Advertisement

Optimization of ANFIS with GA and PSO estimating α ratio in driven piles

  • Hossein MoayediEmail author
  • Mehdi Raftari
  • Abolhasan Sharifi
  • Wan Amizah Wan Jusoh
  • Ahmad Safuan A. Rashid
Original Article
  • 30 Downloads

Abstract

This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full-scale results of the driven piles. The input parameters used in this study were pile diameter (m), pile length (m), relative density (Id), embedment ratio (L/D), both of the pile end resistance (qc) and base resistance at relatively 10% base settlement (qb0.1) from CPT result, whereas the output was α. A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real-monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R2, RMSE, and VAF. According to R2, RMSE, and VAF, values of (0.9439, 0.0123 and 99.91), (0.9872, 0.0117 and 99.99), and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA–ANFIS, and PSO–ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA–ANFIS model in estimating α ratio in driven shafts.

Keywords

Hybrid GA–ANFIS PSO–ANFIS ANFIS Friction capacity 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest in presenting this manuscript.

References

  1. 1.
    Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32:441–455CrossRefGoogle Scholar
  2. 2.
    Moayedi H, Armaghani DJ (2017) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34:1–10Google Scholar
  3. 3.
    Asadi A, Moayedi H, Huat BB, Parsaie A, Taha MR (2011) Artificial neural networks approach for electrochemical resistivity of highly organic soil. Int J Electrochem Sci 6:1135–1145Google Scholar
  4. 4.
    Moayedi H, Huat BB, Kazemian S, Daneshm S (2012) Stabilization of organic soil using sodium silicate system grout. Int J Phys Sci 7:1395–1402Google Scholar
  5. 5.
    Moayedi H, Huat BB, Mokhberi M, Moghaddam AA, Moghaddam SA (2010) Using stone column as a suitable liquefaction remediation in Persian Gulf coast. Electron J Geotech Eng 15:1757–1767Google Scholar
  6. 6.
    Moayedi H, Rezaei A (2017) An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl 31:1–10.  https://doi.org/10.1007/s00521-017-2990-z Google Scholar
  7. 7.
    Moayedi H, Hayati S (2018) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3555-5 Google Scholar
  8. 8.
    Moayedi H, Nazir R, Mosallanezhad M, Noor RBM, Khalilpour M (2018) Lateral deflection of piles in a multilayer soil medium. Case study: the Terengganu seaside platform. Measurement 123:185–192CrossRefGoogle Scholar
  9. 9.
    Mohamad ET, Armaghani DJ, Mahdyar A, Komoo I, Kassim KA, Abdullah A, Majid MZA (2017) Utilizing regression models to find functions for determining ripping production based on laboratory tests. Meas J Int Meas Confed 111:216–225Google Scholar
  10. 10.
    American Petroleum Institute (1993) Recommended practice for planning, designing and constructing fixed offshore platforms—working stress design. American Petroleum Institute, Washington D.C.Google Scholar
  11. 11.
    Nazir R, Moayedi H, Pratikso A, Mosallanezhad M (2014) The uplift load capacity of an enlarged base pier embedded in dry sand. Arab J Geosci 8:1–12Google Scholar
  12. 12.
    Tahmasebi P, Hezarkhani A (2012) A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput Geosci 42:18–27CrossRefGoogle Scholar
  13. 13.
    Samui P, Kim D (2013) Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles. Neural Comput Appl 23:1123–1127CrossRefGoogle Scholar
  14. 14.
    Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10:10CrossRefGoogle Scholar
  15. 15.
    Moayedi H, Hayati S (2018) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput 66:208–219CrossRefGoogle Scholar
  16. 16.
    Moayedi H, Mosallanezhad M, Mehrabi M, Safuan ARA, Biswajeet P (2018) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Engineering with Computers.  https://doi.org/10.1007/s00366-018-0644-0 Google Scholar
  17. 17.
    Moayedi H, Nazir R, Ghareh S, Sobhanmanesh A, Tan YC (2018) Performance analysis of piled-raft foundation system of varying pile lengths in controlling angular distortion. Soil Mech Found Eng 55:265–269CrossRefGoogle Scholar
  18. 18.
    Lee IM, Lee JH (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18:189–200CrossRefGoogle Scholar
  19. 19.
    Kurup PU, Griffin EP (2006) Prediction of soil composition from CPT data using general regression neural network. J Comput Civ Eng 20:281–289CrossRefGoogle Scholar
  20. 20.
