Skip to main content
SpringerLink
Log in

A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant and complicated problem in pile analysis and design. The aim of this research is to develop a new AI predictive models for predicting pile bearing capacity. The first predictive model was developed based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) structure optimized by particle swarm optimization (PSO) algorithm called as ANFIS–GMDH–PSO model; the second model introduced as fuzzy polynomial neural network type group method of data handling (FPNN–GMDH) model. A database consists of different piles property and soil characteristics, collected from literature including CPT and pile loading test results which applied for training and testing process of developed models. Also a common artificial neural network (ANN) model was applied as a reference model for comparing and verifying among hybrid developed models for prediction. The modelling results indicated that improved ANFIS–GMDH model achieved relatively higher performance compared to ANN and FPNN–GMDH models in terms of accuracy and reliability level based on standard statistical performance indices such as coefficient of correlation (R), mean square error, root mean square error and error standard deviation values.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Mayerhof GG (1976) Bearing capacity and settlemtn of pile foundations. J Geotech Geoenvironmental Eng 102 ASCE# 11962

  2. Maizir H, Suryanita R, Jingga H (2016) Estimation of pile bearing capacity of single driven pile in sandy soil using finite element and artificial neural network methods. Int J Appl Phys Sci 2:45–50

    Google Scholar 

  3. Armaghani DJ, Bin Raja RSNS, Faizi K, Rashid ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405

    Google Scholar 

  4. Shahin MA (2013) Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions. In: Yang X, Gandomi AH, Talatahari S, Alavi AH (eds) Metaheuristics water, geotechnical and transport engineering. Elsevier Inc, London, pp 169–204

    Google Scholar 

  5. Zhang L (2004) Reliability verification using proof pile load tests. J Geotech Geoenvironmental Eng 130:1203–1213

    Google Scholar 

  6. Kondner RL (1963) Hyperbolic stress-strain response: cohesive soils. J Soil Mech Found Div 89:115–144

    Google Scholar 

  7. Kordjazi A, Nejad FP, Jaksa MB (2014) Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Comput Geotech 55:91–102

    Google Scholar 

  8. Cai G, Liu S, Tong L, Du G (2009) Assessment of direct CPT and CPTU methods for predicting the ultimate bearing capacity of single piles. Eng Geol 104:211–222

    Google Scholar 

  9. Shahin MA, Jaksa MB, Maier HR (2009) Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Adv Artif Neural Syst 2009:1–9. https://doi.org/10.1155/2009/308239

    Article  Google Scholar 

  10. Schneider JA, Xu X, Lehane BM (2008) Database assessment of CPT-based design methods for axial capacity of driven piles in siliceous sands. J Geotech Geoenviron Eng 134:1227–1244

    Google Scholar 

  11. Shahin MA (2010) Intelligent computing for modeling axial capacity of pile foundations. Can Geotech J 47:230–243

    Google Scholar 

  12. Alkroosh I, Nikraz H (2012) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25:618–627

    Google Scholar 

  13. Shahin MA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front. https://doi.org/10.1016/j.gsf.2014.10.002

    Article  Google Scholar 

  14. Maizir H, Suryanita R (2018) Evaluation of axial pile bearing capacity based on pile driving analyzer (PDA) test using Neural Network. In: IOP conference series: earth and environmental science. IOP, p 12037

  15. Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63

    Google Scholar 

  16. Lima DC de, Tumay MT (1991) Scale effects in cone penetration tests. In: Geotechnical engineering congress—1991. ASCE, pp 38–51

  17. Liu L, Moayedi H, Rashid ASA et al (2019) Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Eng Comput. https://doi.org/10.1007/s00366-019-00767-4

    Article  Google Scholar 

  18. Moayedi H, Moatamediyan A, Nguyen H et al (2019) Prediction of ultimate bearing capacity through various novel evolutionary and neural network models. Eng Comput. https://doi.org/10.1007/s00366-019-00723-2

    Article  Google Scholar 

  19. Semple RM, Rigden WJ (1984) Shaft capacity of driven pipe piles in clay. In: Analysis and design of pile foundations. ASCE, pp 59–79

  20. Randolph MF (2003) Science and empiricism in pile foundation design. Géotechnique 53:847–875

    Google Scholar 

  21. Ghorbani B, Sadrossadat E, Bazaz JB, Oskooei PR (2018) Numerical ANFIS-based formulation for prediction of the ultimate axial load bearing capacity of piles through CPT data. Geotech Geol Eng 36:2057–2076

