The effectiveness of using vegetation to reinforce slopes is influenced by the soil and vegetation characteristics. Hence, this study pioneers the construction of an extensive soil database using random forest machine learning and ordinary kriging methods, focusing on the influence of plant roots on the saturated and unsaturated properties of residual soils. Soil organic content, which includes contributions from both soil organisms and roots, functions as a key factor in estimating soil hydraulic and mechanical properties influenced by vegetation roots. This innovative approach of using organic content to estimate soil properties performs well when applied to machine learning models for soil database development. The results reveal that organic content markedly affects the hydraulic properties of soils, more than their mechanical properties. The finding illustrates the importance of exploring the hydraulic effects of vegetation on slope stability in addition to the traditional emphasis on mechanical reinforcement. This rooted soil database has practical applications in GIS-based analyses for mapping regional slope stability, incorporating the role of plant roots. A case study demonstrated the database's utility, showcasing that vegetation effectively limited rainwater infiltration and improved slope stability. Therefore, this research offers a valuable approach to improving slope stability through informed vegetation strategies.

When constructing on clay and gyttja soils, low-carbon ground improvement methods such as preloading should be preferred over carbon-intensive solutions (e.g., piles or deep mixing with lime-cement binder). The design of preloading requires knowledge about the compressibility and consolidation properties of subsoil, but site-specific oedometer tests may be scarce or even lacking, especially in the early design phases. Hence, this paper presents two extensive databases based on oedometer tests performed on Finnish clay and gyttja soils, with a special emphasis on consolidation rate and creep properties. The FI-CLAY-oedo/14/282 database contains 282 oedometer test-specific data entries, such as initial hydraulic conductivity and maximum creep coefficient. The second database, FI-CLAY-cv/8/774, contains 774 load increment-specific data entries (e.g., coefficient of consolidation) from 232 oedometer tests. The analysis of these databases provided three main results: (i) statistics for bias factors, which quantify the differences between determination methods (log time vs. square root time method and oedometer vs. falling head test), (ii) transformation models (and their transformation uncertainty) to predict creep coefficient from index or consolidation properties, and (iii) typical value distributions for various consolidation rate and creep properties, in a form of histograms and fitted lognormal distributions. All the results are given with statistical information, which allows their straightforward utilization as input data for probabilistic assessment (reliability-based design). It is concluded that the consolidation properties of clay and gyttja soils are indeed characterized by significant uncertainty. Hence, such results are recommended to be used as existing (prior) knowledge when determining design parameters, either by supporting engineering judgement or via a more systematic framework such as Bayesian statistics.