ESR7 Barbara Switala

Research project description

Analysis of Slope Stabilization by Soil Bioengineering Methods

Fellow:                ESR7: Barbara Maria Switala

Supervisor:        Prof. Dr. Ing. Wei Wu

Institution:         Department of Civil Engineering and Natural Hazards, University of Natural Resources and Life Sciences, Vienna

Soil bioengineering methods of slope stabilization have been well known since ages and commonly used worldwide. Despite of this fact, research on this topic is relatively recent. Moreover, usually outdated or too simple uncoupled models are used in the numerical calculations.

Bioengineering is an important and multidisciplinary branch of engineering, especially nowadays, when pro-ecological and economically attractive solutions are more and more desirable.

The aim of this project is to create a three dimensional numerical model which couples both mechanical and hydrological factors, arising from the presence of plants on the slope and protecting the surface layer from the occurrence of shallow landslides.

Additionally the project will enrich the current knowledge about how roots reinforce the soil mass and how it influences the increase of shear strength. This has to be combined with hydrological effects caused by evapotranspiration: modified soil moisture regime, dissipation of excess pore pressure and established matric suction.

As an input to the model, the appropriate slope geometry and soil properties have to be chosen. It is also important to consider different types of plants and roots properties, as well as different levels of the ground water table. To assess the effect of evapotranspiration it is necessary to know the geographical localization of the slope and climatological data from the chosen region. This is strictly connected with work of other MuMoLaDe fellows, who investigate rainfall induced slope failures in both numerical and experimental way, which is essential also for this project. Moreover, it opens a door to future cooperation, especially with ESR 1, ESR 4 and ESR 13, in terms of choosing appropriate data and shearing ideas and obtained results.

 

Fig. 1 Overview of the numerical and analytical model