Bossi, Giulia, Lisa Borgatti, Guido Gottardi, Gianluca Marcato,, 2018, The Boolean Stochastic Generation Method for Addressing the Effect of Marked Soil Heterogeneity in Natural and Anthropic Slopes, XIII IAEG Congress - Engineering Geology for a Sustainable World, San Francisco, California, September 15-23, 2018,
URL: http://www.cnr.it/prodotto/i/391996

Soil heterogeneity plays a crucial role in affecting the stability of natural and artificial slopes. Many authors estimated the uncertainty linked with soil heterogeneity using continuous functions that represent the range of variation of soil parameters within a uniform, single layer. However, in several geomorphological processes, the sediment deposition follows a bimodal pattern resulting in two different types of soil displaying markedly different rheological properties that can alternate in thin layers. The Boolean Stochastic Generation (BoSG) method addresses the uncertainty linked with the mechanical effect due to marked soil heterogeneity through the stochastic generation of numerous soil configurations. The method is called Boolean since the material could be either matrix, with specific properties, or randomly generated layers with another set of parameters. Analyzing the results of the cumulated pool of configurations, it is possible to address the range of variation of target quantities such as displacements or saturation in the entire numerical domain or in specific locations. This could be useful for reliability assessment but also for planning a secondary investigation campaign since it is possible to highlight the areas where uncertainty is higher and where new data would be useful. Moreover, it is possible to select from the pool the configuration appearing more congruent with monitoring data and use the BoSG technique for back analysis. The BoSG method has been applied to study natural and artificial slopes both with two-dimensional and tri-dimensional models, allowing to_ estimate the reliability of slope stability assessment, select automatically the best fit model for large landslides and target secondary investigation campaigns where uncertainty is higher, and evaluate the hazard of backward erosion in levees.

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