Neural network modeling applications in active slope stability problems

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.

[1]  G. Habibagahi,et al.  A neural network framework for mechanical behavior of unsaturated soils , 2003 .

[2]  Jinggang. Cao.  Neural network and analytical modeling of slope stability. , 2002 .

[3]  Jamshid Ghaboussi,et al.  New nested adaptive neural networks (NANN) for constitutive modeling , 1998 .

[4]  R. Hecht-Nielsen,et al.  Neurocomputing: picking the human brain , 1988, IEEE Spectrum.

[5]  J. Nieuwenhuis,et al.  On the stability of seasonally sliding soil masses in the French Alps , 1990 .

[6]  Gordon A. Fenton,et al.  Probabilistic slope stability analysis by finite elements , 2004 .

[7]  Hangseok Choi,et al.  Drained Shear Strength Parameters for Analysis of Landslides , 2005 .

[8]  Toshitaka Kamai,et al.  Monitoring the process of ground failure in repeated landslides and associated stability assessments , 1998 .

[9]  A. Bishop.  The use of the Slip Circle in the Stability Analysis of Slopes , 1955 .

[10]  H. Wang,et al.  Slope stability evaluation using Back Propagation Neural Networks , 2005 .

[11]  J. M. Duncan.  State of the Art: Limit Equilibrium and Finite-Element Analysis of Slopes , 1996 .

[12]  D. V. Griffiths,et al.  SLOPE STABILITY ANALYSIS BY FINITE ELEMENTS , 1999 .

[13]  Alec Westley Skempton,et al.  The Mam Tor landslide, North Derbyshire , 1989, Philosophical transactions of the Royal Society of London. Series A: Mathematical and physical sciences.

[14]  R. Chase,et al.  Determining the kinematics of slope movements using low-cost monitoring and cross-section balancing , 2001 .

[15]  William W. Doe,et al.  Landscape erosion and evolution modeling , 2001 .

[16]  Akira Suemine.  Observational Study on Landslide Mechanism in the Area of Crystalline Schist (Part 1) -An Example of Propagation of Rankine State- , 1983 .

[17]  K. Neaupane,et al.  Some applications of a backpropagation neural network in geo-engineering , 2004 .

[18]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[19]  N. Janbu,et al.  SLOPE STABILITY COMPUTATIONS , 1973 .

[20]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[21]  J. C. Arkwright,et al.  Strains and displacements in the Mam Tor landslip, Derbyshire, England , 2003, Journal of the Geological Society.

[22]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[23]  J. Selegean,et al.  Modeling Debris Slide Geometry with Balanced Cross Sections: A Rigorous Field Test , 2007 .

[24]  R. Chowdhury.  Propagation of failure surfaces in natural slopes , 1978 .

[25]  Toru Higuchi,et al.  Development of progressive landslide failure in cohesive materials , 2005 .

[26]  Wai-Fah Chen.  Limit Analysis and Soil Plasticity , 1975 .

[27]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[28]  R.K. Jurgen,et al.  Sarnoff Labs: 'still crazy' but coping , 1988, IEEE Spectrum.

[29]  Yong Liu,et al.  Neural network modeling for regional hazard assessment of debris flow in Lake Qionghai Watershed, China , 2006 .

[30]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[31]  C. H. Juang,et al.  Three-dimensional site characterisation: neural network approach , 2001 .

[32]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[33]  Ferenc Szidarovszky,et al.  A neural network model for predicting aquifer water level elevations , 2005, Ground water.

[34]  Dov Leshchinsky,et al.  Spatial Distribution of Safety Factors , 2001 .

[35]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .