State of the art of artificial neural networks in xengineering

Electronic Journal of Geotechnical Engineering

Volumn 13, Issue I, 2008, Pages 1-25

  Shahin, Mohamed A ^a   Jaksa, Mark B ^b   Maier, Holger R ^b  

^a CURTIN UNIVERSITY   (Australia)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Artificial intelligence; Artificial neural networks; Geotechnical engineering

Indexed keywords

ARTIFICIAL INTELLIGENCE; GEOTECHNICAL ENGINEERING;

KNOWLEDGE EXTRACTION; STATE OF THE ART;

NEURAL NETWORKS;

EID: 84908614016     PISSN: None     EISSN: 10893032     Source Type: Journal    
DOI: None     Document Type: Article

References (190)

1. Abu-Kiefa, M. A. (1998). "General regression neural networks for driven piles in cohesionless soils." Journal of Geotechnical & Geoenviromental Engineering, ASCE, 124(12), 1177-1185.
2. Adeli, H. (2001). "Neural networks in civil engineering: 1989-2000." Computer-Aided Civil and Infrastructure Engineering, 16(2), 126-142.
3. Agrawal, G., Chameau, J. A., and Bourdeau, P. L. (1997). "Assessing the liquefaction susceptibility at a site based on information from penetration testing." Artificial neural networks for civil engineers: Fundamentals and applications, N. Kartam, I. Flood, and J. H. Garrett, eds., New York, 185-214.
4. Agrawal, G., Weeraratne, S., and Khilnani, K. (1994). "Estimating clay liner and cover permeability using computational neural networks." Proceedings of the 1st Congress on Computing in Civil Engineering, Washington.
5. Ahmad, I., El Naggar, H., and Kahn, A. N. (2007). "Artificial neural network application to estimate kinematic soil pile interaction response parameters." Soil Dynamics and Earthquake Engineering, 27(9), 892-905.
6. Ali, H. E., and Najjar, Y. M. (1998). "Neuronet-based approach for assessing liquefaction potential of soils." Transportation Research Record No. 1633, 3-8.
7. Amari, S. I., Murata, N., Muller, K. R., Finke, M., and Yang, H. H. (1997). "Asympotic statistical theory of overtraining and cross-validation." IEEE Transactions on Neural Networks, 8(5), 985-996.
8. ASCE. (2000). "Artificial neural networks in hydrology. I: Preliminary concepts." J. Hydrologic Engineering, 5(2), 115-123.
9. Basheer, I. A. (1998). "Neuromechanistic-based modeling and simulation of constitutive behaviour of fine-grained soils," PhD Thesis, Kansas State University, Manhattan, KS.
10. Basheer, I. A. (2000). "Selection of methodology for neural network modeling of constitutive hysteresis behavior of soils." Computer-Aided Civil and Infrastructure Engineering, 15(6), 445-463.
11. Basheer, I. A. (2002). "Stress-strain behavior of geomaterials in loading reversal simulated by time-delay neural networks." Journal of Materials in Civil Engineering, 14(3), 270-273.
12. Basheer, I. A., and Najjar, Y. M. (1998). "Modeling cyclic constitutive behavior by neural networks: Theoretical and real data." Proceedings of the 12th Engineering Mechanics Conference, La Jolla, California, 952-955.
13. Basheer, I. A., Reddi, L. N., and Najjar, Y. M. (1996). "Site characterization by neuronets: An application to the landfill sitting problem." Ground Water, 34, 610-617.
14. Baziar, M. H., and Ghorbani, A. (2005). "Evaluation of lateral spreading using artificial neural networks." Soil Dynamics and Earthquake Engineering, 25(1), 1-9.
15. Bebis, G., and Georgiopoulos, M. (1994). "Feed-forward neural networks: Why network size is so important." IEEE Potentials, October/November, 27-31.
16. Benardos, A. G., and Kaliampakos, D. C. (2004). "Modeling TBM performance with artificial neural networks." Tunneling and Underground Space Technology, 19(6), 597-605.
17. Berke, L., and Hajela, P. (1991). "Application of neural networks in structural optimization." NATO/AGARD Advanced Study Institute, 23(I-II), 731-745.
18. Bowden, G. J., Dandy, G. C., and Maier, H. R. (2005). "Input determination for neural network models in water resources applications: Part 1-Background and methodology." Journal of Hydrology, 301(1-4), 75-92.
