Site characterization is indispensable in the design phase of geotechnical engineering projects. As a key factor in site characterization, the characterization of soil undrained shear strength (S_u) is always in the spotlight. Various methods, including laboratory and in-situ tests, have been developed to measure S_u. Nevertheless, these measurements are usually sparse at a specific site due to limited time and budget. To enhance S_u characterization, other relevant geotechnical investigation data (e.g., cone penetration test data), can be transformed into S_u through empirical correlations (referred to as transformation models) to provide more information on S_u. Considering this process introduces the transformation uncertainty and a developed transformation model may not be fully applicable to a local site, probabilistic transformation models (PTMs) have been developed to characterize soil parameters in a site-specific way and quantify the uncertainty to augment engineers’ judgement. However, few PTMs incorporate the spatial correlation of soil parameters, especially in the horizontal direction. This limitation hampers the ability to probabilistically characterize S_u in 2D/3D space, which is significant in practice. Moreover, estimating the horizontal spatial correlation from pure geotechnical data is challenging because they are typically sparse. In light of these circumstances, this thesis first proposes a PTM-based scheme to probabilistically characterize S_u in 2D. Then it is proposed to integrate geophysical data into the scheme. Compared to typical geotechnical investigations, geophysical surveys provide abundant 2D/3D measurement data, which are often correlated with geotechnical data. The fusion of these two data sources benefits characterizing geotechnical data including S_u. Particularly the horizontal spatial correlation of S_u 2D domain can be estimated from the abundant geophysical data. To be specific, a well-established PTM, MUSIC-X, by which measured S_u and other relevant soil parameters can be used to preliminarily characterize S_u, is first adopted. In this case, characterization specifically refers to simulating 1D vertical profiles of S_u. It is then combined with the intrinsic collocated co-kriging (ICCK) model, by which primary data (i.e., S_u) in 2D or theoretically 3D space can be estimated through linearly combining the preliminarily characterized S_u from MUSIC-X modelling and observed secondary data (i.e., geophysical data). The secondary parameter considered in this study is interval velocity (V_int). The scheme, to combine the MUSIC-X and ICCK model to estimate S_u in 2D space by the fusion of geotechnical and geophysical data, is applied to a real case study at Hollandse Kust (west) wind farm zone to demonstrate its effectiveness. The results indicate that such a scheme can robustly estimate a 2D cross section of S_u with quantified uncertainty. A comparative analysis is conducted between the proposed scheme and two alternatives, one lacking preliminary S_u characterization (i.e., without MUSIC-X modelling) and one lacking geophysical data, confirming that the proposed scheme has a relatively high accuracy in the estimated cross section. The research reveals it is sensible to combine MUSIC-X and ICCK for 2D S_u characterization and brings a new perspective that integrating geotechnical and geophysical data is promising to characterize soil parameters in higher dimensional space.

A random field generator based on Local Average Subdivision (LAS) method is proposed in this study in order to achieve probabilistic soil classification and quantify the uncertainty of the generated most probable geological cross section. CPT data and Robertson’s soil classification chart (1990) are adopted to classify the soil. The sole application of LAS makes the random field unconditional, which has been improved to conditional random field generator by using Kriging interpolation. Both unconditional and conditional generator are tested in an illustrative example and the results indicate that the improvement from unconditional to conditional random field reduces the uncertainty of the most probable result of classifications and the classifications in the unconditional random field will converge if there are enough realizations. Additionally, the conditional random field generator is further applied in a case with three conducted CPTs, which build up a domain with very large scale of fluctuations. It’s found that the uncertainty of the generated most probable result of classifications is pretty low so it’s speculated that the proposed generator can be best applied in a large scale of fluctuation scenario. Another finding in the case study is that the proposed random field generator can be used to verify the reliability of conducted CPTs.