Neural network modeling applications in active slope stability problems |
| |
Authors: | Rennie B Kaunda Ronald B Chase Alan E Kehew Karlis Kaugars James P Selegean |
| |
Institution: | (1) Department of Geosciences, Western Michigan University, Kalamazoo, MI 49008, USA;(2) Department of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USA;(3) Great Lakes Hydraulics and Hydrology Office, U.S. Army Corps of Engineers, Detroit District, Detroit, MI 48226, USA |
| |
Abstract: | 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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|