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.