Neural networks for slope movement prediction

International Journal of Geomechanics

Volumn 2, Issue 2, 2002, Pages 153-173

  Mayoraz, F ^a   Vulliet, L ^a  

^a EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION ALGORITHMS; DECISION MAKING; FORECASTING; LANDSLIDES; PORE PRESSURE; PRESSURE DISTRIBUTION; RECURRENT NEURAL NETWORKS; SAFETY FACTOR; WATER;

CONTINUOUS MEASUREMENTS; DAILY PRECIPITATIONS; DECISION MAKING TOOL; LANDSLIDE MONITORING; LEVENBERG-MARQUARDT ALGORITHM; MOVEMENT PREDICTION; PORE-WATER PRESSURES; RECURRENT NETWORKS;

MOTION ESTIMATION;

EID: 15744395274     PISSN: 15323641     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)1532-3641(2002)2:2(153)     Document Type: Article

References (41)

1. L. Vulliet and K. Hutter, A continuum model for natural slopes in slow movement, Géotechnique, 38(2), pp. 199-217, (1988).
2. L. Vulliet, and K. Hutter, Set of constitutive models for soils under slow movement, Journal of Geotechnical Engineering ASCE, 114(9), pp. 1022-1041, (1988).
3. L. Vulliet, and C.S. Desai, Viscoplasticity, and finite elements for landslide analysis, Proc. Int. Symposium on Landslides, Lausanne, pp. 801-806, (1988).
4. N. Samtani et al., Constitutive modeling, and analysis of creeping slopes, Journal of Geotechnical Engineering ASCE, 121(1), pp.43-56, (1995).
5. L. Vulliet, Predicting large displacements of landslides, Proc. Int. Symp. on Numerical Models in Geomechanics, Davos, pp. 527-532, (1995).
6. F. Oboni, Modélisation des pentes instables: Approche probabiliste, Annales de l'Institut technique du bâtiment et des travaux publics, 435, (1985).
7. A. Baki et al., Analyse probabiliste de la stabilité de deux remblais sur versant instable (site expérimental de Sallèdes), in Etudes et recherches des laboratoires des ponts et chaussées, GT 56, LCPC, (1993).
8. Baeza, and J. Corominas, Assessment of shallow landslide susceptibility by means of statistical techniques, Proc. Int. Symposium on Landslides, Trondheim, pp. 147-152, (1996).
9. Azimi et al., Prévision d'éboulement en terrain gypseux, Proc. Int. Conf. on Landslides, Lausanne, pp. 531-536, (1988).
10. P.J. Finlay et al., Use of discriminant functions in assessing the probability of slope failure, Proc. Int. Symp. on Landslides, Trondheim, pp. 209-214, (1996).
11. Y.M. Najjar et al., On the Identification of Compaction Characteristics by Neuronets, Computers, and Geotechnics, 17, pp. 167-187, (1996).
12. Goh, Seismic liquefaction potential assessed by neural networks, Journal of Geotechnical Engineering ASCE, 120(9), pp. 1467-1480, (1994).
13. G. Agrawal et al., Assessing the Liquefaction Susceptibility at a Site Based on Information from Penetration Testing, in Artificial Neural Networks for Civil Engineers. Fundamentals, and Applications, 10, (1995).
14. T.P. Williams, and N. Gucunski, Neural networks for backcalculation of moduli from SASW test, Journal of Computing in Civil Engineering, 9(1), pp. 1-8, (1995).
15. P. Aleotti, and R. Chowdhurry, Landslide hazard assessment: summary review, and new perspectives, Bulletin of Environmental, and Geological Engineering, 58, pp. 21-44, (1999).
16. M. Lee, and J.H. Lee, Prediction of pile bearing capacity using neural network, Computers, and Geotechnics, 18(3), pp. 189-199, (1996).
17. I. Teh et al., Prediction of pile capacity using neural networks, Journal of Computing in Civil Engineering, 11(2), pp. 129-138, (1996).
