PREDICTION OF UPLIFT CAPACITY OF SUCTION CAISSON IN CLAY USING FUNCTIONAL NETWORK AND MULTIVARIATE ADAPTIVE REGRESSION SPLINE

Document Type : Article

Authors

1 Research Scholar, Civil Engineering Department, National Institute of Technology, Rourkela, India - 769008

2 Civil Engineering Department, National Institute of Technology, Rourkela, India - 769008

Abstract

Suction caissons are extensively used as anchors for offshore foundation structures. The uplift capacity of suction caisson is an important factor from effective design point of view. In this paper, two recently developed AI techniques, functional network (FN) and multivariate adaptive regression spline (MARS), have been used to predict the uplift capacity of suction caisson in clay.  The performances of the developed models are compared with other AI techniques; artificial neural network, support vector machine, relevance vector machine, genetic programming, extreme learning machine and group method of data handling with harmony search  (GMDH-HS). The model inputs include the aspect ratio of the caisson, undrained shear strength of soil at the depth of the caisson tip, relative depth of the lug at which the caisson force is applied, load inclination angle and load rate parameter. Comparative analyses are made with the results of the above AI techniques, using different statistical performances criteria; correlation coefficient (R), root mean square error, Nash-Sutcliffe coefficient of efficiency, log-normal distribution of ratio of predicted to observed load capacity, with a ranking system to find out the best predictive model. The FN and MARS models are found to be comparably efficient and they outperform other AI techniques.

