In this thesis the performance of five CPT-based p-y models by Novello (1999), Dyson & Randolph (2001), Li, et al. (2014) and Suryasentana & Lehane (2014;2016) is assessed. This is done in part by comparing the measured load-deflection response from a set of lab- and field tests that is representative for offshore monopile structures to the predicted response generated by a MATLAB pile-response model that incorporates the aforementioned p-y models. It is found that with the exception of Suryasentana & Lehane (2014), alle models are more accurate at large- than at small displacements. Furthermore, the models are generally conservative at a groundline displacement of D/100 and unconservative at a groundline displacement of D/10. The latter is likely related to inability of the models to adequately capture the ultimate soil resistance. The strongest found correlation with prediction accuracy is a negative one with pile rigidity at small displacements. The models from Dyson & Randolph (2001) and Li, et al. (2014) generate predictions with the highest mean accuracy, though all models perform relatively poorly when compared to a benchmark from Burd, et al. (2019).
The aforementioned CPT-based p-y models are tested further through a number of new instrumented pile load tests in the TU Delft geotechnical centrifuge and the experimental methods for these tests are evaluated. Image analysis of CT scanned sand samples shows that though there is substantial non-uniformity present in samples prepared through hand raining, this method yields acceptable results and is preferable to preparation using a sand raining machine. Secondly, though soil disturbance does occur as a result of pile installation at 1g, this is not likely to greatly impact the results. A comparison of the load-deflection predictions generated with the p-y models (including API) for these load tests supports the notion that the initial response from the API (2014) model is far too stiff and that it is therefore unconservative in the prediction of the limit states. Furthermore, a modified 5th order polynomial fitting method is found to adequately fit discrete bending moment data and in turn p-y curves are produced. These show that the pu from the models Suryasentana & Lehane (2014;2016) is only reached at shallow depths and does not match the measured ultimate resistance. Finally, at all except the shallowest depths the CPT-based p-y models strongly overestimate p though the resulting prediction of the load-deflection response is more reasonable.
As mentioned previously, the CPT-based p-y models do not perform optimally for the studied experimental cases, especially at small groundline displacements. This can likely be attributed to the fact that they were generally developed from test data on long and flexible piles. Additionally, rigid piles under lateral load develop moments and shear forces at the base that are not taken into account by methods that rely solely on lateral soil resistances (Peralta 2010). To solve these problems the PISA model with additional soil reaction terms was developed and it has been proven to provide better displacement predictions than traditional methods relying on lateral soil resistance (Byrne, et al., 2015a). However, this model is more complex and may not be the most convenient method to use. A less complex alternative has been proposed which uses a simplified rotational pile-soil interaction model to capture the load-displacement behaviour of perfectly rigid piles of varying diameter, stress level and loading eccentricity. Numerical simulations by Wang, et al. (2022) have shown this to work and in this thesis these results are validated using experimental data. The unification of the pile responses is found to work, but is not as successful for the experimental data. This may be due to several factors, the most likely of which are variations is sand density and imperfect rigidity of the model pile. However, the results support use of methods based on the aforementioned simplified rotational pile-soil interaction model, such as the rotational spring model proposed by Wang, et al. (2022).