Abstract
In this study, a neural-fuzzy (NF) system is combined with group method of data handling (GMDH) in order to estimate the axial bearing capacity of driven piles. To reach optimum design of this conjunction (NF-GMDH) network, the metaheuristic techniques including particle swarm optimization (PSO) and gravitational search algorithm (GSA) were utilized. The datasets used for estimating pile bearing capacity were collected from the literature review. The parameters influencing the modeling and pile capacity analysis were taken into account as Flap number, surrounding soil properties, the pile geometric characteristics, and internal friction angles of the pile–soil interface. The efficiency of hybrid NF-GMDH networks in train and test phases was examined. Applying the PSO algorithm to the hybrid NF-GMDH model structure improved the model performance and achieved a higher level of accuracy in predicting the ultimate pile bearing capacity (RMSE = 1375 and SI = 0.255) compared to NF-GMDH model developed by GSA (RMSE = 1740.7 and SI = 0.357). In addition, based on achieved results, the developed NF-GMDH networks showed relatively better performances in comparison with gene programming and linear regression model methods considered in this study.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alkroosh I, Nikraz H (2012) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25(3):618–627. https://doi.org/10.1016/j.engappai.2011.08.009
Ardalan H, Eslami A, Nariman-Zadeh N (2009) Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Comput Geotech 36(4):616–625. https://doi.org/10.1016/j.compgeo.2008.09.003
Armaghani DJ, Faradonbeh RS, Rezaei H, Rashid ASA, Amnieh HB (2018) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl 29(11):1115–1125. https://doi.org/10.1007/s00521-016-2618-8
Baziar MH, Azizkandi AS, Kashkooli A (2015) Prediction of pile settlement based on cone penetration test results: an ANN approach. KSCE J Civ Eng 19(1):98–106. https://doi.org/10.1007/s12205-012-0628-3
Chan W, Chow Y, Liu L (1995) Neural network: an alternative to pile driving formulas. Comput Geotech 17(2):135–156. https://doi.org/10.1016/0266-352X(95)93866-H
Chen W, Sarir P, Bui XN, Nguyen H, Tahir MM, Armaghani DJ (2019) Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Eng Comput. https://doi.org/10.1007/s00366-019-00752-x
Fatehnia M, Kamal T, Nader H, Erman OE (2015) New method for predicting the ultimate bearing capacity of driven piles by using Flap number. KSCE J Civ Eng 19(3):611–620. https://doi.org/10.1007/s12205-013-0315-z
Guven A, Azamathulla HMd, Zakaria NA (2009) Linear genetic programming for prediction of circular pile scour. Ocean Eng 36(12–13):985–991
Guven A, Azamathulla HMd, Gunal M (2012) Comparative study of predicting scour around a circular pile, ICE. Maritime Eng 165(1):31–40
Hwang HS (2006) Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication. Comput Ind Eng 50(4):450–457. https://doi.org/10.1016/j.cie.2005.08.005
Khari M, Dehghanbanadaki A, Motamedi S, Armaghani DJ (2019a) Computational estimation of lateral pile displacement in layered sand using experimental data. Measurement 146:110–118. https://doi.org/10.1016/j.measurement.2019.04.081
Khari M, Armaghani DJ, Dehghanbanadaki A (2019b) Prediction of lateral deflection of small-scale piles using hybrid PSO–ANN model. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-04134-9
Kiefa MA (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng 124(12):1177–1185. https://doi.org/10.1061/(ASCE)1090-0241(1998)124:12(1177)
Kim BI, Lee SH (2005) Comparison of bearing capacity characteristics of sand and gravel compaction pile treated ground. KSCE J Civ Eng 9(3):197–203. https://doi.org/10.1007/BF02829050
Lee I-M, Lee J-H (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189–200. https://doi.org/10.1016/0266-352x(95)00027-8
Maizir H, Kassim KA (2013) Neural network application in prediction of axial bearing capacity of driven piles. In: Proceedings of the international multiconference of engineers and computer scientists, Vol I, IMECS 2013, March 13–15, 2013, Hong Kong
Mission JLC, Kim HJ, Park IS (2007) Analysis of static axial load capacity of single piles and large diameter shafts using nonlinear load transfer curves. KSCE J Civ Eng 11(6):285
