Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree

Abstract

M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length (L), angle of oblique load (α), sand density (ρ), number of batter piles (B), and number of vertical piles (V) as input and oblique load (Q) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load (α) and number of batter pile (B) affect the oblique load capacity for both smooth and rough pile groups.

Keywords

batter piles / oblique load test / neural network / M5 model tree / random forest regression / ANOVA

References

[1] Tschebotarioff G P. The resistance to lateral loading of single piles and pile groups. Special Publication, 1953 , 154: 38–48

[2] Murthy V N. Behaviour of battered piles embedded in sand subjected to lateral loads. In: Proceedings of the Symposium on Bearing Capacity of Piles. Roorkee, 1964,142–153

[3] Prakash S, Subramanyam G. Behaviour of battered piles under lateral loads. Journal Indian National Society of Soil Mechanics and Foundation Engineering, 1965, 4(2): 177–196

[4] Ranjan G, Ramasamy G, Tyagi R P. Lateral response of batter piles and pile bents in clay. Indian Geotechnical Journal, 1980, 10(2): 135–142

[5] Lu S S. Design load of bored pile laterally loaded. In: Proceedings of the l0th International Conference on Soil Mechanics and Foundation Engineering. 1981, 767–777

[6] Veeresh C. Behaviour of batter anchor piles in marine clay subjected to vertical pull out. In: Proceedings of the Sixth International Offshore and Polar Engineering Conference. 1996

[7] Meyerhof G G. The uplift capacity of foundations under oblique loads. Canadian Geotechnical Journal, 1973, 10(1): 64–70

[8] Awad A, Ayoub A. Ultimate uplift capacity of vertical and inclined piles in cohesionless soil. In: Proceedings of the 5th International Conference on Soil Mechanics and Foundation Engineering. Budapest, 1976, 221–227

[9] Meyerhof G G. Uplift resistance of inclined anchors and piles. International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts, 1975, 12(7): 97

[10] Chattopadhyay B C, Pise P J. Axial uplift capacity of inclined piles. Indian Geotechnical Journal, 1986, 16(3): 197–214.

[11] Bose K K, Krishnan A. Pullout capacity of model piles in sand . Indian Geotechnical Society Chennai Chapter, 2009, 49–54

[12] Nazir A, Nasr A. Pullout capacity of batter pile in sand. Journal of Advanced Research, 2013, 4(2): 147–154

[13] Sharma B, Zaheer S, Hussain Z. Experimental model for studying the performance of vertical and batter micropiles. In: Proceedings of Geo-Congress. Atlanta, 2014, 4252–4264

[14] Teng W C, Flucker R L, Graham J S. Design of steel pile foundations for transmission owers. IEEE Transactions on Power Apparatus and Systems, 1970, PAS-89(3): 399–411

[15] Al-Shakarchi Y J, Fattah M Y, Kashat I K. The behaviour of batter piles under uplift loads. In: Proceedings of International Conference on Geotechnical Engineering. 2004, 105–114

[16] Mroueh H, Shahrour I. Numerical analysis of the response of battered piles to inclined pullout loads. International Journal for Numerical and Analytical Methods in Geomechanics, 2009, 33(10): 1277–1288

[17] Bhardwaj S, Singh S K. Ultimate capacity of battered micropiles under oblique pullout loads. International Journal of Geotechnical Engineering, 2015, 9(2): 190–200

[18] Rajashree S S. Nonlinear cyclic analysis of laterally loaded pile in clay. Dissertation for the Doctoral Degree. Madras: Indian Institute of Technology, 1997

[19] Sabry M. Shaft resistance of a single vertical or batter pile in sand subjected to axial compression or uplift loading. Thesis for the Master’s Degree. Montreal: Concordia University, 2011

[20] Hanna A, Sabry M. Trends in pullout behavior of batter piles in sand. In: Proceedings of the 82nd Annual Meeting of the Transportation Research Board. 2003

[21] Giannakou A, Gerolymos N, Gazetas G, Tazoh T, Anastasopoulos I. Seismic behavior of batter piles: Elastic response. Journal of Geotechnical and Geoenvironmental Engineering, 2010, 136(9): 1187–1199

[22] Johnson K. Load-deformation behaviour of foundations under vertical and oblique loads. Dissertation for the Doctoral Degree. Townsville: James Cook University, 2005

[23] Ramadan M I, Butt S D, Popescu R. Finite element modeling of offshore anchor piles under mooring forces. In: Proceedings of 62nd Canadian Geotechnical Society Conference. Geo Halifax, 2009, (9): 785–792

