Artificial Neural Networks for Modeling Drained Monotonic Behavior of Rockfill Materials
Publication: International Journal of Geomechanics
Volume 14, Issue 3
Abstract
In this paper, the feasibility of developing and using artificial neural networks (ANNs) for modeling the monotonic behaviors of various angular and rounded rockfill materials is investigated. The database used for development of the ANN models is comprised of a series of 82 large-scale, drained triaxial tests. The deviator stress-volumetric strain versus axial strain behaviors were first simulated by using ANNs. A feedback model using multilayer perceptrons for predicting drained behavior of rockfill materials was developed in the MATLAB environment, and the optimal ANN architecture was obtained by a trial-and-error approach in accordance with error indexes and real data. Reasonable agreement between the simulated behaviors and the test results was observed, indicating that the ANNs are capable of capturing the behavior of rockfill materials. The ability of ANNs to predict the constitutive hardening-soil model parameters, residual deviator stresses, and corresponding volumetric strain was also investigated. Moreover, the generalization capability of ANNs was also used to check the effects of items not tested, such as dry density, grain-size distributions, and Los Angeles abrasion.
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Acknowledgments
The author is grateful to the Department of Geotechnical Engineering, Road, Housing and Urban Development Research Center (BHRC), for conducting the tests and for financial support, and to the Ministry of Energy, as the project’s client, for providing the data for this research. Also, the author thanks Dr. Piltan Tabatabei Shorjeh for his valuable suggestions. The open source code of the proposed models in the MATLAB7 environment is available from the author.
References
Aghaei Araei, A. (2002). “Back analysis of deformations induced during first impounding of Masjed-E-Soleyman dam.” M.S. thesis, Dept. of Civil and Environmental Engineering, Amirkabir Univ. of Technology, Tehran, Iran.
Aghaei Araei, A., Soroush, A., and Rayhani, M. (2010a). “Large-scale triaxial testing and numerical modeling of rounded and angular rockfill materials.” Sci. Iran. Trans. Civ. Eng., 17(3), 169–183.
Aghaei Araei, A., Soroush, A., Tabatabaei, S. H., and Ghalandarzadeh, A. (2010b). “Assessment of monotonic triaxil behavior of rockfill materials.” Research Project No. 1-1520-2009, Road, Housing and Urban Development Research Center, Tehran, Iran.
Akin, S., and Karpuz, C. (2008). “Estimating drilling parameters for diamond bit drilling operations using artificial neural networks.” Int. J. Geomech., 68–73.
ASTM. (2003). “Standard test methods for laboratory compaction characteristics of soil using modified effort.” D1557-02e1, West Conshohocken, PA.
ASTM. (2004). “Standard test method for consolidated undrained triaxial compression test for cohesive soils.” D4767-04, West Conshohocken, PA.
ASTM. (2009). “Standard test method for resistance to degradation of large-size coarse aggregate by abrasion and impact in the Los Angeles machine.” C535-09, West Conshohocken, PA.
Banimahd, M., Yasrobi, S. S., and Woodward, P. K. (2005). “Artificial neural network for stress–strain behavior of soils: Knowledge based verification.” Comput. Geotech., 32(5), 377–386.
Baziar, M. H., and Jafarian, Y. (2007). “Assessment of liquefaction triggering using strain energy concept and ANN model: Capacity energy.” Soil. Dyn. Earthquake Eng., 27(12), 1056–1072.
Brinkgreve, R. B. J., and Vermeer, P. A. (1998). PLAXIS finite element code for soil and rock analyses user's manual, version 7, Plaxis, Delft, Netherlands.
Das, K. S., Samui, P., and Sabat, A. K. (2012). “Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine.” Int. J. Geomech., 606–61.
Duncan, J. M., and Chang, C.-Y. (1970). “Nonlinear analysis of stress and strain in soils.” J. Soil Mech. and Found. Div., 96(5), 1629–1653.
Ellis, G. W, Yao, C., Zhao, R., and Penumadu, D. (1995). “Stress-strain modeling of sands using artificial neural networks.” J. Geotech. Engrg., 429–435.
Fumagalli, E. (1969). “Tests on cohesionless materials for rockfill dams.” J. Soil Mech. and Found. Div., 95(1), 313–332.
Ghanbari, A., Sadeghpour, A. H., Mohamadzadeh, H., and Mohamadzadeh, M. (2008). “An experimental study on the behavior of rockfill materials using large scale tests.” Electron. J. Geotech. Eng., 13(Bundle G), 1–16.
Gupta, A. K. (2000). “Constitutive modeling of rockfill materials.” Ph.D. dissertation, Indian Institute of Technology, Delhi, India.
Gupta, A. K. (2009). “Effect of particle size and confining pressure on breakage and strength parameters of rockfill materials.” Electron. J. Geotech. Eng., 14(Bundle H), 1–12.
Habibagahi, G., and Bamdad, A. (2003). “A neural network framework for mechanical behavior of unsaturated soils.” Can. Geotech. J., 40(3), 684–693.
Hagan, M. T., and Menhaj, M. B. (1996). “Training feedforward networks with Marquardt algorithm.” IEEE Trans. Neural Netw., 5(6), 989–992.
Hashem, S. (1993). “Sensitivity analysis for feedforward artificial neural networks with differentiable activity functions.” Proc., Int. Conf. on Neural Networks, Vol. 1, IEEE, New York, 419–429.