    Google Scholar 

  22. Shaik S, Krishna KSR, Abbas M et al (2018) Applying several soft computing techniques for prediction of bearing capacity of driven piles. Eng Comput. https://doi.org/10.1007/s00366-018-0674-7

    Article  Google Scholar 

  23. Sulewska MJ (2017) Applying artificial neural networks for analysis of geotechnical problems. Comput Assist Methods Eng Sci 18:231–241

    Google Scholar 

  24. Moayedi H, Armaghani DJ (2018) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34:347–356

    Google Scholar 

  25. Nawari NO, Liang R, Nusairat J (1999) Artificial intelligence techniques for the design and analysis of deep foundations. Electron J Geotech Eng 4:1–21

    Google Scholar 

  26. Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2014.08.007

    Article  Google Scholar 

  27. Momeni E, Nazir R, Armaghani DJ, Maizir H (2015) Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sci Res J 19:85–93

    Google Scholar 

  28. Yang Y, Rosenbaum MS (2002) The artificial neural network as a tool for assessing geotechnical properties. Geotech Geol Eng 20:149–168

    Google Scholar 

  29. Asteris PG, Plevris V (2017) Anisotropic masonry failure criterion using artificial neural networks. Neural Comput Appl 28:2207–2229

    Google Scholar 

  30. Zhou J, Li E, Yang S et al (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Google Scholar 

  31. Toghroli A, Suhatril M, Ibrahim Z, Safa M, Shariati M, Shamshirband S (2018) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf 29(8):1793–1801

    Google Scholar 

  32. Chen C, Shi L, Shariati M et al (2019) Behavior of steel storage pallet racking connection—a review. 30:457–469

    Google Scholar 

  33. Koopialipoor M, Nikouei SS, Marto A et al (2018) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bull Eng Geol Environ 78:3799–3813

    Google Scholar 

  34. Koopialipoor M, Jahed Armaghani D, Hedayat A et al (2018) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput. https://doi.org/10.1007/s00500-018-3253-3

    Article  Google Scholar 

  35. Koopialipoor M, Jahed Armaghani D, Haghighi M, Ghaleini EN (2017) A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1116-2

    Article  Google Scholar 

  36. Zhao Y, Noorbakhsh A, Koopialipoor M et al (2019) A new methodology for optimization and prediction of rate of penetration during drilling operations. Eng Comput. https://doi.org/10.1007/s00366-019-00715-2

    Article  Google Scholar 

  37. Koopialipoor M, Fahimifar A, Ghaleini EN et al (2019) Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Eng Comput. https://doi.org/10.1007/s00366-019-00701-8

    Article  Google Scholar 

  38. Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput. https://doi.org/10.1007/s00366-016-0447-0

    Article  Google Scholar 

  39. Khandelwal M, Mahdiyar A, Armaghani DJ et al (2017) An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals. Environ Earth Sci 76:399. https://doi.org/10.1007/s12665-017-6726-2

    Article  Google Scholar 

  40. Asteris PG, Nikoo M (2019) Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl. https://doi.org/10.1007/s00521-018-03965-1

    Article  Google Scholar 

  41. Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M (2019) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26(13):1146–1153

    Google Scholar 

  42. Sarir P, Chen J, Asteris PG et al (2019) Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng Comput. https://doi.org/10.1007/s00366-019-00808-y

    Article  Google Scholar 

  43. Asteris PG, Tsaris AK, Cavaleri L et al (2016) Prediction of the fundamental period of infilled RC frame structures using artificial neural networks. Comput Intell Neurosci 2016:20

    Google Scholar 

  44. Plevris V, Asteris PG (2014) Modeling of masonry failure surface under biaxial compressive stress using Neural Networks. Constr Build Mater 55:447–461

    Google Scholar 

  45. Cavaleri L, Asteris PG, Psyllaki PP et al (2019) Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. Appl Sci 9:2788

    Google Scholar 

  46. Xu C, Gordan B, Koopialipoor M et al (2019) Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access 7:94692–94700

    Google Scholar 

  47. Shao Z, Armaghani DJ, Bejarbaneh BY et al (2019) Estimating the friction angle of black shale core specimens with hybrid-ANN approaches. Measurement. https://doi.org/10.1016/j.measurement.2019.06.007

    Article  Google Scholar 

  48. Khari M, Dehghanbandaki A, Motamedi S, Armaghani DJ (2019) Computational estimation of lateral pile displacement in layered sand using experimental data. Measurement 146:110–118

    Google Scholar 

  49. Mohamad ET, Li D, Murlidhar BR et al (2019) The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production. Eng Comput. https://doi.org/10.1007/s00366-019-00770-9

