Neural network modeling applications in active slope stability problems

被引:0
|
作者
Rennie B. Kaunda
Ronald B. Chase
Alan E. Kehew
Karlis Kaugars
James P. Selegean
机构
[1] Western Michigan University,Department of Geosciences
[2] Western Michigan University,Department of Computer Science
[3] U.S. Army Corps of Engineers,Great Lakes Hydraulics and Hydrology Office
来源
关键词
Artificial neural network; Geotechnic; Slope stability; Earthflow; Lake Michigan;
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学科分类号
摘要
A back propagation artificial neural network approach is applied to three common challenges in engineering geology: (1) characterization of subsurface geometry/position of the slip (or failure surface) of active landslides, (2) assessment of slope displacements based on ground water elevation and climate, and (3) assessment of groundwater elevations based on climate data. Series of neural network models are trained, validated, and applied to a landslide study along Lake Michigan and cases from the literature. The subsurface characterization results are also compared to a limit equilibrium circular failure surface search with specific adopted boundary conditions. It is determined that the neural network models predict slip surfaces better than the limit equilibrium slip surface search using the most conservative criteria. Displacements and groundwater elevations are also predicted fairly well, in real time. The models’ ability to predict displacements and groundwater elevations provides a foundational framework for building future warning systems with additional inputs.
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页码:1545 / 1558
页数:13
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