Skip to main content
SpringerLink
Log in

Application of improved ANFIS approaches to estimate bearing capacity of piles

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

An accurate estimation of deep foundation bearing capacity in different types of soils with the aid of the field experiments results is taken into account as the most important problems in geotechnical engineering. In recent decade, applying a broad range of artificial intelligence (AI) models has become widespread to solve various types of complicated problems in geotechnics. In this way, this study presents an application of two improved adaptive neuro-fuzzy inference system (ANFIS) techniques to estimate ultimate piles bearing capacity on the basis of cone penetration test (CPT) results basically used in analysis of pile foundations. The first model was combination of ANFIS and group method of data handling (GMDH) and the second one was related to the integration of fuzzy polynomial (FP) and GMDH model. Furthermore, in the ANFIS–GMDH, constant coefficients of ANFIS model were optimized using gravitational search algorithm (GSA). To improve the proposed AI models for carrying out training and testing stages, a reliable database in form of four input variables included information about different properties of soils and driven piles obtained from CPTs results. Performance of the proposed approaches indicated that FP–GMDH had better performance (RMSE = 0.0647 and SI = 0.378) in comparison with ANFIS–GMDH–GSA (RMSE = 0.084 and SI = 0.412). The use of multiple linear regression and multiple nonlinear regression equations showed lower level of precision in prediction of axial-bearing capacity of driven piles compared to the ANFIS–GMDH–GSA and FP–GMDH techniques.

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

Similar content being viewed by others

References

  • Abu-Kiefa M (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng 124(12):1177–1185

    Article  Google Scholar 

  • Ahangar-Asr A, Javadi AA, Khalili N (2014) A new approach to thermo-mechanical modelling of the behaviour of unsaturated soils. Int J Numer Anal Methods Geomech 39:539–557

    Article  Google Scholar 

  • Alavi AH, Gandomi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1):171–187

    Article  Google Scholar 

  • Alavi AH, Ameri M, Gandomi AH, Mirzahosseini MR (2011) Formulation of flow number of asphalt mixes using a hybrid computational method. Constr Build Mater 25(3):1338–1355

    Article  Google Scholar 

  • Alkroosh IS, Nikraz H (2011a) Correlation of pile axial capacity and CPT data using gene expression programming. Geotech Geol Eng 29(5):725–748

    Article  Google Scholar 

  • Alkroosh IS, Nikraz H (2011b) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25(3):618–627

    Article  Google Scholar 

  • Alkroosh IS, Nikraz H (2012) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25(3):618–627

    Article  Google Scholar 

  • Alkroosh IS, Nikraz I (2013) Evaluation of pile lateral capacity in clay applying evolutionary approach. Int J Geomath 4(1):462–465

    Google Scholar 

  • Alkroosh I, Nikraz H (2014) Predicting pile dynamic capacity via application of an evolutionary algorithm. Soils Found 54(2):233–242

    Article  Google Scholar 

  • Alkroosh IS, Bahadori M, Nikraz H, Bahadori A (2015) Regressive approach for predicting bearing capacity of bored piles from cone penetration test data. J Rock Mech Geotech Eng 7(5):584–592

    Article  Google Scholar 

  • Amanifard N, Nariman-Zadeh N, Farahani MH, Khalkhali A (2008) Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks. J Energy Convers Manag 49(10):2588–2594

    Article  Google Scholar 

  • Armaghani DJ, Raja RSNSB, 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(2):391–405

    Article  Google Scholar 

  • Bezdek JC (1973) Cluster validity with fuzzy sets. J Cybern 3:58–73. https://doi.org/10.1080/01969727308546047

    Article  MathSciNet  MATH  Google Scholar 

  • Cevik A (2007) Unified formulation for web crippling strength of cold-formed steel sheeting using stepwise regression. J Constr Steel Res 63(10):1305–1316

    Article  Google Scholar 

  • Cevik A (2011) Modeling strength enhancement of FRP confined concrete cylinders using soft computing. Expert Syst Appl 38(5):5662–5673

    Article  Google Scholar 

  • Ebrahimian B, Movahed V (2017) Application of an evolutionary-based approach in evaluating pile bearing capacity using CPT results. Ships Offshore Struct 12(7):937–953

    Article  Google Scholar 

  • Farlow SJ (ed) (1984) Self-organizing method in modelling: GMDH type algorithm. Marcel Dekker Inc, New York

    MATH  Google Scholar 

  • Fatehnia M, Tawfiq K, Hataf N, Ozguven EE (2015) New method for predicting the ultimate bearing capacity of driven piles by using Flap number. KSCE J Civil Eng 19(3):611–620