    Article  Google Scholar 

  50. Chen W, Sarir P, Bui X-N et al (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

    Article  Google Scholar 

  51. Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31:409–424

    Google Scholar 

  52. Armaghani DJ, Hasanipanah M, Amnieh HB, Mohamad ET (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29:457–465

    Google Scholar 

  53. Yang H, Liu J, Liu B (2018) Investigation on the cracking character of jointed rock mass beneath TBM disc cutter. Rock Mech Rock Eng 51:1263–1277

    Google Scholar 

  54. Yang H, Wang H, Zhou X (2016) Analysis on the damage behavior of mixed ground during TBM cutting process. Tunn Undergr Sp Technol 57:55–65

    Google Scholar 

  55. Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Sp Technol 81:112–120

    Google Scholar 

  56. Yang H, Koopialipoor M, Armaghani DJ et al (2019) Intelligent design of retaining wall structures under dynamic conditions. Steel Compos Struct 31:629–640

    Google Scholar 

  57. Yang HQ, Lan YF, Lu L, Zhou XP (2015) A quasi-three-dimensional spring-deformable-block model for runout analysis of rapid landslide motion. Eng Geol 185:20–32

    Google Scholar 

  58. Asteris P, Roussis P, Douvika M (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17:1344

    Google Scholar 

  59. Chen H, Asteris PG, Jahed Armaghani D et al (2019) Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models. Appl Sci 9:1042

    Google Scholar 

  60. Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50:629–644

    Google Scholar 

  61. Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30:4016003

    Google Scholar 

  62. Zhou J, Shi X, Du K et al (2016) Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. Int J Geomech 17:4016129

    Google Scholar 

  63. Shi X, Jian Z, Wu B et al (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Met Soc China 22:432–441

    Google Scholar 

  64. Kordjazi A, Pooya Nejad F, Jaksa MB (2015) Prediction of load-carrying capacity of piles using a support vector machine and improved data collection. In: Ramsay G (ed) Proceedings of the 12th Australia New Zealand conference on geomechanics: the changing face of the earth - geomechanics & human influence, pp 1–8

  65. Armaghani DJ, Faradonbeh RS, Rezaei H et al (2016) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2618-8

    Article  Google Scholar 

  66. Guo H, Zhou J, Koopialipoor M et al (2019) Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng Comput. https://doi.org/10.1007/s00366-019-00816-y

    Article  Google Scholar 

  67. Zhou J, Koopialipoor M, Murlidhar BR et al (2019) Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance. Nat Resour Res. https://doi.org/10.1007/s11053-019-09519-z

    Article  Google Scholar 

  68. Moayedi H, Raftari M, Sharifi A et al (2019) Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng Comput. https://doi.org/10.1007/s00366-018-00694-w

    Article  Google Scholar 

  69. Ebrahimian B, Movahed V (2017) Application of an evolutionary-based approach in evaluating pile bearing capacity using CPT results. Ships Offshore Struct 12:937–953

    Google Scholar 

  70. Kurnaz TF, Kaya Y (2019) A novel ensemble model based on GMDH-type neural network for the prediction of CPT-based soil liquefaction. Environ Earth Sci 78:339

    Google Scholar 

  71. Ivakhnenko AG, Ivakhnenko GA, Muller JA (1994) Self-organization of neural networks with active neurons. Pattern Recognit Image Anal 4:185–196

    Google Scholar 

  72. Lawal IA, Auta TA (2012) Applicability of GMDH-based abducitve network for predicting pile bearing capacity. In: automation. IntechOpen

  73. Najafzadeh M, Azamathulla HM (2013) Neuro-fuzzy GMDH to predict the scour pile groups due to waves. J Comput Civ Eng 29:4014068

    Google Scholar 

  74. Harandizadeh H, Toufigh MM, Toufigh V (2018) Different neural networks and modal tree method for predicting ultimate bearing capacity of piles. Iran Univ Sci Technol 8:311–328

    Google Scholar 

  75. Najafzadeh M, Tafarojnoruz A, Lim SY (2017) Prediction of local scour depth downstream of sluice gates using data-driven models. ISH J Hydraul Eng 23:195–202

    Google Scholar 

  76. Najafzadeh M, Saberi-Movahed F, Sarkamaryan S (2018) NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects. Mar Georesour Geotechnol 36:589–602

    Google Scholar 

  77. Suman S, Das SK, Mohanty R (2016) Prediction of friction capacity of driven piles in clay using artificial intelligence techniques. Int J Geotech Eng 10:469–475