Keywords

Main Subjects


References

1. Senepere, D. and Auvergne, G.A. \Suction anchor
piles-a proven alternative to driving and drilling", O -
shore Technology Conference, Houston, Texas, Houston,
Texas, Paper No. 4206 (1982).
S. Bhattacharya et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 517{531 529
2. Albert, L.F., Holtz, R.D., and Magris, E. \The superpile
system: A feasible alternate foundation for TLP
in deep water", In Proceedings of the 19th Annual O -
shore Technology Conference, Houston, Texas, OTC
5392, pp. 307-314 (1987).
3. Clukey, E.C., Morrison, M.J., Gariner, J., and Corte,
J.F. \The response of suction caisson in normally
consolidated clays to cyclic TLP loading conditions",
Proceedings of the 27th Annual O shore Technology
Conference, Houston, Texas, pp. 909-918 (1995).
4. Cao, J., Phillips, R., and Popescu, R. \Physical
and numerical modelling on suction caissons in clay",
Proceedings of the 18th Canadian Congress of Applied
Mechanics, Memorial University of Newfoundland, St.
John's, Canada, pp. 217-218 (2001).
5. Cao, J., Phillips, R., Audibert, J.M.E., and Al-
Khafazi, Z. \Numerical analysis of the behaviour of
suction caissons in clay", Proceedings of the 12th International
O shore and Polar Engineering Conference,
Kitakyushu, Japan, pp. 795-799 (2002a).
6. Cao, J., Phillips, R., Popescu, R., Al-Khafaji, Z., and
Audibert, J.M.E. \Penetration resistance of suction
caissons in clay", Proceedings of the 12th International
O shore and Polar Engineering Conference,
Kitakyushu, Japan, pp. 800-806 (2002b).
7. El-Gharbawy, S.L. and Olson, R.E. \Modeling of
suction caisson foundations", Proceedings of the 10th
International O shore and Polar Engineering Conference,
Seattle, Washington, USA (2000).
8. Whittle, A.J. and Kavvadas, M.J. \Formulation
of MIT-E3 constitutive model for overconsolidated
clays", Journal of Geotechnical Engineering, 120(1),
pp. 173-198 (1994).
9. Zdravkovic, L., Potts, D.M., and Jardine, R.J. \A
parametric study of the pull-out capacity of bucket
foundations in soft clay", Geotechnique, 51(1), pp. 55-
67 (2001).
10. Goodman, L.J., Lee, C.N., and Walker, F.J. \The
feasibility of vacuum anchorage in soil", Geotechnique,
1(4), pp. 356-359 (1961).
11. Larsen, P. \Suction anchors as an anchoring system
for
oating o shore constructions", Proc. 21st Annual
O shore Technology Conference, Houston, TX, pp.
535-540 (1989).
12. Steensen-Bach, J.O. \Recent model tests with suction
piles in clay and sand", Proceedings of the 24th Annual
O shore Technology Conference, Houston, TX, 2, pp.
323-330 (1992).
13. Datta, M. and Kumar, P. \Suction beneath cylindrical
anchors in soft clay", Proceedings of the Sixth International
O shore and Polar Engineering Conference, Los
Angeles, California, pp. 544-548 (1996).
14. Singh, B., Datta, M., and Gulhati, S.K. \Pullout
behavior of superpile anchors in soft clay under static
loading", Marine Georesources and Geotechnology, 14,
pp. 217-236 (1996).
15. Rao, S.N., Ravi, R., and Ganapathy, C. \Behavior of
suction anchors in marine clays under TLP loading",
Proceedings of the 16th International Conference on
O shore Mechanics and Arctic Engineering, Yokohama,
Japan, 1, pp. 151-155 (1997a).
16. Rao, S.N., Ravi, R., and Ganapathy, C. \Pullout behavior
of model suction anchors in soft marine clays",
Proceedings of the Seventh International O shore and
Polar Engineering Conference, Honolulu, HI, pp. 740-
744 (1997b).
17. Kumar, N.D. and Rao, S.N. \Lateral load of cylindrical
caisson in shore protection: a laboratory study",
Coast. Eng. J., 53(4), pp. 365-395 (2011).
18. Clukey, E.C. and Morrison, M.J. \A centrifuge and
analytical study to evaluate suction caissons for TLP
applications in the Gulf of Mexico", Design and Performance
of Deep Foundations: Piles and Piers in
Soil and Soft Rock, P.P. Nelson, T.D. Smith and E.C.
Clukey, Eds., ASCE Geotechnical Special Publication,
38, pp. 141-156 (1993).
19. Chen, W. and Randolph, M.F. \Uplift capacity of suction
caissons under sustained and cyclic loading in soft
clay", Journal of Geotechnical and Geoenvironmental
