Geotechnical models are usually built upon assumptions and simplifications, inevitably resulting in discrepancies between model predictions and measurements. To enhance prediction accuracy, geotechnical models are typically calibrated against measurements by bringing in additional empirical or semiempirical correction terms. Different approaches have been used in the literature to determine the optimal values of empirical parameters in the correction terms. When measured data are abundant, calibration outcomes using different approaches can be expected to be practically the same. However, if measurements are scarce or limited, calibration outcomes could differ significantly, depending largely on the adopted calibration approach. In this study, we examine two most commonly used approaches for geotechnical model calibration in the literature, namely, (1) purely data-catering (PDC) approach, and (2) root mean squared error (RMSE) method. Here, the purely data-catering approach refers to selection of empirical parameter values that minimize coefficient of variation of model factor while maintains its mean value of one, based solely on measured data. A real case of calibrating the Federal Highway Administration (FHWA) simplified facing load model for design of soil nail walls is illustrated to thoroughly elaborate the differences in practical calibration and design outcomes using the two approaches under scarce data conditions.
Multi-step building energy model calibration process based on measured data