    Chern SG, Lee CY (2009) CPT-based simplified liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol Taiwan 17:326–331Google Scholar
  21. 21.
    Suman S, Das SK, Mohanty R (2016) Prediction of friction capacity of driven piles in clay using artificial intelligence techniques. Int J Geotech Eng 10:469–475CrossRefGoogle Scholar
  22. 22.
    Meyerhof G (1963) Some recent research on the bearing capacity of foundations. Can Geotech J 1:16–26CrossRefGoogle Scholar
  23. 23.
    Terzaghi K, Peck R, Mesri G (1943) Soil mechanics in engineering practice. Wiley, OxfordGoogle Scholar
  24. 24.
    Xia T, Wang W (2009) Study on evaluating methods for time-dependent ultimate bearing capacity of single driven pile. Ieee Computer Soc, Los AlamitosCrossRefGoogle Scholar
  25. 25.
    Liu H, Li TJ, Zhang YF (1997) The application of artificial neural networks in estimating the pile bearing capacity. A A Balkema, LeidenGoogle Scholar
  26. 26.
    Shanbeh M, Najafzadeh D, Ravandi SAH (2012) Predicting pull-out force of loop pile of woven terry fabrics using artificial neural network algorithm. Ind Textila 63:37–41Google Scholar
  27. 27.
    Chan WT, Chow YK, Liu LF (1995) Neural-network—an alternative to pile driving formulas. Comput Geotech 17:135–156CrossRefGoogle Scholar
  28. 28.
    Goh ATC (1996) Pile driving records reanalyzed using neural networks. J Geotech Eng-ASCE 122:492–495CrossRefGoogle Scholar
  29. 29.
    Teh CI, Wong KS, Goh ATC, Jaritngam S (1997) Prediction of pile capacity using neural networks. J Comput Civil Eng 11:129–138CrossRefGoogle Scholar
  30. 30.
    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 36:616–625CrossRefGoogle Scholar
  31. 31.
    Moayedi H, Hayati S (2018) Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. Int J Geomech 18:06018009CrossRefGoogle Scholar
  32. 32.
    Arel E (2012) Predicting the spatial distribution of soil profile in Adapazari/Turkey by artificial neural networks using CPT data. Comput Geosci 43:90–100CrossRefGoogle Scholar
  33. 33.
    Sakr M (2013) Comparison between high strain dynamic and static load tests of helical piles in cohesive soils. Soil Dyn Earthq Eng 54:20–30CrossRefGoogle Scholar
  34. 34.
    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 55:91–102CrossRefGoogle Scholar
  35. 35.
    Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle RiverCrossRefGoogle Scholar
  36. 36.
    Jang SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  37. 37.
    Armaghani DJ, Momeni E, Abad S, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860CrossRefGoogle Scholar
  38. 38.
    Thomas S, Pillai GN, Pal K, Jagtap P (2016) Prediction of ground motion parameters using randomized ANFIS (RANFIS). Appl Soft Comput 40:624–634CrossRefGoogle Scholar
  39. 39.
    Mohamad ET, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2017) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28:393–406CrossRefGoogle Scholar
  40. 40.
    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 34:307–317CrossRefGoogle Scholar
  41. 41.
    Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32:705–715CrossRefGoogle Scholar
  42. 42.
    Mahdiyar A, Hasanipanah M, Armaghani DJ, Gordan B, Abdullah A, Arab H, Abd Majid MZ (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput 33:807–817CrossRefGoogle Scholar
  43. 43.
    Mohamad ET, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Env 74:745–757CrossRefGoogle Scholar
  44. 44.
    Armaghani DJ, Raja RS, Faizi K, Rashid ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405CrossRefGoogle Scholar
  45. 45.
    Gavin K, Cadogan D, Tolooiyan A, Casey P (2013) The base resistance of non-displacement piles in sand. Part I: field tests. Proc Inst Civ Eng Geotech Eng 166:540–548Google Scholar
  46. 46.
    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 36:2057–2076CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Hossein Moayedi
    • 1
    • 2
    Email author
  • Mehdi Raftari
    • 3
  • Abolhasan Sharifi
    • 4
  • Wan Amizah Wan Jusoh
    • 5
  • Ahmad Safuan A. Rashid
    • 1
    • 2
  1. 1.Department of Geotecthnics and Transportation, School of Civil Engineering, Faculty of EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
  3. 3.Department of Civil EngineeringKhorramabad Branch, Islamic Azad UniversityKhorramabadIran
  4. 4.Department of Civil Engineering, Faculty of EngineeringRazi UniversityKermanshahIran
  5. 5.Faculty of Civil Engineering and EnvironmentUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia

Personalised recommendations