Current practice to model the occurrence of submarine landslides is based on methods that assess the potential of site-specific failures, all with the objective of providing elements to identify and quantify regional features associated to geohazards, before a project development takes place. Also, survey data to estimate parameters required to model submarine landslides show typically limited availability, mainly because of the cost associated to offshore surveying campaigns. In this paper, a probabilistic calibration approach is introduced using Bayesian statistical inference to maximize the use of available site investigation data, and to best estimate the occurrence of a marine landslide. For this purpose, a landslide model thought for its simplicity is used to illustrate the applicability and potential of the calibration methodology. The aim is to introduce a systematic approach to produce prior probability distributions of the model parameters, based on an actual integrated marine site investigation including geological, geophysical, and geomatics data, to then compare it with a posterior probability distribution of the same model parameters, but estimated after collecting in situ soil samples and testing them in the laboratory to produce the corresponding soil strength properties. This comparison allows to explore (a) the influence of the number of in situ samples, (b) the influence of a landslide factor of safety, and (c) the influence of the soil heterogeneity, into the likelihood of the occurrence of a marine landslide. The model parameters that are considered for calibration include the initial state of the submerged and saturated soil unit weight, the thickness of the soils' unit layers, the pseudo-static seismic coefficient, and the slope angle, while the soil undrained shear strength is considered as the reference parameter to conduct the calibration (i.e., to compare model predictions vs. actual observations). Results show the potential of the proposed methodology to produce landslide geohazard maps, which are needed for the planning and design of marine infrastructure.

Large area civil engineering projects, such as offshore wind farms, require extensive soil investigations for detailed soil characterisations. Site-wide geotechnical soil units are commonly defined for simplification due to budgetary constraints. Consequently, practitioners rely on a limited number of costly laboratory tests and a set of semi-empirical CPT correlations, predominantly established based on research sands, for deriving sand parameters. A recent publication by the authors highlights some valid concerns about currently often applied idealisation when deriving strength parameters of natural sands and presents some possible pathways to address the limitations with a grading curve parameter (d10+d30). In the current paper, the size of the original laboratory test database is increased to improve the robustness of the methods. In addition, the database is used to also explore the potential of the d10+d30-parameter to improve estimations of drained stiffness parameters. However, since the current database mainly consists of relatively fine sands with varying fines content, a previously published database of much coarser clean sands is applied to investigate the limitations of the presented methods. Finally, a new independent trial database is collected to demonstrate the performance of the new methods for estimating drained strength and stiffness parameters compared with commonly applied industry-acknowledged methods. Even though limitations of the presented methods are identified for coarser clean sands, significantly improved reliability is demonstrated when deriving drained strength and stiffness parameters of relatively fine and slightly silty to very silty siliceous offshore sands.

Important unsaturated soil mechanics topics for all geotechnical engineers and geotechnical engineering students are reviewed. These key topics include: (1) Soil is an elastoplastic material for which the macro-level response, in general, is controlled by two separate stress variables: total stress (net stress) and negative pore water pressure (suction). (2) Pore water pressures are always negative above the groundwater table-and should not be conservatively assumed zero; (3) shear strength and volume change of unsaturated soils are dependent on soil suction, as well as confining stress, and therefore geotechnical site investigations and testing must account for both stress variables; (4) water flow follows Darcy's law, but hydraulic conductivity is a strong function of water content such that fine-grained soil can have a higher conductivity than course-grained soil, leading to unexpected results when using saturated flow thinking processes; (5) unsaturated soil response is complex and difficult to intuit in the absence of laboratory testing and simulation. Features of unsaturated soil behavior most frequently encountered in geotechnical practice are highlighted, with discussion and demonstration from existing literature. Suggestions are given for relatively simple approaches for first steps in taking unsaturated soil mechanics principles into consideration in site investigation, laboratory testing, and design-related decisions.

In geotechnical engineering, an appreciation of local geological conditions from similar sites is beneficial and can support informed decision -making during site characterization. This practice is known as site recognition, which necessitates a rational quantification of site similarity. This paper proposes a data -driven method to quantify the similarity between two cross -sections based on the spatial variability of one soil property from a spectral perspective. Bayesian compressive sensing (BCS) is first used to obtain the discrete cosine transform (DCT) spectrum for a cross-section. Then DCT-based auto -correlation function (ACF) is calculated based on the obtained DCT spectrum using a set of newly derived ACF calculation equations. The cross-sectional similarity is subsequently reformulated as the cosine similarity of DCT-based ACFs between cross -sections. In contrast to the existing methods, the proposed method explicitly takes soil property spatial variability into account in an innovative way. The challenges of sparse investigation data, non -stationary and anisotropic spatial variability, and inconsistent spatial dimensions of different cross -sections are tackled effectively. Both numerical examples and real data examples from New Zealand are provided for illustration. Results show that the proposed method can rationally quantify cross-sectional similarity and associated statistical uncertainty from sparse investigation data. The proposed method advances data -driven site characterization, a core application area in data -centric geotechnics.

The offshore wind farm industry has recently experienced significant global growth. This study presents a thorough site investigation and analysis of the cyclic resistance of marine clay for offshore foundation design, using the Shaba wind farm in southern China as a case study. In-situ cone penetrometer (CPTu) tests and borehole sampling are conducted to explore the geotechnical characteristics of the soils. However, the soil conditions are characterized by multiple layers and complex sedimentary components. The classification and mechanical properties, such as water content and cyclic resistances, are compared through CPTu interpretation and laboratory tests. The findings indicated that a single physical indicator cannot determine cyclic resistance. In addition, the well-established method in existing literature proved unsuitable for marine clay. Consequently, multiple regression analysis shows that a linear relationship exist between cyclic resistance and depth-corrected CPTu index [EXP(qE/fs)0.3/H], hence a new evaluation method is developed to predict the cyclic resistance of marine clay based on CPTu data. This research aims to provide more reliable guidance for geotechnical investigations, supporting the rapid expansion of offshore wind farms.