19. Bowden, G. J., Maier, H. R., and Dandy, G. C. (2002). "Optimal division of data for neural network models in water resources applications." Water Resources Research, 38(2), 2.1-2.11.
20. Braddock, R. D., Kremmer, M. L., and Sanzogni, L. (1998). "Feed-forward artificial neural network model for forecasting rainfall run-off." Environmetrics, 9, 419-432.
21. Breiman, L. (1994). "Comment on 'Neural networks: A review from a statistical by B. Cheng and D. M. Titterington." Statistical Science, 9(1), 38-42.
22. Brown, M., and Harris, C. J. (1994). Neurofuzzy adaptive modeling and control, Prentice-Hall, Englewood Cliffs, New Jersey.
23. Buntine, W. L., and Weigend, A. S. (1991). "Bayesian back-propagation." Complex Systems, 5, 603-643.
24. Burke, L. I., and Ignizio, J. P. (1992). "Neural networks and operations research: An overview." Computer and Operations Research, 19(3/4), 179-189.
25. Cai, S., Toral, H., Qiu, J., and Archer, J. S. (1994). "Neural network based objective flow regime identification in air-water two phase flow." Canadian Journal of Chemical Engineering, 72, 440-445.
26. Cal, Y. (1995). "Soil classification by neural-network." Advances in Engineering Software, 22(2), 95-97.
27. Campolo, M., Soldati, A., and Andreussi, P. (1999). "Forecasting river flow rate during low-flow periods using neural networks." Water Resources Research, 35(11), 3547-3552.
28. Castellano, G., Fanelli, A. M., and Pelillo, M. (1997). "An iterative pruning algorithm for feed-forward neural networks." IEEE Transactions on Neural Networks, 8(3), 519-531.
29. Caudill, M. (1988). "Neural networks primer, Part III." AI Expert, 3(6), 53-59.
30. Celik, S., and Tan, O. (2005). "Determination of pre-consolidation pressure with artificial neural network." Civil Engineering and Environmental Systems, 22(4), 217-231.
31. Chan, W. T., Chow, Y. K., and Liu, L. F. (1995). "Neural network: An alternative to pile driving formulas." Computers and Geotechnics, 17, 135-156.
32. Chen, Y., Azzam, R., and Zhang, F. (2006). "The displacement computation and construction pre-control of a foundation pit in Shanghai utilizing FEM and intelligent methods." Geotechnical and Geological Engineering, 24(6), 1781-1801.
33. Chester, D. L. (1990). "Why two hidden layers are better than one." International Joint Conference on Neural Networks, 1, 265-268.
34. Cybenko, G. (1989). "Approximation by superpositions of a sigmoidal function." Mathematics of Control, Signals, and Systems, 3, 303-314.
35. Das, S. K., and Basudhar, P. K. (2006). "Undrained lateral load capacity of piles in clay using artificial neural network." Computers and Geotechnics, 33(8), 454-459.
36. Ellis, G. W., Yao, C., and Zhao, R. (1992). "Neural network modeling of the mechanical behavior of sand." Proceedings of the Engineering Mechanics, 421-424.
37. Ellis, G. W., Yao, C., Zhao, R., and Penumadu, D. (1995). "Stress-strain modeling of sands using artificial neural networks." Journal of Geotechnical Engineering, ASCE, 121(5), 429-435.
38. Erzin, Y. (2007). "Artificial neural networks approach for swell pressure versus soil suction behavior." Canadian Geotechnical Journal, 44(10), 1215-1223.
39. Fahlman, S. E. (1988). "Fast learning variations on back-propagation: An empirical study." Proceedings of the 1988 Connectionist Models Summer School, Pittsburgh, 38-51.
40. Fahlman, S. E., and Lebiere, C. (1990). "The cascade-correlation learning architecture." Advances in Neural Information Processing Systems 2, D. S. Touretzky, ed., Morgan Kaufmann, San Mateo, California, 524-532.
41. Faraway, J., and Chatfield, C. (1998). "Time series forecasting with neural networks: A comparative study using the airline data." Applied Statistics, 47(2), 231-250.
42. Fausett, L. V. (1994). Fundamentals neural networks: Architecture, algorithms, and applications, Prentice-Hall, Englewood Cliffs, New Jersey.
43. Feng, X. T., Chen, B., Yang, C., Zhou, H., and Ding, X. (2006). "Identification of viscoelastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm." International Journal of Rock Mechanics and Mining Sciences, 43(5), 789-801.