    Article  Google Scholar 

  • Gandomi AH, Alavi AH, Sahab MG (2010) New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming. Mater Struct 43(7):963–983

    Article  Google Scholar 

  • Iba H, deGaris H (1996) Extending genetic programming with recombinative guidance. In: Angeline P, Kinnear K (eds) Advances in genetic programming, vol 2. MIT Press, Cambridge

    Google Scholar 

  • Ivahnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 1(4):364–378

    Article  MathSciNet  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  • Józefiak K, Zbiciak A, Maślakowski M, Piotrowski T (2015) Numerical modelling and bearing capacity analysis of pile foundation. Procedia Eng 111:356–363

    Article  Google Scholar 

  • Kalantary F, Ardalan H, Nariman-Zadeh N (2009) An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol 104(1–2):144–155

    Article  Google Scholar 

  • Khandelwal M, Marto A, Fatemi SA, Ghoroqi M, Armaghani DJ, Singh TN, Tabrizi O (2018) Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng Comput 34(2):307–317

    Article  Google Scholar 

  • Kohestani VR, Vosoughi M, Hassanlourad M, Fallahnia M (2017) Bearing capacity of shallow foundations on cohesionless soils: a random forest based approach. Civil Eng Infrastruct J 50(1):35–49

    Google Scholar 

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

    Article  Google Scholar 

  • Lee IM, Lee JH (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189–200

    Article  Google Scholar 

  • Long JH, Wysockey MH (1999) Accuracy of methods for predicting axial capacity of deep foundations. In: Proceedings of OTRC 99 conference: analysis, design, construction, and testing of deep foundation ASCE, Austin, TX, 29–30 April, GSP 88, pp 190–195

  • Maizir H (2017) Evaluation of shaft bearing capacity of single driven pile using neural network. In: Proceedings of the international multiconference of engineers and computer scientists, vol I, IMECS, March 15–17, Hong Kong

  • McLachlan GJ, Do K-A, Ambroise C (2004) Analyzing microarray gene expression data. Wiley, Hoboken

    Book  MATH  Google Scholar 

  • Mehrara M, Moeini A, Ahrari M, Erfanifard A (2009) Investigating the efficiency in oil futures market based on GMDH approach. Expert Syst Appl 36(4):7479–7483

    Article  Google Scholar 

  • Moayedi H, Armaghani DJ (2018) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34(2):347–356

    Article  Google Scholar 

  • Mohanty R, Suman S, Das SK (2018) Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques. Int J Geotech Eng 12(2):209–216

    Article  Google Scholar 

  • Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

    Article  Google Scholar 

  • Najafzadeh M, Barani GA (2011) Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge pier. Sci Iran Trans A 18(6):1207–1213

    Article  Google Scholar 

  • Najafzadeh M, Saberi-Movahed F (2018) GMDH-GEP to predict free span expansion rates below pipelines under waves. Mar Georesour Geotechnol. https://doi.org/10.1080/1064119X.2018.1443355

    Article  Google Scholar 

  • Najafzadeh M, Tafarojnoruz A (2016) Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers. Environ Earth Sci 75(2):157

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Hessami-Kermani MR (2013a) Abutment scour in live-bed and clear-water using GMDH network. Water Sci Technol IWA 67(5):1121–1128

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Azamathulla HMd (2013b) GMDH to predict scour depth around vertical piers in cohesive soils. Appl Ocean Res 40:35–41

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Hessami Kermani MR (2013c) GMDH network based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Hessami-Kermani MR (2013d) Group method of data handling to predict scour depth around vertical piles under regular waves. Sci Iran 30(3):406–413

    Google Scholar 

  • Najafzadeh M, Barani GA, Hessami-Kermani M-R (2013e) Group method of data handling to predict scour depth around vertical piles under regular waves. Sci Iran 20(3):406–413

    Google Scholar 

  • Najafzadeh M, Barani GA, Hessami-Kermani MR (2014a) GMDH networks to predict scour at downstream of a ski-jump bucket. Earth Sci Inf 7(4):231–248

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Azamathulla HMd (2014b) Prediction of pipeline scour depth in clear-water and live-bed conditions using GMDH. Neural Compu Appl 24(3–4):629–635

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Hessami-Kermani MR (2014c) Estimation of pipeline scour due to waves by the group method of data handling. J Pipeline Syst Eng Pract ASCE 5(3):06014002

    Article  Google Scholar 

  • Najafzadeh M, Rezaie-Balf M, Rashedi E (2016a) Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models. J Hydroinf 18(5):867–884