[24] Achmus M, Thieken K. On the behavior of piles in non-cohesive soil under combined horizontal and vertical loading. Acta Geotechnica, 2010, 5(3): 199–210

[25] Trochanis A M, Bielak J, Christiano P. Three-dimensional nonlinear study of piles. Journal of Geotechnical Engineering, 1991, 117(3): 429–447

[26] Rajashree S S, Sundaravadivelu R. Degradation model for one-way cyclic lateral load on piles in soft clay. Computers and Geotechnics, 1996, 19(4): 289–300

[27] Rajashree S S, Sitharam T G. Nonlinear cyclic load analysis for lateral response of batter piles in soft clay with a rigorous degradation model. In: Proceedings of International Conference on Offshore and Nearshore Geotechnical Engineering. New Delhi, 2000

[28] Chan W T, Chow Y K, Liu L F. Neural network: An alternative to pile driving formulas. Computers and Geotechnics, 1995, 17(2): 135–156

[29] Chow Y K, Chan W T, Liu L F, Lee S L. Prediction of pile capacity from stress-wave measurements: A neural network approach. International Journal for Numerical and Analytical Methods in Geomechanics, 1995, 19(2): 107–126

[30] Goh A T. Pile driving records reanalyzed using neural networks. Journal of Geotechnical Engineering, 1996, 122(6): 492–495

[31] Teh C I, Wong K S, Goh A T, Jaritngam S. Prediction of pile capacity using neural networks. Journal of Computing in Civil Engineering, 1997, 11(2): 129–138

[32] Lok T M H, Che W F. Axial capacity prediction for driven piles using ANN: Model comparison. In: Proceedings of Geotechnical Engineering for Transportation Projects. Los Angeles, 2004, 697–704

[33] Etemad-Shahidi A, Ghaemi N. Model tree approach for prediction of pile groups scour due to waves. Ocean Engineering, 2011, 38(13): 1522–1527

[34] Pal M, Singh N K, Tiwari N K. Pier scour modelling using random forest regression. ISH Journal of Hydraulic Engineering, 2013, 19(2): 69–75

[35] Singh G, Sachdeva S N, Pal M. M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India. Accident Analysis and Prevention, 2016, 96: 108–117

[36] Bhattacharya B, Solomatine D P. Neural networks and M5 model trees in modelling water level-discharge relationship. Neurocomputing, 2005, 63: 381–396

[37] Solomatine D P, Xue Y. M5 model trees and neural networks: Application to flood forecasting in the upper reach of the Huai River in China. Journal of Hydrologic Engineering, 2004, 9(6): 491–501

[38] Leshem G, Ritov Y. Traffic flow prediction using AdaBoost algorithm with random forests as a weak learner. International Journal of Intelligent Technology, 2007, 19: 193–198

[39] Hamdia K M, Msekh M A, Silani M, Vu-Bac N, Zhuang X, Nguyen-Thoi T, Rabczuk T. Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling. Composite Structures, 2015, 133: 1177–1190

[40] Badawy M F, Msekh M A, Hamdia K M, Steiner M K, Lahmer T, Rabczuk T. Hybrid nonlinear surrogate models for fracture behavior of polymeric nanocomposites. Probabilistic Engineering Mechanics, 2017, 50: 64–75

[41] Hamdia K M, Silani M, Zhuang X, He P, Rabczuk T. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215–227

[42] 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

[43] 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

[44] 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

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

[46] Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464

[47] Quinlan J R. Learning with continuous classes. In: Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. 1992, 92: 343–348

[48] Breiman L. Bagging predictors. Machine Learning, 1996, 24(2): 123–140

[49] Breiman L, Friedman J H, Olshen R A, Stone C J. Classification and Regression Trees. Monterey: Wadsworth & Brooks/Cole Advanced Books & Software, 1984

[50] Ismail A, Jeng D S, Zhang L L. An optimised product-unit neural network with a novel PSO-BP hybrid training algorithm: Applications to load-deformation analysis of axially loaded piles. Engineering Applications of Artificial Intelligence, 2013, 26(10): 2305–2314

[51] Shahin M A. Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks. Soil and Foundation, 2014, 54(3): 515–522

[52] Goh A T, Zhang W G. An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines. Engineering Geology, 2014, 170: 1–10 doi:10.1016/j.enggeo.2013.12.003

[53] Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed. Upper Saddle River: Prentice-Hall, 1999

[54] Zikmund W G, Babin B J, Carr J C, Griffin M. Business Research Methods. Boston: Cengage Learning , 2013

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature