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Memarian fard H. Conjugate gradient neural network in prediction of clay behavior and parameters sensitivities. NMCE 2016; 1 (2) :9-20
URL: http://nmce.kntu.ac.ir/article-1-23-en.html
MSc. Student in Department of Solid Mechanics, Moscow State University of Civil Engineering (MGSU), Moscow, Russia
Abstract:   (2632 Views)
The use of artificial neural networks has increased in many areas of engineering. In particular, this method has been applied to many geotechnical engineering problems and demonstrated some degree of success. A review of the literature reveals that it has been used successfully in modeling soil behavior, site characterization, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to stochastic approximation of the gradient. Here we explore a number of ways to adopt ideas from conjugate gradient and Back Propagation in the stochastic setting, using fast Hessian-vector products to obtain curvature information effectively. In our benchmark experiments the resulting highly scalable algorithms converge about an order of magnitude faster than ordinary stochastic gradient descent. The objective of this paper is to provide a general view to describe this method in predicting mechanical behavior and constitutive modeling issues in geo-mechanical behavior of cohesive soil to be used in geo-mechanics. In this research the Batching Back Propagation method (BBP) has been employed and the characterized parameters are introduced as initial void ratio, liquid limit, plasticity index, natural density, moisture percent, solid density of grain, over consolidation ratio, and pre-consolidation pressure. The paper also intends to present how much the input memory may cover the accuracy of predicted behavior of standard triaxial drained and undrained tests. The paper also discusses the strengths and limitations of the proposed method compared to the other modeling approaches. Also, the sensitivity of intended parameters is investigated.
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Type of Study: Research | Subject: Special
Received: 2014/05/9 | Revised: 2014/08/11 | Accepted: 2014/09/23 | ePublished ahead of print: 2014/10/2

1. [1] Jaksa, M. B. (1995). "The influence of spatial variability on the geotechncial design properties of a stiff, overconsolidated clay," PhD thesis, The University of Adelaide, Adelaide.
2. [2] Hubick, K. T. (1992). Artificial neural networks in Australia, Department of Industry, Technology and Commerce, Commonwealth of Australia, Canberra.
3. [3] Bhagat, P.M. (2005) Pattern Recognition in Industry, Elsevier. ISBN 0-08-044538-1
4. [4] Marquardt.D., (1963), An algorithm for least-squares estimation of non-linear parameters. Journal of the Society of Industrial and Applied Mathematics, 11(2):431-441. [DOI:10.1137/0111030]
5. [5] Schraudolph, N.N., (1999), Local gain adaptation in stochastic gradient descent. In Proceedings of the 9th International Conference on Artificial Neural Networks, pages 569-574, Edinburgh, Scotland, 1999. IEE, London. [DOI:10.1049/cp:19991170]
6. [6] Schraudolph, N.N., (2002), Fast curvature matrix-vector products for second-order gradient descent. Neural Computation, 14(7):1723-1738. [DOI:10.1162/08997660260028683]
7. [7] Santiago, R.A., G. Lendaris, (2005), "Reinforcement Learning and the Frame Problem," Proc. IJCNN.
8. [8] Funahashi. K. (1989), On the approximate realization of continuous mappings by neuralnetworks, Neural Networks 2, 183-192, 1989 . [DOI:10.1016/0893-6080(89)90003-8]
9. [9] Lendaris, G., J. Neidhoefer, (2004), "Guidance in the Use of Adaptive Critics for Control," Ch. 4 in Handbook of Learning and Approximate Dynamic Programming, J. Si, A.G. Barto, W.B. Powell, D. Wunsch, Eds., 97-124.
10. [10] Fausett, L. V. (1994). Fundamentals neural networks: Architecture, algorithms, and applications, Prentice-Hall, Inc., Englewood Cliffs, New Jersey.
11. [11] Orr, G. B., (1995), Dynamics and Algorithms for Stochastic Learning. PhD thesis, Department of Computer Science and Engineering, Oregon Graduate Institute, Beaverton, OR 97006, 1995. ftp://neural.cse.ogi.edu/pub/neural/papers/orrPhDch1-5. ps.Z, orrPhDch6-9.ps.Z.
12. [12] Graepel, T. and Schraudolph, N. N.(2002), Stable adaptive momentum for rapid online learning in nonlinear systems. In Dorronsoro [11]. http://www.inf.ethz.ch/˜schraudo/pubs/sam.ps.gz. [DOI:10.1007/3-540-46084-5_73]
13. [13] Shannon, T.T., R.A. Santiago, G. Lendaris, (2003), Accelerated Critic Learning in Approximate Dynamic Programming via Value Templates and Perceptual Learning, Proc. IJCNN.
14. [14] Moselhi, O., Hegazy, T., and Fazio, P. (1992). "Potential applications of neural networks in construction." Can. J. Civil Engrg, 19, 521-529. [DOI:10.1139/l92-061]
15. [15] Flood, I., and Kartam, N. (1994). "Neural networks in civil engineering I: Principles and understanding." J. Computing in Civil Engrg, ASCE, 8(2), 131-148. [DOI:10.1061/(ASCE)0887-3801(1994)8:2(131)]
16. [16] Maier, H. R., and Dandy, G. C. (2000). "Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications." Environmental Modelling & Software, 15(2000), 101-124. [DOI:10.1016/S1364-8152(99)00007-9]
17. [17] Gardner, M. W., and Dorling, S. R. (1998). "Artificial neural networks (The multilayer perceptron) - A review of applications in the atmospheric sciences." Atmospheric Environment, 32(14/15), 2627-2636. [DOI:10.1016/S1352-2310(97)00447-0]
18. [18] Goh, A. T. C. (1994a). "Nonlinear modelling in geotechnical engineering using neural networks." Australian Civil Engineering Transactions, CE36(4), 293-297.