    Google Scholar 

  78. Najafzadeh M, Lim SY (2015) Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci Inform 8:187–196

    Google Scholar 

  79. Najafzadeh M, Bonakdari H (2016) Application of a neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers. J Pipeline Syst Eng Pract 8:6016003

    Google Scholar 

  80. Harandizadeh H, Toufigh MM, Toufigh V (2018) Application of improved ANFIS approaches to estimate bearing capacity of piles. Soft Comput. https://doi.org/10.1007/s00500-018-3517-y

    Article  Google Scholar 

  81. Momeni E, Armaghani DJ, Fatemi SA, Nazir R (2018) Prediction of bearing capacity of thin-walled foundation: a simulation approach. Eng Comput 34:319–327

    Google Scholar 

  82. Kordnaeij A, Kalantary F, Kordtabar B, Mola-Abasi H (2015) Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties. Soils Found 55:1335–1345

    Google Scholar 

  83. Ishikawa M (1996) Structural learning with forgetting. Neural Netw 9:509–521

    Google Scholar 

  84. Ohtani T, Ichihashi H, Miyoshi T, Nagasaka K (1998) Structural learning with M-apoptosis in neurofuzzy GMDH. In: 1998 IEEE international conference on fuzzy systems proceedings. IEEE world congress on computational intelligence (Cat. No. 98CH36228). IEEE, pp 1265–1270

  85. Sharifi A, Teshnehlab M (2007) Simultaneously structural learning and training of Neurofuzzy GMDH using GA. In: 2007 Mediterranean conference on control & automation. IEEE, pp 1–5

  86. Wang B, Moayedi H, Nguyen H et al (2019) Feasibility of a novel predictive technique based on artificial neural network optimized with particle swarm optimization estimating pullout bearing capacity of helical piles. Eng Comput. https://doi.org/10.1007/s00366-019-00764-7

    Article  Google Scholar 

  87. Ghomsheh VS, Shoorehdeli MA, Teshnehlab M (2007) Training ANFIS structure with modified PSO algorithm. In: 2007 Mediterranean conference on control & automation. IEEE, pp 1–6

  88. Abraham A (2001) Neuro fuzzy systems: State-of-the-art modeling techniques. In: International work-conference on artificial neural networks. Springer, pp 269–276

  89. Akcayol MA (2004) Application of adaptive neuro-fuzzy controller for SRM. Adv Eng Softw 35:129–137

    Google Scholar 

  90. Davis L (ed) (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  91. Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, pp 760–766

  92. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179:2232–2248

    MATH  Google Scholar 

  93. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    MathSciNet  MATH  Google Scholar 

  94. Tonnizam Mohamad E, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of Artificial Neural Networks. Int Rev Model Simul 5:2501–2506

    Google Scholar 

  95. Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 63:29–43. https://doi.org/10.1016/j.tust.2016.12.009

    Article  Google Scholar 

  96. Jahed Armaghani D, Hasanipanah M, Mahdiyar A et al (2016) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2598-8

    Article  Google Scholar 

  97. Khandelwal M, Armaghani DJ (2016) Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech Geol Eng 34:605–620. https://doi.org/10.1007/s10706-015-9970-9

    Article  Google Scholar 

  98. Jahed Armaghani D, Hajihassani M, Marto A et al (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess. https://doi.org/10.1007/s10661-015-4895-6

    Article  Google Scholar 

  99. Jahed Armaghani D, Hajihassani M, Monjezi M et al (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci. https://doi.org/10.1007/s12517-015-1908-2

    Article  Google Scholar 

  100. Jahed Armaghani D, Mohd Amin MF, Yagiz S et al (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci. https://doi.org/10.1016/j.ijrmms.2016.03.018

    Article  Google Scholar 

  101. Mohamad ET, Faradonbeh RS, Armaghani DJ et al (2017) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28:393–406

    Google Scholar 

  102. Davisson MT (1972) High capacity piles. In: Proceedings of the lecture series on Innovation in Foundation Construction, ASCE, NY, pp 81–112

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, P.O. Box 76169133, Kerman, Iran

    Hooman Harandizadeh

  2. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Danial Jahed Armaghani

  3. Department of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran

    Mahdy Khari

Authors
  1. Hooman Harandizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahdy Khari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danial Jahed Armaghani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harandizadeh, H., Jahed Armaghani, D. & Khari, M. A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets. Engineering with Computers 37, 685–700 (2021). https://doi.org/10.1007/s00366-019-00849-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00849-3

Keywords

Search

Navigation