44. Ferentinou, M. D., and Sakellariou, M. G. (2007). "Computational intelligence tools for the prediction of slope performance." Computers and Geotechnics, 34(5), 362-384.
45. Flood, I. (1991). "A Gaussian-based neural network architecture and complementary training algorithm." Proceedings of the International Joint Conference on Neural Networks, New York, 171-176.
46. Flood, I., and Kartam, N. (1994). "Neural networks in civil engineering I: Principles and understanding." Journal of Computing in Civil Engineering, 8(2), 131-148.
47. Fortin, V., Ouarda, T. B. M. J., and Bobee, B. (1997). "Comment on 'The use of artificial neural networks for the prediction of water quality parameters' by H. R. Maier and G. C. Dandy." Water Resources Research, 33(10), 2423-22424.
48. Fu, Q., Hashash, Y. M. A., Hung, S., and Ghaboussi, J. (2007). "Integration of laboratory testing and constitutive modeling of soils." Computers and Geotechnics, 34(5), 330-345.
49. 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.
50. Garson, G. D. (1991). "Interpreting neural-network connection weights." AI Expert, 6(7), 47-51.
51. Ghaboussi, J., and Sidarta, D. E. (1998). "New nested adaptive neural networks (NANN) for constitutive modeling." Computers and Geotechnics, 22(1), 29-52.
52. Giraudel, J. L., and Lek, S. (2001). "A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination." Ecological Modeling, 146, 329-339.
53. Goh, A. T. (2002). "Probabilistic neural network for evaluating seismic liquefaction potential." Canadian Geotechnical Journal, 39(1), 219-232.
54. Goh, A. T., and Goh, S. H. (2007). "Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data." Computers and Geotechnics, 34(5), 410-421.
55. Goh, A. T., Kulhawy, F. H., and Chua, C. G. (2005). "Bayesian neural network analysis of undrained side resistance of drilled shafts." Journal of Geotechnical and Geoenvironmental Engineering, 131(1), 84-93.
56. Goh, A. T. C. (1994a). "Nonlinear modeling in geotechnical engineering using neural networks." Australian Civil Engineering Transactions, CE36(4), 293-297.
57. Goh, A. T. C. (1994b). "Seismic liquefaction potential assessed by neural network." Journal of Geotechnical & Geoenvironmental Engineering, ASCE, 120(9), 1467-1480.
58. Goh, A. T. C. (1995a). "Empirical design in geotechnics using neural networks." Geotechnique, 45(4), 709-714.
59. Goh, A. T. C. (1995b). "Modeling soil correlations using neural networks." Journal of Computing in Civil Engineering, ASCE, 9(4), 275-278.
60. Goh, A. T. C. (1996a). "Neural-network modeling of CPT seismic liquefaction data." Journal of Geotechnical Engineering, ASCE, 122(1), 70-73.
61. Goh, A. T. C. (1996b). "Pile driving records reanalyzed using neural networks." Journal of Geotechnical Engineering, ASCE, 122(6), 492-495.
62. Goh, A. T. C., and Kulhawy, F. H. (2003). "Neural network approach to model the limit state surface for reliability analysis." Canadian Geotechnical Journal, 40(1235-1244), 6.
63. Goh, A. T. C., Wong, K. S., and Broms, B. B. (1995). "Estimation of lateral wall movements in braced excavation using neural networks." Canadian Geotechnical Journal, 32, 1059-1064.
64. Gokceoglu, C., Yesilnacar, E., Sonmez, H., and Kayabasi, A. (2004). "A neuro-fuzzy model for modulus of deformation of jointed rock masses." Computers and Geotechncis, 31(5), 375-383.
65. Gribb, M. M., and Gribb, G. W. (1994). "Use of neural networks for hydraulic conductivity determination in unsaturated soil." Proceedings of the 2nd International Conference on Ground Water Ecology, Bethesda, 155-163.
66. Habibagahi, G., and Bamdad, A. (2003). "A neural network framework for mechanical behavior of unsaturated soils." Canadian Geotechnical Journal, 40(3), 684-693.
67. Haj-Ali, R., Pecknold, D. A., Ghaboussi, J., and Voyiadjis, G. Z. (2001). "Simulated micromechanical models using artificial neural networks." Journal of Engineering Mechanics, 127(7), 730-738.
68. Hammerstrom, D. (1993). "Working with neural networks." IEEE Spectrum, 30(7), 46-53.
69. Hanna, A. M., Morcous, G., and Helmy, M. (2004). "Efficiency of pile groups installed in cohesionless soil using artificial neural networks." Canadian Geotechnical Journal, 41(6), 1241-1249.