Engineering, 133(11), pp. 1352-1363 (2007).
20. Hogervorst, J.R. \Field trails with large diameter
suction piles", Proceedings of the 12th annual O shore
Technology Conference, Houston, Texas, pp. 217-224
(1980).
21. Tjelta, T.I., Guttormsen, T.R., and Hermstad, J.
\Large-scale penetration test at a deepwater site",
Proceedings of the 18th Annual O shore Technology
Conference, Houston, TX, pp. 201-212 (1986).
22. Dyvik, R., Anderson, K.H., Hansen, S.B., and Christophersen,
H.P. \Field tests on anchors in clay I: description",
Journal of Geotechnical Engineering, 119(10),
pp. 1515-1531 (1993).
23. Cho, Y., Lee, T.H., Park, J.B., Kwag, D.J., Chung,
E.S., and Bang, S. \Field tests on suction pile installation
in sand", Proceedings of the 21st International
Conference on O shore Mechanics and Artic Engineering,
Oslo, Norway, pp. 765-771 (2002).
24. Azamathullah, H.Md., Deo, M.C., and Deolalikar, P.B.
\Neural networks for estimation of scour downstream
of a ski-jump bucket", J. Hydraul. Eng., 131(10), pp.
898-908 (2005).
25. Das, S.K. and Basudhar, P.K. \Undrained lateral
load capacity of piles in clay using arti cial neural
network", Computers and Geotechnics, 33(8), pp. 454-
459 (2006).
26. Rahman, M.S., Wang, J., Deng, W., and Carter, J.P.
\A neural network model for the uplift capacity of
suction caissons", Computers and Geotechnics, 28, pp.
269-287 (2001).
530 S. Bhattacharya et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 517{531
27. Pai, G.A.V. \Prediction of uplift capacity of suction
caissons using a neuro-genetic network", Engineering
with Computers, 21, pp. 129-139 (2005).
28. Goh, T.C., Kulhawy, F.H., and Chua, C.G. \Bayesian
neural network analysis of undrained side resistance of
drilled shafts", Journal of Geotechnical and Geoenvironmental
Engineering, 131(1), pp. 84-93 (2005).
29. Das, S.K. and Basudhar, P.K. \Prediction of residual
friction angle of clays using arti cial neural network",
Engineering Geology, 100(3-4), pp. 142-145 (2008).
30. Bartlett, P.L. \The sample complexity of pattern
classi cation with neural networks; the size of the
weights is more important than the size of network",
IEEE Trans. Inf. Theory, 44(2), pp. 525-536 (1998).
31. Vapnik, V.N., Statistical Learning Theory, New York,
Wiley (1998).
32. Das, S.K., Samui, P., and Sabat, A.K. \Application
of arti cial intelligence to maximum dry density and
uncon ned compressive strength of cement stabilized
soil", Geotechnical and Geological Journal, 29(3), pp.
329-342 (2011).
33. Das, S.K. and Muduli, P.K. \Evaluation of liquefaction
potential of soil using genetic programming",
Proceedings of the Golden Jubilee Indian Geotechnical
Conference, 2, Kochi, India, pp. 827-830 (2011).
34. Yang, C.X., Tham, L.G., Feng, X.T., Wang, Y.J., and
Lee, P.K. \Two-stepped evolutionary algorithm and
its application to stability analysis of slopes", Journal
of Computing in Civil Engineering ASCE, 18(2), pp.
145-153 (2004).
35. Javadi, A.A., Rezania, M., and Nezhad, M.M. \Evaluation
of liquefaction induced lateral displacements
using genetic programming", Computers and Geotechnics,
33, pp. 222-233 (2006).
36. Rezania, M. and Javadi A.A. \A new genetic programming
model for predicting settlement of shallow
foundations", Canadian Geotechnical Journal, 44, pp.
1462-1473 (2007).
37. Muduli, P.K., Das, M.R., Samui, P., and Das, S.K.
\Uplift capacity of suction caisson in clay using arti -
cial intelligence techniques", Marine Georesources and
Geotechnology, 31(4), pp. 375-390 (2013).
38. Alavi, A.H., Aminian, P., Gandomi, A.H., and Esmaeili,
M.A. \Genetic-based modeling of uplift capacity
of suction caissons", Expert Systems with Applications,
38, pp. 12608-12618 (2011).
39. Alavi, A.H., Gandomi, A.H., Mousavi, M., and Mollahasani,
A. \High-precision modeling of uplift capacity
of suction caissons using a hybrid computational
method", Geomechanics and Engineering, 2(4), pp.
253-280 (2010).
40. Gandomi, A.H., Alavi, A.H., and Yun, G.J. \Formulation
of uplift capacity of suction caissons using multiexpression
programming", KSCE Journal of Civil Engineering,