    Article  Google Scholar 

  • Najafzadeh M, Etemad-Shahidi A, Lim SY (2016b) Scour prediction in long contractions using ANFIS and SVM. Ocean Eng 111:128–135

    Article  Google Scholar 

  • Najafzadeh M, Saberi-Movahed F, Sarkamaryan S (2017) NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects. Mar Georesour Geotechnol. https://doi.org/10.1080/1064119x.2017.1355944

    Article  Google Scholar 

  • Nariman-Zadeh N, Darvizeh A, Ahmad-Zadeh GR (2003) Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc Inst Mech Eng Part B J Eng Manuf 217(6):779–790

    Article  MATH  Google Scholar 

  • Oh S, Pedrycz W (2006) The design of self-organizing neural networks based on PNs and FPNs with the aid of genetic optimization and extended GMDH method. Int J Approx Reason 43:26–58

    Article  MATH  Google Scholar 

  • Onwubolu GC (2008) Design of hybrid differential evolution and group method in data handling networks for modeling and prediction. Inf Sci 178:3618–3634

    Article  Google Scholar 

  • Qin Y, Langari R, Gu L (2015) A new modeling algorithm based on ANFIS and GMDH. J Intell Fuzzy Syst 29(4):1321–1329

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745

    Article  MathSciNet  MATH  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2011) Filter modeling using gravitational search algorithm. Eng Appl Artif Intell 24(1):117–122

    Article  MATH  Google Scholar 

  • Sakaguchi A, Yamamoto T (2000) A GMDH network using back propagation and its application to a controller design. J IEEE 4:2691–2697

    Google Scholar 

  • Samui P, Shahin M (2014) Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation. J Numer Methods Civil Eng 1(1):37–45

    Google Scholar 

  • Sanchez E, Shibata T, Zadeh LA (1997) Genetic algorithms and fuzzy logic systems. World Scientific, Singapore

    Book  MATH  Google Scholar 

  • Shaghaghi S, Bonakdari H, Gholami A, Ebtehaj I, Zeinolabedini M (2017) Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design. Appl Math Comput 313:271–286

    MATH  Google Scholar 

  • Shahin MA (2010) Intelligent computing for modeling axial capacity of pile foundations. Can Geotech J 47(2):230–243

    Article  Google Scholar 

  • Shahin MA (2015) Use of evolutionary computing for modelling some complex problems in geotechnical engineering. Geomech Geoeng 10(2):109–125

    Article  Google Scholar 

  • Srinivasan D (2008) Energy demand prediction using GMDH networks. Neuro Comput 72(1–3):625–629

    Google Scholar 

  • Taherkhani A, Basti A, Nariman-Zadeh N, Jamali A (2018) Achieving maximum dimensional accuracy and surface quality at the shortest possible time in single-point incremental forming via multi-objective optimization. Proc Inst Mech Eng Part B J Eng Manuf. https://doi.org/10.1177/0954405418755822

    Article  Google Scholar 

  • Tanyildizi H, Cevik A (2010) Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming. Constr Build Mater 24(12):2612–2618

    Article  Google Scholar 

  • Xie Y, Liu C, Gao S, Tang J, Chen Y (2017) Lateral load bearing capacity of offshore high-piled wharf with batter piles. Ocean Eng 142:377–387

    Article  Google Scholar 

  • Yaseen ZM, Ramal MM, Diop L, Jaafar O, Demir V, Kisi O (2018) Hybrid adaptive neuro-fuzzy models for water quality index estimation. Water Resour Manag. https://doi.org/10.1007/s11269-018-1915-7

    Article  Google Scholar 

  • Zahiri A, Najafzadeh M (2018) Optimized expressions to evaluate the flow discharge in main channels and floodplains using evolutionary computing and model classification. Int J River Basin Manag 16(1):123–132

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, 22 Bahman Blvd., P.O. Box 76175-133, Kerman, Iran

    Hooman Harandizadeh & Mohammad Mohsen Toufigh

  2. Department of Earthquake and Geotechnics, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, P.O. Box 76315117, Kerman, Iran

    Vahid Toufigh

Authors
  1. Hooman Harandizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Mohsen Toufigh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vahid Toufigh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hooman Harandizadeh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harandizadeh, H., Toufigh, M.M. & Toufigh, V. Application of improved ANFIS approaches to estimate bearing capacity of piles. Soft Comput 23, 9537–9549 (2019). https://doi.org/10.1007/s00500-018-3517-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3517-y

Keywords

Search

Navigation