19. [19] Goh, A. T. C. (1995a). "Back-propagation neural networks for modeling complex systems." Artificial Intelligence in Engineering, 9, 143-151. [DOI:10.1016/0954-1810(94)00011-S]
20. [20] Ellis, G. W., Yao, C., Zhao, R., and Penumadu, D. (1995). "Stress-strain modelling of sands using artificial neural networks." J. Geotech. Engrg., ASCE, 121(5), 429-435. [DOI:10.1061/(ASCE)0733-9410(1995)121:5(429)]
21. [21] Sidarta, D. E., and Ghaboussi, J. (1998). "Constitutive modeling of geomaterials from non-uniform material tests." J. Computers & Geomechanics, 22(10), 53-71. [DOI:10.1016/S0266-352X(97)00035-9]
22. [22] Al-Rabadi, A.N., G. Lendaris, (3003), Artificial Neural Network Implementation Using Many-Valued Quantum Computing, Proceedings of IJCNN.
23. [23] Ghaboussi, J., and Sidarta, D. E. (1998). "New nested adaptive neural networks (NANN) for constitutive modeling." J. Computers and Geotechnics, 22(1), 29-52. [DOI:10.1016/S0266-352X(97)00034-7]
24. [24] Penumadu, D., and Zhao, R. (1999). "Triaxial compression behavior of sand and gravel using artificial neural networks (ANN)." J. Computers and Geotechnics, 24, 207-230. [DOI:10.1016/S0266-352X(99)00002-6]
25. [25] Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998a). "Modeling of soil behavior with a recurrent neural network." Canadian Geotech. J., 35(5), 858-872. [DOI:10.1139/t98-042]
26. [26] Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998b). "Modelling of shearing behavior of a residual soil with recurrent neural network." Int. J. Numerical and Analytical Methods in Geomechanics, 22(8), 671-687. https://doi.org/10.1002/(SICI)1096-9853(199808)22:8<671::AID-NAG939>3.0.CO;2-Y [DOI:10.1002/(SICI)1096-9853(199808)22:83.0.CO;2-Y]
27. [27] Greenwood, G. W., (2005), On the practicality of using intrinsic reconfiguration for fault recovery, IEEE Transactions on Evolutionary Computation 9(4), 398-405. [DOI:10.1109/TEVC.2005.850278]
28. [28] Najjar, Y. M., and Basheer, I. A. (1996a). "Neural network approach for site characterization and uncertainty prediction." Geotechnical Special Publication, ASCE, 58(1), 134-148.
29. [29] Greenwood, G. W., (2005), ''On the usefulness of accessibility graphs with combinatorial optimization problems'', Journal of Interdisciplinary Mathematics 8(2), 277-286. [DOI:10.1080/09720502.2005.10700408]
30. [30] Agrawal, G., Weeraratne, S., and Khilnani, K. (1994). "Estimating clay liner and cover permeability using computational neural networks." Proc., First Congress on Computing in Civil Engrg., Washington, June 20-22.
31. [31] Najjar, Y. M., Ali, H. E., and Basheer, I. A. (1999). "On the use of neurons for simulating the stress-strain behavior of soils." Proc., 7th Int. Symposium on Numerical Models in Geomechanics, G. N. Pande, ed., Graz, Austria, NUMOG VII, September 1-3, 657-662.
32. [32] Najjar, Y. M., and Basheer, I. A. (1996b). "Utilizing computational neural networks for evaluating the permeability of compacted clay liners." Geotechnical and Geological Engineering, 14, 193-221.
33. [33] Basheer, I. A., and Najjar, Y. M. (1995). "A neural-network for soil compaction." Proc., 5th Int. Symp. Numerical Models in Geomechanics, G. N. Pande and S. Pietruszczak, eds., Roterdam: Balkema, 435-440.
34. [34] Ellis, G. W., Yao, C., and Zhao, R. (1992). "Neural network modeling of the mechanical behavior of sand." Proc., Engineering Mechanics, ASCE, 421-424.
35. [35] Penumadu, D., Jin-Nan, L., Chameau, J.-L., and Arumugam, S. (1994). "Rate dependent behavior of clays using neural networks." Proc., 13th Conf. Int. soc. Soil Mech. & Found. Engrg., New Delhi, 4, 1445-1448.
36. [36] Basheer, I. A. (1998). "Neuromechanistic-based modeling and simulation of constitutive behavior of fine-grained soils." Ph.D. dissertation, KansasStateUniversity, Manhattan, KS.
37. [37] Najjar, Y. M., and Ali, H. E. (1999). "Simulating the stress-strain behavior of Nevada sand by ANN." Proc., 5th U.S.National Congress on Computational Mechanics (USACM), Boulder, Colorado, August 4-6.
38. [38] Zhu, J. H., and Zamman, M. M. (1997). "Neural network modeling for a cohesionless soil." 76th Meeting of the Transportation Research Board, January, Washington, D.C.,
39. [39] Tutumluer, E., and Seyhan, U. (1998). "Neural network modeling of anisotropic aggregate behavior from repeated load triaxial tests. " Transportation Research Record 1615, National Research Council, Washington, D.C. [DOI:10.3141/1615-12]

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