Momeni E, Armaghani DJ, Fatemi SA, Nazir R (2018) Prediction of bearing capacity of thin-walled foundation: a simulation approach. Eng Comput 34(2):319–327. https://doi.org/10.1007/s00366-017-0542-x
Nagasaka K, Ichihashi H, Leonard R (1995) Neuro-fuzzy GMDH and its application to modelling grinding characteristics. Int J Prod Res 33(5):1229–1240. https://doi.org/10.1080/00207549508930206
Najafzadeh M (2015) Neuro-fuzzy GMDH based evolutionary algorithms to predict the scour pile under clear-water condition. Ocean Eng 99:85–94
Najafzadeh M, Azamathulla HMd (2013) Group method of data handling to predict scour depth around bridge piers. Neural Comput Appl 23(7–8):2107–2112
Najafzadeh M, Azamathulla HM (2015) Neuro-fuzzy GMDH to predict the scour pile groups due to waves. ASCE J Comput Civ Eng 29(5):04014068
Najafzadeh M, Barani G-A (2011) Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Scientia Iranica 18(6):1207–1213. https://doi.org/10.1016/j.scient.2011.11.017
Najafzadeh M, Saberi-Movahed F (2018) GMDH-GEP to predict free span expansion rates below pipelines under waves. Mar Georesour Geotechnol 37(3):375–392
Najafzadeh M, Barani G, Azamathulla HM (2013a) GMDH to predict scour depth around a pier in cohesive soils. Appl Ocean Res 40:35–41. https://doi.org/10.1016/j.apor.2012.12.004
Najafzadeh M, Barani G, Hessami-Kermani MR (2013b) Abutment scour in live-bed and clear-water using GMDH Network. Water Sci Technol 67(5):1121–1128
Najafzadeh M, Barani G, Hessami Kermani MR (2013c) GMDH network based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106
Najafzadeh M, Barani G, Hessami-Kermani MR (2013d) Group method of data handling to predict scour depth around vertical piles under regular waves. Scientia Iranica 30(3):406–413
Najafzadeh M, Barani GA, Hessami-Kermani MR (2015) Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds. Ocean Eng 104:387–396
Najafzadeh M, Rezaie-Balf M, Rashedi E (2016) Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models. J Hyroinform 18(5):867–884
Najafzadeh M, Saberi-Movahed F, Sarkamaryan S (2018) NF-GMDH systems based evolutionary algorithms for evaluation of bridge pier local scour depth with debris effects. Mar Georesour Geotechnol 36(5):589–602
Nawari N, Liang R, Nusairat J (1999) Artificial intelligence techniques for the design and analysis of deep foundations. Electron J Geotech Eng 4:1–21
Pal M (2011) Modelling pile capacity using generalised regression neural network. In: Proceedings of Indian geotechnical conference December 15-17, 2011, Kochi (Paper No. N-027)
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2011) Filter modeling using gravitational search algorithm. Eng Appl Artif Intell 24(1):117–122. https://doi.org/10.1016/j.engappai.2010.05.007
Shahin MA (2010) Intelligent computing for modeling axial capacity of pile foundations. Can Geotech J 47(2):230–243. https://doi.org/10.1139/T09-094
Shahin MA (2014) Load–settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks. Soils Found 54(3):515–522. https://doi.org/10.1016/j.sandf.2014.04.015
Shahin MA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7(1):33–44. https://doi.org/10.1016/j.gsf.2014.10.002
Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust. Geomech. 36(1):49–62
Shahin MA, Jaksa MB, Maier HR (2008) State of the art of artificial neural networks in geotechnical engineering. Electron J Geotech Eng 8(1):1–26
Tarawneh B, Imam R (2014) Regression versus artificial neural networks: predicting pile setup from empirical data. KSCE J Civ Eng 18(4):1018–1027. https://doi.org/10.1007/s12205-014-0072-7
Wardani S, Surjandari N, Jajaputra A (2013) Analysis of ultimate bearing capacity of single pile using the artificial neural networks approach: a case study. In: Proceedings of the 18th international conference on soil mechanics and geotechnical engineering, Paris, France
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Harandizadeh, H., Toufigh, V. Application of Developed New Artificial Intelligence Approaches in Civil Engineering for Ultimate Pile Bearing Capacity Prediction in Soil Based on Experimental Datasets. Iran J Sci Technol Trans Civ Eng 44 (Suppl 1), 545–559 (2020). https://doi.org/10.1007/s40996-019-00332-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40996-019-00332-5