Hornik, K. M., Stinchcombe, M., and White, H. (1989). “Multilayer feedforward networks are universal approximators.” Neural Netw., 2(5), 359–366.
Indraratna, B., Ionescu, D., and Christie, H. D. (1998). “Shear behavior of railway ballast based on large-scale triaxial tests.” J. Geotech. Geoenviron. Eng., 439–449.
Juang, C. H., and Chen, C. J. (1999). “CPT-based liquefaction evaluation using artificial neural networks.” Comput. Aided Civ. Infrastruct. Eng., 14(3), 221–229.
Lawrence, J., and Fredrickson, J. (1998). Brain maker user’s guide and reference manual, 7th Ed., California Scientific Software Press, Nevada City, CA.
Lu, M., AbouRizk, S. M., and Hermann, U. H. (2001). “Sensitivity analysis of neural networks in spool fabrication productivity studies.” J. Comput. Civ. Eng., 299–308.
MATLAB7 [Computer software]. Natick, MA, MathWorks.
Marachi, N. D., Chan, C. K., and Seed, H. B. (1972). “Evaluation of properties of rockfill materials.” J. Soil Mech. and Found. Div., 98(1), 95–114.
Marsal, R. J. (1967). “Large scale testing of rockfill materials.” J. Soil Mech. and Found. Div., 93(2), 27–43.
Najjar, Y., and Zhang, X. C. (2000). “Characterizing the 3D stress–strain behavior of sandy soils: a neuro mechanistic approach.” Numerical methods in geotechnical engineering, G. M. Filz and D. V. Griffiths, eds., ASCE, New York, 43–57.
Najjar, Y. M., and Ali, H. E. (1999). “On the use of neuronets for simulating the stress-strain behavior of soils.” Proc., 7th Int. Symp. on Numerical Models in Geomechanics, G. N. Pande, S. Pietruszczak, and H. F. Schweiger, eds., Taylor & Francis, London, 657–662.
Penumadu, D., and Zhao, R. (1999). “Triaxial compression behavior of sand and gravel using artificial neural networks (ANN).” Comput. Geotech., 24(3), 207–230.
Sakellariou, M. G., and Ferentinou, M. D. (2005). “A study of slope stability prediction using neural networks.” Geotech. Geol. Eng., 23(4), 419–445.
Salim, W., and Indraratna, B. (2004). “A new elastoplastic constitutive model for coarse granular aggregates incorporating particle breakage.” Can. Geotech. J., 41(4), 657–671.
Samui, P., and Sitharam, T. G. (2010). “Site characterization model using artificial neural network and kriging.” Int. J. Geomech., 171–180.
Schanz, T., Vermeer, P. A., and Bonnier, P. G. (1999). “The hardening-soil model-formulation and verification.” Beyond 2000 in computational geotechnics: Ten years of PLAXIS International, R. B. J. Brinkgreve, ed., Balkema, Amsterdam, Netherlands, 281–296.
Shahin, M. A., and Indraratna, B. (2006). “Modeling the mechanical behavior of railway ballast using artificial neural networks.” Can. Geotech. J., 43(11), 1144–1152.
Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2004). “Data division for developing neural networks applied to geotechnical engineering.” J. Comput. Civ. Eng., 105–114.
Smith, G. N. (1986). Probability and statistics in civil engineering: An introduction, Collins, London.
Soroush, A., and Aghaei Araei, A. (2006). “Analysis of behavior of a high rockfill dam.” Proc. Inst. Civ. Eng. Geotech. Eng., 159(1), 49–59.
Tatsouka, F. (2009). “Importance of high backfill compaction for better performance of soil structure.” Proc., Technical Committee 29 (TC29) Meeting of the Int. Society of Soil Mechanics and Geotechnical Engineering (ISSMGE), International Society of Soil Mechanics and Geotechnical Engineering, London.
Thiers, G. R., and Donovan, T. D. (1981). “Field density gradation and triaxial testing of large-size rockfill for Little Blue Run Dam, Laboratory shear strength of soil.” ASTM STP 740, R. N. Yong and F. C. Townsend, eds., ASTM, West Conshohocken, PA, 315–325.
Varadarajan, A., Sharma, K. G., Abbas, S. M., and Dhawan, A. K. (2006a). “Constitutive model for rockfill materials and determination of material constants.” Int. J. Geomech., 226–237.
Varadarajan, A., Sharma, K. G., Abbas, S. M., and Dhawan, A. K. (2006b). “The role of nature of particles on the behavior of rockfill materials.” Soils Found., 46(5), 569–584.
Varadarajan, A., Sharma, K. G., Venkatachalam, K., and Gupta, A. K. (2003). “Testing and modeling two rockfill materials.” J. Geotech. Geoenviron. Eng., 206–218.
Venkatachalam, K. (1993). “Prediction of mechanical behavior of rockfill materials.” Ph.D. thesis, Indian Institute of Technology, Delhi, India.
Zaman, M., Solanki, P., Ebrahimi, A., and White, L. (2010). “Neural network modeling of resilient modulus using routine subgrade soil properties.” Int. J. Geomech., 1–12.
Zhu, J.-H., Zaman, M. M., and Anderson, S. A. (1998). “Modeling of soil behavior with a recurrent neural network.” Can. Geotech. J., 35(5), 858–872.
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© 2014 American Society of Civil Engineers.
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Received: Dec 22, 2012
Accepted: May 28, 2013
Published online: May 30, 2013
Published in print: Jun 1, 2014
Discussion open until: Aug 20, 2014
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