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

doi:10.1007/s11709-017-0446-2

Frontiers of Structural and Civil Engineering

2018, Vol. 12

Issue (4): 594-608 https://doi.org/10.1007/s11709-017-0446-2

本期目录

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

Abdussamad ISMAIL(

)

Department of Civil Engineering, Bayero University, Kano, Nigeria

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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.

Key words： piles in compression load-deformation behaviour product-unit neural network

收稿日期: 2017-04-10 出版日期: 2018-11-20

Corresponding Author(s): Abdussamad ISMAIL

引用本文:

. [J]. Frontiers of Structural and Civil Engineering, 2018, 12(4): 594-608.
Abdussamad ISMAIL. ANN-based empirical modelling of pile behaviour under static compressive loading. Front. Struct. Civ. Eng., 2018, 12(4): 594-608.

链接本文:

https://academic.hep.com.cn/fsce/CN/10.1007/s11709-017-0446-2
https://academic.hep.com.cn/fsce/CN/Y2018/V12/I4/594

Fig.1

Tab.1

	Length (m)		Perimeter (m)		Base area (m²)		Elastic modulus (GPa)
							Concrete		Steel
Set	Training	Testing	Training	Testing	Training	Testing	Training	Testing	Training	Testing
$x ¯$	11.785	15.782	1.5627	1.3405	0.37230	0.26920	28.93	28.93	200	200
$x m i n$	30.480	31.590	3.8023	2.3939	2.63885	0.51890	28.975	28.975	200	200
$x m a x$	3.650	6.240	0.3990	0.6384	0.00033	0.00032	28.9	28.9	200	200

Tab.2

Tab.3

Tab.4

Parameter	Concrete pile			Steel pile
Parameter	Driven	Bored (dry)	Bored (wet)	H-steel	Pipe
$C 1 *$	-1.2617	-1.5265	-3.158	-0.4825	-1.2477
$C 2 *$	7.4131	4.0196	9.417	1.2000	9.8365
$C 3 *$	-1156.7931	-2773.5869	-1743.864	-497.4374	-778.0912
$C 4 *$	12.5277	1.8631	0.086	58.9117	9.7524
$C 5 *$	843.1412	2280.6517	1520.356	520.3886	586.0043
$C 6 *$	-0.0544	-0.0035	-0.006	-0.1629	-0.1430

Tab.5

Piles in silt	Piles in clay
$C 1 = C 1 * (f m + 1) α 1 \| (f P + 1) 1.1674$	$C 1 = C 1 * (f c + 1) β 1 \| (f P + 1) 1.1674$
$C 2 = C 2 * (f m + 1) α 2 \| (f P + 1) 1.0706$	$C 2 = C 2 * (f c + 1) β 2 \| (f P + 1) 1.0706$
$C 3 = C 3 * (f m + 1) α 3 \| (f P + 1) 0.1185$	$C 3 = C 3 * (f c + 1) β 3 \| (f P + 1) 0.1185$
$C 4 = C 4 * (f m + 1) α 4 \| (f P + 1) 1.8514$	$C 4 = C 4 * (f c + 1) β 4 \| (f P + 1) 1.8514$
$C 5 = C 5 * (f m + 1) α 5 \| (f P + 1) − 0.1248$	$C 5 = C 5 * (f c + 1) β 5 \| (f P + 1) − 0.1248$
$C 6 = C 6 * (f m + 1) α 6 \| (f P + 1) 1.4099$	$C 6 = C 6 * (f c + 1) β 6 \| (f P + 1) 1.4099$

Tab.6

Parameter	Pile type		Parameter	Pile type
Parameter	Driven	Bored	Parameter	Driven	Bored
$α 1$	1.2116	0.0954	$β 1$	-0.0544	-0.2202
$α 2$	-3.5895	0.9541	$β 2$	-6.4958	0.4383
$α 3$	-0.9161	0.1716	$β 3$	-0.6348	-0.1101
$α 4$	3.7225	3.7366	$β 4$	2.0345	2.2703
$α 5$	-0.1026	2.0021	$β 5$	0.1708	0.0957
$α 6$	1.0802	2.3988	$β 6$	0.6985	6.8073

Tab.7

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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 https://doi.org/10.1680/geot.1992.42.3.411
6	Poskitt T J. A new method for single pile settlement prediction and analysis. Geotechnique, 1993, 43(4): 615–619 https://doi.org/10.1680/geot.1993.43.4.615
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 https://doi.org/10.1016/j.commatsci.2015.02.045
8	Rafiq M Y, Bugmann G, Easterbrook D J. Neural network design for engineering applications. Computers & Structures, 2001, 79(17): 1541–1552 https://doi.org/10.1016/S0045-7949(01)00039-6
9	Ghaboussi J, Sidarta D E. New nested adaptive neural networks (nann) for constitutive modelling. Computers and Geotechnics, 1998, 22(1): 29–52 https://doi.org/10.1016/S0266-352X(97)00034-7
10	Lee T L, Jeng D S. Artificial neural networks for tide forecasting. Ocean Engineering, 2002, 29(9): 1003–1022 https://doi.org/10.1016/S0029-8018(01)00068-3
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 https://doi.org/10.1016/j.asoc.2010.11.021
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 https://doi.org/10.1016/j.engappai.2012.12.011
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 https://doi.org/10.1016/j.mechmat.2013.07.021
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 https://doi.org/10.1016/j.compositesb.2013.11.014
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 https://doi.org/10.1016/j.compgeo.2009.04.003
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 https://doi.org/10.1016/j.amc.2006.07.025
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 https://doi.org/10.1109/4235.985692
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 https://doi.org/10.1016/j.neucom.2007.10.013
22	Durbin R, Rumelhart R. Product units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation, 1989, 1(1): 133–142 https://doi.org/10.1162/neco.1989.1.1.133
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 https://doi.org/10.1109/69.494162
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 https://doi.org/10.1016/j.commatsci.2014.04.066
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 https://doi.org/10.1016/j.advengsoft.2016.06.005
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 https://doi.org/10.1680/geot.1959.9.4.153
36	Goh A T C. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 1995, 9(9): 143–151 https://doi.org/10.1016/0954-1810(94)00011-S
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 https://doi.org/10.1016/S0266-352X(00)00033-1
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 https://doi.org/10.1061/(ASCE)1090-0241(1999)125:2(122)

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