ANN-based empirical modelling of pile behaviour under static compressive loading

Abstract

Artificial neural networks have been widely used over the past two decades to successfully develop empirical models for a variety of geotechnical problems. In this paper, an empirical model based on the product-unit neural network (PUNN) is developed to predict the load-deformation behaviour of piles based SPT values of the supporting soil. Other parameters used as inputs include particle grading, pile geometry, method of installation as well as the elastic modulus of the pile material. The model is trained using full-scale pile loading tests data retrieved from FHWA deep foundations database. From the results obtained, it is observed that the proposed model gives a better simulation of pile load-deformation curves compared to the Fleming’s hyperbolic model and t-z approach.

Keywords

piles in compression / load-deformation behaviour / product-unit neural network

References

[1] Poulos H G. Settlement of pile foundations. Numerical methods in Geotechnical Engineerng, McGraw-Hill, New York, 1977, 326–363

[2] Desai C S. Deep foundations. Numerical methods in Geotechnical Engineerng. McGraw-Hill, New York, 1977, 235–271

[3] Tomlinson M J. Pile Design and Construction Practice. Longman, 6th edition, 1995

[4] Chin F K. Estimation of the ultimate load of piles from tests not carried to failure. In: Proc. 2nd SE Asian Conf. Soil Engrg, Singapore, 1970, 81–92

[5] Fleming W. A new method of single pile settlement and analysis. Geotechnique, 1992, 42(3): 411–425

[6] Poskitt T J. A new method for single pile settlement prediction and analysis. Geotechnique, 1993, 43(4): 615–619

[7] Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of pncs: A stochastic approach based on ann and anfis. Computational Materials Science, 2015, 102: 304–313

[8] Rafiq M Y, Bugmann G, Easterbrook D J. Neural network design for engineering applications. Computers & Structures, 2001, 79(17): 1541–1552

[9] Ghaboussi J, Sidarta D E. New nested adaptive neural networks (nann) for constitutive modelling. Computers and Geotechnics, 1998, 22(1): 29–52

[10] Lee T L, Jeng D S. Artificial neural networks for tide forecasting. Ocean Engineering, 2002, 29(9): 1003–1022

[11] Kabiri-Samani A R, Aghaee-Tarazjani J, Borghei S M, Jeng D S. Application of neural networks and fuzzy logic models to long-shore sediment transport. Applied Soft Computing, 2011, 11(2): 2880–2887

[12] Ismail A, Jeng D S, Zhang L L, Zhang J S. Predictions of bridge scour: Application of a feed-forward neural network with an adaptive activation function. Engineering Applications of Artificial Intelligence, 2013, 26(5-6): 1540–1549

[13] Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84

[14] Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (pncs). Composites. Part B, Engineering, 2014, 59: 80–95

[15] Pooya Nejad F, Jaksa M B, Kakhi M, McCabe B A. Prediction of pile settlement using artificial neural networks based on standard penetration test data. Computers and Geotechnics, 2009, 36(7): 1125–1133

[16] Ismail A, Jeng D S. Modelling load settlement behaviour of piles using high-order neural network (hon-pile model). Eng. Appl. of AI., 2011, 24(5): 813–821

[17] Eberhart R C, Kennedy J. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, 39–43

[18] Zhang J R, Zhang J, Lok T M, Lyu M R. A hybrid particle swarm optimizationback propagation algorithm for feedforward neural network training. Applied Mathematics and Computation, 2007, 185(2): 1026–1037

[19] Clerc M, Kennedy J. The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58–73

[20] Fun M H, Hagan M T. Levenberg-marquardt training for modular networks. In IEEE International Conference on Neural Networks, 1996, 468–473

[21] Yu J, Wang S, Xi L. Evolving artificial neural networks using an improved pso and dpso. Neurocomputing, 2008, 71(4-6): 1054–1060

[22] Durbin R, Rumelhart R. Product units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation, 1989, 1(1): 133–142

[23] Prevost J H, Popescu R. Constitutive relations for soil materials. Electronic Journal of Geotechnical Engineering, 1996, 1

[24] Poulos H G, Davis E H. Pile foundation analysis and design. Wiley, New Jersey, 1980

[25] Kulhawy F H, Mayne P W. Manual on estimating soil properties fro foundation design. Technical Report 2-38, Electric power Res. Inst. EL-6800; Palo Alto Carlifonia, 1990

[26] Bowles J E. Foundation analysis and design. McGraw-Hill, New York, 1997

[27] Fleming W G K, Weltman A J, Randolph M F, Elson W K. Piling Engineering. Taylor and Francis, Oxford, 2009

[28] Swingler K. Applying neural networks: a practical guide. Academic Press, New York, 1996

[29] Looney C G. Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(2): 211–226

[30] Nelson M, Illingworth W T. A practical guide to neural nets. Addisin-Wesley, Reading MA, 1990

[31] Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions ofmechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535

[32] Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

[33] McVay M C, Towsend F C, Bloomquist D G, O’Brien M O, Caliendo J A. Numerical analysis of vertically loaded pile groups. Proceedings of the Foundation Engineering Congress, North Western University, Illinois, 1989, 675–690

[34] Meyerhof G G. Bearing capacity and settlement of pile foundations. Journal of Geotechnical Engineering, 1976, GT3(102): 197–228

[35] Skempton A W. Cast in situ bored piles in london clay. Geotechnique, 1959, 3(4): 153–173

[36] Goh A T C. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 1995, 9(9): 143–151

[37] Rahman M S, Wang J, Deng W, Carter J P. A neural network model of the uplift capacity of suction caissons. Computers and Geotechnics, 2001, 28(4): 269–287

[38] Reese L C, O’Neill M W. Drilled shafts: Construction and design. Technical Report HI-88-042, FHWA, 1988

[39] Balakrishnan E G, Balasubramaniam A S, Phien-Wej N. Load deformation analysis of bored piles in residual weathered formation. Journal of Geotechnical and Geoenvironmental Engineering, 1999, 125(2): 122–131

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag GmbH Germany