70. Hanna, A. M., Ural, D., and Saygili, G. (2007). "Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data." Soil Dynamics and Earthquake Engineering, 27(6), 521-540.
71. Hashash, Y. M. A., Jung, S., and Ghaboussi, J. (2004). "Numerical implementation of a neural network based material model in finite element analysis." International Journal for Numerical Methods in Engineering, 59(7), 989-1005.
72. Hassoun, M. H. (1995). Fundamentals of artificial neural networks, MIT Press, Cambridge.
73. Hecht-Nielsen, R. (1987). "Kolmogrov's mapping neural network existence theorem." Proceedings of the 1st IEEE International Joint Conference on Neural Networks, San Diego, CA, III-11-III-14.
74. Hecht-Nielsen, R. (1989). "Theory of the back-propagation neural network." Proceedings of the International Joint Conference on Neural Networks, Washington, DC, 593-605.
75. Hecht-Nielsen, R. (1990). Neurocomputing, Addison-Wesely Publishing Company, Reading, MA.
76. Hornik, K., Stinchcombe, M., and White, H. (1989). "Multilayer feed-forward networks are universal approximators." Neural Networks, 2, 359-366.
77. Hush, D. R., and Horne, B. G. (1993). "Progress in supervised neural networks." IEEE SP Magazine, 10(1), 8-39.
78. Jain, A., Sudheer, K. P., and Srinivasulu, S. (2004). "Identification of physical processes inherent in artificial neural network rainfall runoff models." Hydrological Processes, 18(3), 571-581.
79. Javadi, A., Rezania, M., and Mousavi, N. M. (2006). "Evaluation of liquefaction induced lateral displacements using genetic programming." Computers and Geotechnics, 33(4-5), 222-233.
80. Johari, A., Habibagahi, G., and Ghahramani, A. (2006). "Prediction of soil-water characteristic curve using genetic programming." Journal of Geotechnical and Geoenvironmental Engineering, 132(5), 661-665.
81. Juang, C. H., and Chen, C. J. (1999). "CPT-based liquefaction evaluation using artificial neural networks." Computer-Aided Civil and Infrastructure Engineering, 14(3), 221-229.
82. Karnin, E. D. (1990). "A simple procedure for pruning back-propagation trained neural networks." IEEE Transactions on Neural Networks, 1(2), 239-242.
83. Kavli, T. (1993). "ASMOD-An algorithm for adaptive spline modeling of observation data." International Journal of Control, 58(4), 947-967.
84. Kim, Y., and Kim, B. (2006). "Use of artificial neural networks in the prediction of liquefaction resistance of sands." Journal of Geotechnical and Geoenvironmental Engineering, 132(11), 1502-1504.
85. Kim, Y., and Kim, B. (2008). "Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model." Computers and Geotechnics, 35(3), 313-322.
86. Kingston, G. B., Lambert, M. F., and Maier, H. R. (2005a). "Bayesian parameter estimation applied to artificial neural networks used for hydrological modeling." Water Resources Research, 41(W12409), doi:10.1029/2005WR004152.
87. Kingston, G. B., Maier, H. R., and Lambert, M. F. (2005b). "Calibration and validation of neural networks to ensure physically plausible hydrological modeling." Journal of Hydrology, 314(2005), 158-176.
88. Kingston, G. B., Maier, H. R., and Lambert, M. F. (2006). "A probabilistic method to assist knowledge extraction from artificial neural networks used for hyrdological prediction." Mathematical and Computer Modeling, 44(5-6), 499-512.
89. Kingston, G. B., Maier, H. R., and Lambert, M. F. (2008). "Bayesian model selection applied to artificial neural networks used for water resources modeling." Water Resources Research, 44(W04419), doi:10.1029/2007WR006155.
90. Kocjancic, R., and Zupan, J. (2000). "Modeling of the river flow rate: The influence of the training set selection." Chemometric and Intelligent Laboratory Systems, 54, 21-34.
91. Kudrycki, T. P. (1988). "Neural network implementation of a medical diagnosis expert system," MS thesis, College of Engineering, University of Cincinnati.
92. Kung, G. T., Hsiao, E. C., Schuster, M., and Juang, C. H. (2007). "A neural network approach to estimating deflection of diaphram walls caused by excavation in clays." Computers and Geotechnics, 34(5), 385-396.
