Calibration of models for pile settlement analysis using 64 field load tests

Abstract

Due to the presence of uncertainties, errors inevitably arise with the estimations of pile settlement. To properly consider serviceability requirements in limit state design, it is necessary to characterize the performance of commonly used settlement prediction models. In this work, information from 64 cases of long driven steel H-piles from field static loading tests in Hong Kong is utilized to evaluate the errors of three settlement prediction models for single piles: two elastic methods and a nonlinear load–transfer method. Commonly adopted soil parameters recommended in two Hong Kong design guidelines are used to reflect the uncertainty arising from evaluation of soil properties. The model error is represented by a bias factor. A conventional statistical analysis was first conducted to study the variability of model bias. A regression analysis method was then proposed as a supplemental analysis of model bias when only limited test data were available or when the measured settlement data distribute in a large range. Both methods result in very similar mean biases. The mean bias of each prediction model tends to vary with the load level and the bearing stratum at the pile toe; while the coefficient of variation of model bias only varies in narrow ranges.