It is well recognised that the inherent soil variability and the effect of vegetation, in particular the effect of tree root reinforcement, have a significant effect on the stability of a natural slope. However, in practice, these factors are not commonly considered in routine slope stability analysis. This is due mainly to the fact that the effects of soil variability and vegetation are complex and difficult to quantify. Furthermore, the available slope stability analysis computer programs used in practice, which adopt conventional limit equilibrium methods, are unable to consider these factors. To predict the stability of a natural slope more accurately, especially the marginally stable one, the effects of soil variability and vegetation needs to be taken into account. The research presented in this thesis focuses on investigating and quantifying the effects of soil variability and vegetation on the stability of natural slopes. The random finite element method (RFEM), developed by Griffiths and Fenton (2004), is adopted to model the effect of soil variability on slope stability. The soil variability is quantified by the parameters called the coefficient of variation (COV) and scale of fluctuation (SOF), while the safety of a slope is assessed using probability of failure. In this research, extensive parametric studies are conducted, using the RFEM, to investigate the influence of COV and SOF on the probability of failure of a cohesive slope (i.e. undrained clay slope) with different geometries. Probabilistic stability charts are then developed using the results obtained from the parametric studies. These charts can be used for a preliminary assessment of the probability of failure of a spatially random cohesive slope. In addition, the effect of soil variability on c'–ϕ' slopes is also studied. The available RFEM computer program (i.e. rslope2d) is limited to analysing slopes with single-layered soil profile. Therefore, in this research, this computer program is modified to analyse slopes with two-layered soil profiles. The modified program is then used to investigate the effect of soil variability on two-layered spatially random cohesive slopes. It has been demonstrated that the spatial variability of soil variability has a significant effect on the reliability of both single and two-layered soil slopes. Artificial neural networks (ANNs), which are a powerful data-mapping tool for determining the relationship between a set of input and output variables, are used in an attempt to predict the probability of failure of a spatially random cohesive slope. The aim is to provide an alternative tool to the RFEM and the developed probabilistic stability charts because the RFEM analyses are computationally intensive and time consuming. The results obtained from the parametric studies of a spatially random cohesive slope are used as the database for the ANN model development. It has been demonstrated that the ANN models developed in this research are capable of predicting the probability of failure of a spatially random cohesive slope with high accuracy. The developed ANN models are then transformed into relatively simple formulae for direct application in practice. The effect of root reinforcement caused by vegetation is modelled as additional cohesion to the soils, known as root cohesion, cr. The areas affected by tree roots (i.e. root zone) are incorporated in the finite element slope stability model. The extent of the root zone is defined by the depth of root zone, hr. Parametric studies are conducted and the results are used to develop a set of stability charts that can be used to assess the contribution of root reinforcement on slope stability. Furthermore, ANN models and formulae are also developed based on the results obtained from the parametric studies. It has been demonstrated that the factor of safety of a slope increase linearly with the values cr and hr, and the contribution of root reinforcement to a marginally stable slope is significant. In addition, probabilistic slope stability analysis considering both the variability of the soils and root cohesion are conducted using the modified RFEM computer program. It has been demonstrated that the spatial variability of root cohesion has a significant effect on the probability of slope failure. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1349971 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009
| Identifer | oai:union.ndltd.org:ADTP/264747 |
| Date | January 2009 |
| Creators | Chok, Yun Hang |
| Source Sets | Australiasian Digital Theses Program |
| Detected Language | English |
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