93. Kurup, P. U., and Dudani, N. K. (2002). "Neural network for profiling stress history of clays from PCPT data." 2002, 128(7), 569-579.
94. Lachtermacher, G., and Fuller, J. D. (1994). "Back-propagation in hydrological time series forecasting." Stochastic and Statistical Methods in Hydrology and Environmental Engineering, K. W. Hipel, A. I. McLeod, U. S. Panu, and V. P. Singh, eds., Kluwer Academic, Dordrecht.
95. Lapedes, A., and Farber, R. (1988). "How neural networks work." Neural Information Processing Systems, American Institute of Physics, 442-456.
96. Lee, C., and Sterling, R. (1992). "Identifying probable failure modes for underground openings using a neural network." International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts, 29(1), 49-67.
97. Lee, I. M., and Lee, J. H. (1996). "Prediction of pile bearing capacity using artificial neural networks." Computers and Geotechnics, 18(3), 189-200.
98. Lee, S. J., Lee, S. R., and Kim, Y. S. (2003). "An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation." Computers and Geotechnics, 30(6), 489-503.
99. Lefik, M., and Schrefler, B. A. (2003). "Artificial neural network as an incremental nonlinear constitutive model for a finite element code." Computer Methods in Applied Mechanics and Engineering, 192(31-32), 3265-3283.
100. Lu, Y. (2005). "Underground blast induced ground shock and its modeling using artificial neural network." Computers and Geotechnics, 32(3), 164-178.
101. MacKay, G. (1992). "A practical Bayesian framework for back-propagation networks." Neural Computation, 4, 448-472.
102. Maier, H. R., and Dandy, G. C. (2000). "Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications." Environmental Modeling & Software, 15(2000), 101-124.
103. Maren, A., Harston, C., and Pap, R. (1990). Handbook of neural computing applications, Academic Press, San Diego, California.
104. Masters, T. (1993). Practical neural network recipes in C++, Academic Press, San Diego, California.
105. May, R. J., Maier, H. R., Dandy, G. C., and Fernando, T. M. (2008). "Non-linear variable selection for artificial neural networks using partial mutual information." Environmental Modeling and Software, doi:10.1016/j.envsoft.2008.03.007.
106. Mayoraz, F., and Vulliet, L. (2002). "Neural networks for slope movement prediction." The International Journal of Geomechanics, 2(2), 153-173.
107. Miller, G. F., Todd, P. M., and Hedge, S. U. (1989). "Designing neural networks using genetic algorithms." Proceedings of the 3rd International Conference on Genetic Algorithms, Arlington, 379-384.
108. Minns, A. W., and Hall, M. J. (1996). "Artificial neural networks as rainfall-runoff models." Hydrological Sciences Journal, 41(3), 399-417.
109. Moon, H. K., Na, S. M., and Lee, C. W. (1995). "Artificial neural-network integrated with expert-system for preliminary design of tunnels and slopes." Proceedings of the 8th International Congress on Rock Mechanics, Rotterdam: Balkema, 901-905.
110. Najjar, Y. M., and Ali, H. E. (1998). "CPT-based liquefaction potential assessment: A neuronet approach." ASCE Geotechnical Special Publication, 1, 542-553.
111. Najjar, Y. M., and Ali, H. E. (1999). "Simulating the stress-strain behavior of Nevada sand by ANN." Proceedings of the 5th U.S. National Congress on Computational Mechanics (USACM), Boulder, Colorado.
112. Najjar, Y. M., Ali, H. E., and Basheer, I. A. (1999). "On the use of neuronets for simulating the stress-strain behavior of soils." Proceedings of the 7th International Symposium on Numerical Models in Geomechanics, Graz, Austria, 657-662.
113. Najjar, Y. M., and Basheer, I. A. (1996). "Neural network approach for site characterization and uncertainty prediction." ASCE Geotechnical Special Publication, 58(1), 134-148.
114. Najjar, Y. M., Basheer, I. A., and McReynolds, R. (1996a). "Neural modeling of Kansan soil swelling." Transportation Research Record, No. 1526, 14-19.
115. Najjar, Y. M., Basheer, I. A., and Naouss, W. A. (1996b). "On the identification of compaction characteristics by neuronets." Computers and Geotechnics, 18(3), 167-187.
116. Najjar, Y. M., and Huang, C. (2007). "Simulating the stress-strain behavior of Georgia kaolin via recurrent neuronet approach." Computers and Geotechnics, 34(5), 346-362.