15(2), pp. 363-373 (2011).
41. Friedman, J.H. \Multivariate adaptive regression
spline", The Annals of Statistics, 19(1), pp. 1-141
(1991).
42. Samui, P., Das, S., and Kim, D. \Uplift capacity
of suction caisson in clay using multivariate adaptive
regression spline", Ocean Engineering, 38, pp. 2123-
2127 (2011).
43. Cheng, M.Y., Cao, M.T., and Tran, D.H. \A Hybrid
fuzzy inference model based on RBFNN and arti cial
bee colony for predicting the uplift capacity of suction
caissons", Automation in Construction, 41, pp. 60-69
(2014).
44. Muduli, P.K., Das, S.K., Samui, P., and Sahoo, R.
\Prediction of uplift capacity of suction caisson in
clay using extreme learning machine", Ocean Systems
Engineering, 5(1), pp. 41-54 (2015).
45. Shahr-Babak, M.M., Khanjani, M.J., and Qaderi,
K. \Uplift capacity prediction of suction caisson in
clay using a hybrid intelligence method (GMDH-HS)",
Applied Ocean Research, 59, pp. 408-416 (2016).
46. Castillo, E. and Gutierrez, J.M. \Nonlinear time series
modeling and prediction using functional networks:
Extracting information masked by chaos", Physics
Letters A, 244, pp. 71-84 (1998).
47. Castillo, E., Cobo, A., Gutierrez, J.M., and Pruneda,
E. \Working with di erential, functional and di erence
equations using functional networks", Appl. Math.
Modeling, 23, pp. 89-107 (1999).
48. Castillo, E., Cobo, A., Gutierrez, J.M., and Pruneda,
R.E., Functional Network a Neural Based Paradigm,
Springer Science + Business Media, LLC (1998).
49. Castillo, E., Gutierrez, J.M., Cobo, A., and Castillo,
C. \Some learning methods in functional networks",
Comput. Aided Civ. Infrastruct. Eng., 1, pp. 427-439
(2000b).
50. Rajasekaran, S. \Functional networks in structural engineering",
Journal of Computing in Civil Engineering,
18(2), pp. 172-181 (2000).
51. Attoh-Okine, N.O. \Modelling incremental pavement
roughness using functional network", Canadian Journal
of Civil Engineering, 32(5), pp. 899-905 (2005).
52. El-Sebakhy, A.E., Abdulraheem, A., Ahmed, M., Al-
Majed, A., Rahajia, P., Azzedin, F. and Sheltami, T.
\Functional network as a novel approach for prediction
of permeability in a carbonate reservoir", Proceedings
of the SPE Conference (2007).
53. Adeniran, A., Elshafei, M., and Hamada, G. \Functional
network softsensor for formation porosity and
water saturation in oil wells", International Instrumentation
and Measurement Technology Conference,
Singapore (2009).
S. Bhattacharya et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 517{531 531
54. Abu-Farsakh, M.Y. and Titi, H.H. \Assessment of
direct cone penetration test methods for predicting
the ultimate capacity of friction driven piles", Journal
Geotechnical and Geoenvironmental Engineering,
130(9), pp. 935-944 (2004).
55. Zhang, W., Goh, A.T.C., and Zhang, Y. \Multivariate
adaptive regression splines application for multivariate
geotechnical problems with big data", Geotech. Geol
Eng., 34, pp. 193-204 (2016)
56. Hastie, T., Tibshirani, R., and Freidman, J., The Elements
of Statistical Learning: Data Mining, Inference
and Prediction, Springer (2001).
57. Jekabsons, G., VariReg: A Software Tool for Regression
Modeling Using Various Modeling Methods,
Riga Technical University, www.cs.rtu.lv/jekabsons/
(2010).
58. Math Works Inc, Matlab User's Manual, Version 6.5,
The Math Works, Inc., Natick (2005).
59. Kennard, R.W. and Stone, L.A. \Computer aided
design of experiments", Technometrics, 11(1), pp. 137-
148 (1969).
60. Das, S.K. and Sivakugan, N. \Discussion of intelligent
computing for modeling axial capacity of pile foundations",
Canadian Geotechnical Journal, 37(8), pp. 928-
930 (2010).
61. Golbraikh, A. and Tropsha, A. \Beware of q2", Journal
of Molecular Graphics and Modelling, 20, pp. 269-276
(2002).
62. Roy, P.P. and Roy, K. \On some aspects of variable
selection for partial least squares regression models",
QSAR Comb. Sci., 27, pp. 302-313 (2008).

Volume 25, Issue 2
Transactions on Civil Engineering (A)
March and April 2018
Pages 517-531
  • Receive Date: 16 October 2015
  • Revise Date: 19 October 2016
  • Accept Date: 28 January 2017