18. J. Ghaboussi et al., Neural network based modelling in geomechanics, Proc. Int. Conf. on Computer Methods, and Advances in Geomechanics, Morgantown, (1994).
19. J. Ghaboussi et al., Neural network constitutive models determined from structural tests, ASCE Journal of Engineering Mechanics, (1995).
20. G.W. Ellis et al., Stress-strain Modeling of Sands Using Artificial Neural Networks, Journal of Geotechnical Engineering ASCE, 121(5), pp. 429-435, (1995).
21. X. Wu et al., Use of Neural Networks in Detection of Structural Damage, Computers, and Structures, 42(4), pp. 649-659, (1992).
22. J. Ghaboussi, and A. Joghataie, Active Control of Structures Using Neural Networks, Journal of Engineering Mechanics, 121(4), pp. 555-567, (1995).
23. Z.P. Szewczyk, and P. Hajela, Damage detection in structures based on feature sensitive neural networks, Journal of Computing in Civil Engineering, 8(2), pp. 163-178, (1994).
24. R.P. Lippmann, An Introduction to Computing with Neural Nets, IEEE ASSP Magazine, pp. 4-22, (1987).
25. W.S. McCullogh, and W. Pitts, A logical calculus of the ideas immanent in nervous system, Bulletin of Mathematical Biophysics, 5, pp. 115-133, (1943).
26. M. Leshno et al., Multilayer FeedForward Networks with a Non Linear Activation Function Can Approximate any Function, Neural Network, 6, pp. 861-867, (1993).
27. S. Haykin, Neural Network: A Comprehensive Foundation, MacMillan, 1994.
28. T. Kohonen, Self-Organization, and Associative Memory, 3ème édition, Springer Series in Information Sciences, Springer-Verlag, 1989.
29. Y. LeCun, A Learning Scheme for Asymetric Threshold Networks, Proc. Cognitiva, 85, pp. 599-604, (1985).
30. F. Blayo, Réseaux Neuronaux, cours microprocesseurs II, DI, EPF Lausanne, 1992.
31. J. Hertz et al., Introduction to the Theory of Neural Computation, Addison-Wesley, 1991.
32. R. Battiti, First-, and second-order methods for learning: Between steepest descent, and Newton's method, Neural Computation, 4, pp. 141-166, (1992).
33. F. Shanno, Conjugate Gradient Methods with Inexact Searches, Mathematics of Operations Research, 3(3), (1978).
34. K. Levenberg, A Method for the Solution of certain Non-Linear Problems in Least Squares, Quarterly Journal of Applied Mathematics, 2, pp. 164-168, (1944).
35. W. Marquardt, An Algorithm for Least Squares Estimation of Non-Linear Parameters, SIAM Journal of Numeric Analysis, 11, pp. 431-441, (1963).
36. M.T. Hagan, and M. B. Menhaj, Training Feed-Forward Networks with the Marquardt Algorithm, IEEE Transactions on Neural Networks, 5(6), pp. 989-993, (1994).
37. P. Pouget, and M. Livet, Relations entre la pluviométrie, la piézométrie et les déplacements d'un versant instable (site expérimental de Sallèdes), in Etudes et recherches des laboratoires des ponts et chaussées, GT 57, LCPC, (1994).
38. MATLAB, User's, and Reference Guide + Neural networks Toolbox, MathWorks, 1992.
39. P. Grassberger, and I. Procaccia, Measuring the Strangeness of Strange Attractors, Physica D, 9, pp. 189-208, (1983).
40. H. Pi, and C. Peterson, Finding the Embedding Dimension, and Variable Dependencies in Time Series, Neural Computation, 6, pp. 509-520, (1994).
41. D. Djukic, Modélisation des systèmes non-linéaires: ordre, adaptation des paramétres et hystérèse, Ph.D. Dissertation, p. 158, DE, EPF Lausanne, (1997).