117. Narendara, B. S., Sivapullaiah, P. V., Suresh, S., and Omkar, S. N. (2006). "Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study." computers and Geotechnics, 33(3), 196-208.
118. Nawari, N. O., Liang, R., and Nusairat, J. (1999). "Artificial intelligence techniques for the design and analysis of deep foundations." Electronic Journal of Geotechnical Engineering, http://geotech.civeng.okstate.edu/ejge/ppr9909.
119. Neaupane, K., and Achet, S. (2004). "Some applications of a back-propagation neural network in geo-engineering." Environmental Geology, 45(4), 567-575.
120. NeuralWare. (1997). NeuralWorks Predict Release 2.1, NeuralWare Inc., Pittsburgh.
121. Ni, S. H., Lu, P. C., and Juang, C. H. (1996). "A fuzzy neural network approach to evaluation of slope failure potential." Journal of Microcomputers in Civil Engineering, 11, 59-66.
122. Olden, J. D., Joy, M. K., and Death, R. G. (2004). "An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data." Ecological Modeling, 178(3-4), 389-397.
123. Padmini, D., Ilamparuthi, K., and Sudheer, K. P. (2008). "Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models." Computers and Geotechnics, 35(1), 33-46.
124. Pal, M. (2006). "Support vector machine-based modeling of seismic liquefaction potential." International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983-996.
125. Penumadu, D., and Chameau, J.-L. (1997). "Geo-material modeling using artificial neural networks." Artificial neural networks for civil engineers: fundamentals and applications, N. Kartam, I. Flood, and J. Garrett, eds., ASCE, New York, 160-184.
126. Penumadu, D., Jin-Nan, L., Chameau, J.-L., and Arumugam, S. (1994). "Rate dependent behavior of clays using neural networks." Proceedings of the 13th Conference of the International Society of Soil Mechanics & Foundation Engineering, New Delhi, 1445-1448.
127. Penumadu, D., and Zhao, R. (1999). "Triaxial compression behavior of sand and gravel using artificial neural networks (ANN)." Computers and Geotechnics, 24(3), 207-230.
128. Provenzano, P., Ferlisi, S., and Musso, A. (2004). "Interpretation of a model footing response through an adaptive neural fuzzy inference system." Computers and Geotechnics, 31(3), 251-266.
129. Rahman, M. S., Wang, J., Deng, W., and Carter, J. P. (2001). "A neural network model for the uplift capacity of suction cassions." Computers and Geotechnics, 28(4), 269-287.
130. Rankine, R., and Sivakugan, N. (2005). "Prediction of paste backfill performance using artificial neural networks." Proceedings of the 16th International Society for Soil Mechanics and Foundation Engineering Osaka, Japan, 1107-1110.
131. Ray, C., and Klindworth, K. K. (2000). "Neural networks for agrichemical vulnerability assessment of rural private wells." Journal of Hydrologic Engineering, 5(2), 162-171.
132. Rezania, M., and Javadi, A. (2007). "A new genetic programming model for predicting settlement of shallow foundations." Canadian Geotechnical Journal, 44(12), 1462-1472.
133. Ripley, B. D. (1996). Pattern recognition and neural networks, Cambridge University Press, Cambridge.
134. Rizzo, D. M., and Dougherty, D. E. (1994). "Application of artificial neural networks for site characterization using hard and soft information." Proceedings of the 10th International Conference on Computational Methods in Water Resources, Dordrecht: Kluwer Academic, 793-799.
135. Rizzo, D. M., Lillys, T. P., and Dougherty, D. E. (1996). "Comparisons of site characterization methods using mixed data." ASCE Geotechnical Special Publication, 58(1), 157-179.
136. Rogers, L. L., and Dowla, F. U. (1994). "Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling." Water Resources Research, 30(2), 457-481.
137. Rojas, R. (1996). Neural networks: A systematic introduction, Springer-Verlag, Berlin.
138. Romo, M., Garcia, S. R., Mendoza, M. J., and Taboada-Urtuzuastegui, V. (2001). "Recurrent and Constructive-Algorithm Networks for Sand Behavior Modelling." The International Journal of Geomechanics, 1(4), 371-387.
139. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). "Learning internal representation by error propagation." Parallel Distributed Processing, D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge.
140. Salchenberger, L. M., Cinar, E. M., and Lash, N. A. (1992). "Neural networks: A new tool for predicting thrift failures." Decision Science, 23, 899-916.
