Abstract
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.
Highlights
It has been well recognized that model uncertainty plays a key role in reliability-based design of geotechnical structures [1,2,3,4,5], as it is usually much larger than uncertainty associated with design parameters
Calibration of equation (9) is carried out . e accuracy of the calibrated model is compared based on sample mean and sample coefficient of variation (COV) and root mean squared errors (RMSEs)
We investigate the influences of the calibration outcomes on practical designs of facing of soil nail walls. e facing design must ensure adequate margins of safety against various limit states, including facing flexural, punching shear, and headed-studs tensile failures
Summary
It has been well recognized that model uncertainty plays a key role in reliability-based design of geotechnical structures [1,2,3,4,5], as it is usually much larger than uncertainty associated with design parameters (e.g., soil cohesion, unit weight, and internal friction angle). Geotechnical models need to be assessed and calibrated against measured or observed data before used for design. In many cases, measurements or observations that are available for model assessment and calibration are limited, mainly due to two reasons: first, obtaining in situ geotechnical data is costly and time consuming in general; and second, monitored data were always undervalued and not well collected and pooled, in recent years, geotechnical engineers start to realize the value of data and make effort to make the best use of it [6,7,8,9,10,11,12,13]. Assessment and calibration of geotechnical models using limited data are far better than doing nothing at all [3]. One is the purely datacatering (PDC) approach, and the other is the root mean squared error (RMSE) approach
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