-18（万方平台首次上网日期，不代表论文的发表时间）