141. Samui, P. (2008). "Prediction of friction capacity of driven piles in clay using the support vector machine." Canadian Geotechnical Journal, 45(2), 288-296.
142. Sarle, W. S. (1994). "Neural networks and statistical models." Proceedings of the 19th Annual SAS Users Group International Conference, Cary, NC: SAS Institute, 1538-1550.
143. Shahin, M. A. (2008). "Modeling axial capacity of pile foundations by intelligent computing." Proceedings of the BGA International Conference on Foundations, Dundee (Scotland), in press.
144. Shahin, M. A., and Indraratna, B. (2006). "Modeling the mechanical behavior of railway ballast using artificial neural networks." Canadian Geotechnical Journal, 43(1), 1144-1152.
145. Shahin, M. A., and Jaksa, M. B. (2004). "Probabilistic assessment of the uncertainty associated with the pullout capacity of marquee ground anchors." Proceedings of the 9th Australia New Zealand Conference on Geomechanics, Auckland, In Press.
146. Shahin, M. A., and Jaksa, M. B. (2005a). "Modeling the pullout capacity of marquee ground anchors using neurofuzzy technique." Proceedings of the International Journal of Modeling and Simulation, MODSIM 2005, Melbourne, Australia, 66-72.
147. Shahin, M. A., and Jaksa, M. B. (2005b). "Neural network prediction of pullout capacity of marquee ground anchors." Computers and Geotechnics, 32(3), 153-163.
148. Shahin, M. A., and Jaksa, M. B. (2006). "Pullout capacity of small ground anchors by direct cone penetration test methods and neural methods." Canadian Geotechnical Journal, 43(6), 626-637.
149. Shahin, M. A., and Jaksa, M. B. (2008). "Intelligent computing for predicting axial capacity of drilled shafts." International Foundation Congress and Equipment Expo '09, Florida, abstract submitted.
150. Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). "Artificial neural network applications in geotechnical engineering." Australian Geomechanics, 36(1), 49-62.
151. Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2002a). "Artificial neural networkbased settlement prediction formula for shallow foundations on granular soils." Australian Geomechanics, 37(4), 45-52.
152. Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2003a). "Neurofuzzy networks applied to settlement of shallow foundations on granular soils." Proceedings of the 9th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP9, San Francisco, Millpress, Rotterdam, 1379-1383.
153. Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2004a). "Application of neural networks in foundation engineering." International e-Conference on Modern Trends in Foundation Engineering: Geotechnical Challenges & Solutions, http://www.civil.iitm.ac.in.
154. Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2005a). "Neural network based stochastic design charts for settlement prediction." Canadian Geotechnical Journal, 42(1), 110-120.
155. Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2005b). "Stochastic simulation of settlement of shallow foundations based on a deterministic neural network model." Proceedings of the International Congress on Modeling and Simulation, MODSIM 2005, Melbourne (Australia), 73-78.
156. Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2002b). "Predicting settlement of shallow foundations using neural networks." Journal of Geotechnical & Geoenvironmental Engineering, ASCE, 128(9), 785-793.
157. Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2003b). "Closure to: Predicting settlement of shallow foundations on cohesionless soils using neural networks." Journal of Geotechnical & Geoenvironmental Engineering, ASCE, 128(9), 785-793. of the International Congress on Modeling and Simulation, MODSIM 2003, Townsville, Queensland, 1886-1891.
158. Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2003d). "Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models." Computers and Geotechnics, 30(8), 637-647.
159. Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2004b). "Data division for developing neural networks applied to geotechnical engineering." Journal of Computing in Civil Engineering, ASCE, 18(2), 105-114.
160. Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2005c). "Investigation into the robustness of artificial neural network models for a case study in civil engineering." Proceedings of the International Congress on Modeling and Simulation, MODSIM 2005, Melbourne (Australia), 79-83.
161. Shang, J. Q., Ding, W., Rowe, R. K., and Josic, L. (2004). "Detecting heavy metal contamination in soil using complex permittivity and artificial neural networks." Canadian Geotechnical Journal, 41(6), 1054-1067.
162. Shi, J., Ortigao, J. A. R., and Bai, J. (1998). "Modular neural networks for predicting settlement during tunneling." Journal of Geotechnical & Geoenvironmental Engineering, ASCE, 124(5), 389-395.
163. Shi, J. J. (2000). "Reducing prediction error by transforming input data for neural networks." Journal of Computing in Civil Engineering, ASCE, 14(2), 109-116.
