Minding the geotechnical data gap: appraisal of the variability of key soil parameters for slope stability modelling in Saint Lucia

Abstract

Identification of failure thresholds and critical uncertainties associated with slope stability often requires the specification of geotechnical parameter values for input into a physically-based model. The variation of these parameters (including mechanical soil properties such as effective friction angle and cohesion) can have a significant impact on the computed factor of safety. These uncertainties arise from natural variations in soils, measurement techniques, and lack of reliable information. Researchers may use statistical analysis coupled with numerical simulation to determine possible ranges of slope factors of safety and the relative influence of geotechnical and other parameters, such as topsoil depth and rainfall. This study investigates the variation of geotechnical parameters observed on the island of Saint Lucia in the Eastern Caribbean. A database of particle size distributions, in-situ moisture contents, Atterberg and direct shear box test results is compiled from 91 samples of tropical soils in Saint Lucia. A study of various probability distributions shows that the Weibull distribution may be favoured for the effective friction angle of the Saint Lucian soils considered based on the Akaike information criterion, employed as an estimator of the relative quality of statistical models dealing with the trade-off between goodness-of-fit and simplicity of the model.

The SAFER geodatabase for the Kathmandu Valley: Geotechnical and geological variability

“…Compilation of geodatabases can help reduce geotechnical uncertainty for design and assessment (cf. Ching and Phoon, 2014;Kulhawy and Mayne, 1990;Roopnarine et al, 2012;Shepheard et al, 2019). A key study Piya (2004) compiled a database of 185 well logs and 328 shallow boreholes to produce a single knowledge base of geological information for the Kathmandu Valley.…”

Determining the Geotechnical Slope Failure Factors via Ensemble and Individual Machine Learning Techniques: A Case Study in Mandi, India

“…Physical models may involve different geotechnical factors, which may help these models generate forecasts about slope failures (Tofani et al, 2017;Bicocchi et al, 2019). However, physical models involving different geotechnical factors may be constraint by the underlying mechanics explaining slope failures (Yalcin 2011;Igwe, 2015;Tofani et al, 2017;Bicocchi et al, 2019;Shepheard et al, 2019). Amidst constraints in physical models, data-driven machine-learning (ML) techniques may provide an alternate approach for predicting slope failures (Aloetti and Chowdhury 1999; Kavzoglu et al, 2014;Huang et al, 2017;Park et al, 2019;Asheghi et al, 2020;Moayedi et al, 2021).…”

“…Statistical methods Porosity, dry density, angle of internal friction, and saturated permeability Shepheard et al (2019)…”

“…However, the individual and ensemble feature selection techniques were not explored. Shepheard et al (2019) performed a statistical approach for determining the factors influencing the slope failures in Saint…”

A database of saturated hydraulic conductivity of fine-grained soils: probability density functions

“…Recently Shepheard et al (2019) have shown in that Weibull distribution may better describe peak effective friction angle (φ'peak) (number of datapoints (n) = 85) and cohesion intercept (c') (n = 86) for a database of soils from the island of Saint Lucia.…”