In uncoupled consolidation analysis, settlement and pore water pressure are solved independently, whereas in coupled analysis, they are solved simultaneously to ensure continuity (i.e., the volume change in soil due to compression must equal the water volume change caused by dissipation). This study investigates the coupling effects of soil deformation and pore water pressure dissipation in the back analysis of soft soil settlements. It further evaluates the suitability of both coupled and uncoupled constitutive models with different types of monitoring data, providing practical guidance for selecting consolidation models and achieving reliable long-term predictions. The one-dimensional governing equations for soft soil consolidation, incorporating prefabricated vertical drains and creep deformation, are first reviewed. A case study of a trial embankment in Ballina, New South Wales, Australia, is then used to demonstrate the impact of coupling effects and monitoring data on settlement predictions. The results show that considering coupling effects not only improves long-term settlement predictions but also reduces uncertainties in the updated soil parameters, especially when both settlement and pore water pressure data are used.

The large-strain geometric assumptions and nonlinear compressibility and permeability have significant effects on the consolidation of soft soils with high compressibility. However, analytical solutions for large-strain nonlinear consolidation of soft soils with partially penetrating PVDs have been rarely reported in the literature. A double logarithmic model is adopted to describe the nonlinear compressibility and permeability of soft soils with high compressibility, and a large-strain consolidation model for soft soils with partially penetrating PVDs under the condition that the excess pore water pressure at the interface between the improved and unimproved layers is equal is established based on Gibson's large-strain consolidation theory. The analytical solution for the large-strain nonlinear consolidation model for soft soils with partially penetrating PVDs is obtained. The reliability of the analytical solution obtained in this study is verified by comparing it with the existing solutions under different conditions, and the maximum deviation between the two methods does not exceed 5 %. On this basis, consolidation behaviors of soft soils with partially penetrating PVDs under different conditions were analyzed by extensive calculations. Finally, the proposed analytical solution for the large strain consolidation model is applied to the settlement calculation of the Bachiem Highway Project, which further demonstrates the applicability of the consolidation model.

The prediction of time-dependent deformations of embankments constructed on soft soils is essential for preloading or surcharge design. The predictions can be obtained by Bayesian back analysis methods progressively based on measurements so that practical decisions can be made after each monitoring round. However, the effect of creep is typically ignored in previous settlement predictions based on Bayesian back analysis to avoid the heavy computational costs. This study aims to fill this gap by combining the Bayesian back analysis with a decoupled consolidation constitutive model, which accounts for creep to perform long-term settlement predictions of the trial embankment with prefabricated vertical drains (PVDs) constructed in Ballina, Australia. The effect of creep on settlement predictions is illustrated by the comparisons of the cases with and without considering creep. The results show that good settlement predictions could be obtained if creep is ignored and could be further improved if creep is incorporated when the monitoring settlement data is applied in the Bayesian back analysis. Ignoring creep could lead to an underestimation of the ultimate consolidation settlement. The swelling index kappa and the compression index lambda need to be adjusted to larger values to match the measurements if creep is ignored. Four updating schemes (using surface settlement data only, using settlement data at all monitoring depths, using pore water pressure data only, and using both settlement and pore water pressure data) are applied to study the effects of monitoring data on the accuracy of settlement prediction. The results show that the variability introduced by the noisy pore water pressure data result in fluctuating settlement predictions. Incorporating both settlement and pore water pressure observations into the Bayesian updating process reduces the variability in the updated soil parameters.