164. Sidarta, D. E., and Ghaboussi, J. (1998). "Constitutive modeling of geomaterials from non-uniform material tests." Computers & Geomechanics, 22(10), 53-71.
165. Singh, T. N., and Singh, V. (2005). "An intelligent approach to prediction and control ground vibration in mines." Geotechnical and Geological Engineering, 23(3), 249-262.
166. Sinha, S. K., and Wang, M. C. (2008). "Artificial neural network prediction models for soil compaction and permeability." Geotechnical Engineering Journal, 26(1), 47-64.
167. Sivakugan, N., Eckersley, J. D., and Li, H. (1998). "Settlement predictions using neural networks." Australian Civil Engineering Transactions, CE40, 49-52.
168. Smith, G. N. (1986). Probability and statistics in civil engineering: An introduction, Collins, London.
169. Smith, M. (1993). Neural networks for statistical modeling, Van Nostrand Reinhold, New York.
170. Stone, M. (1974). "Cross-validatory choice and assessment of statistical predictions." Journal of Royal Statistical Society, B 36, 111-147.
171. Sudheer, K. P. (2005). "knowledge extraction from trained neural network river flow models." Journal of Hydologic Engineering, 10(4), 264-269.
172. Sudheer, K. P., and Jain, A. (2004). "Explaining the internal behavior of artificial neural network river flow models." Hydrological Processes, 18(4), 833-844.
173. Sudheer, K. P., Nayak, P. C., and Ramasastri, K. S. (2003). "Improving peak flow estimates in artificial neural network river flow models." Hydrological Processes, 17, 677-686.
174. Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2003c). "Neural and neurofuzzy techniques applied to modeling settlement of shallow foundations on granular soils." Proceedings 175.
175. Teh, C. I., Wong, K. S., Goh, A. T. C., and Jaritngam, S. (1997). "Prediction of pile capacity using neural networks." Journal of Computing in Civil Engineering, ASCE, 11(2), 129-138.
176. Tokar, S. A., and Johnson, P. A. (1999). "Rainfall-runoff modeling using artificial neural networks." Journal of Hydrologic Engineering, 4(3), 232-239.
177. 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, DC.
178. Twomey, J. M., and Smith, A. E. (1997). "Validation and verification." Artificial neural networks for civil engineers: Fundamentals and applications, N. Kartam, I. Flood, and J. H. Garrett, eds., ASCE, New York, 44-64.
179. Ural, D. N., and Saka, H. (1998). "Liquefaction assessment by neural networks." Electronic Journal of Geotechnical Engineering, http://www.ejge.com/Ppr9803/ Ppr9803.htm
180. White, H. (1989). "Learning in artificial neural networks: A statistical perspective." Neural Computation, 1, 425-464.
181. Yang, Y., and Rosenbaum, M. S. (2002). "The artificial neural network as a tool for assessing geotechnical properties." Geotechnical Engineering Journal, 20(2), 149-168.
182. Yoo, C., and Kim, J. (2007). "Tunneling performance prediction using an integrated GIS and neural network." Computers and Geotechnics, 34(1), 19-30.
183. Young-Su, K., and Byung-Tak, K. (2006). "Use of artificial neural networks in the prediction of liquefaction resistance of sands." Journal of Geotechnical and Geoenvironmental Engineering, 132(11), 1502-1504.
184. Yu, X.-H. (1992). "Can back-propagation error surface not have local minima." IEEE Transaction on Neural Networks, 3, 1019-1021.
185. Zhao, H. (2008). "Slope reliability analysis using a support vector machine." Computers and Geotechnics, 35(3), 459-467.
186. Zhou, Y., and Wu, X. (1994). "Use of neural networks in the analysis and interpretation of site investigation data." Computer and Geotechnics, 16, 105-122.
187. Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998a). "Modeling of soil behavior with a recurrent neural network." Canadian Geotechnical Journal, 35(5), 858-872.
188. Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998b). "Modeling of shearing behaviour of a residual soil with recurrent neural network." International Journal of Numerical and Analytical Methods in Geomechanics, 22(8), 671-687.
189. Zhu, J. H., Zaman, M. M., and Trafalis, T. B. (1996). "Prediction of shear stress-strain behavior of soil with recurrent neural network." Intelligent Engineering Systems Through Artificial Neural Networks, 6, 809-814.
190. Zurada, J. M. (1992). Introduction to artificial neural systems, West Publishing Company, St. Paul.