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Determination of the friction capacity of driven piles using three sophisticated search schemes

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Abstract

Proper approximation of friction capacity (FC) of driven piles is a noticeable issue in geotechnical engineering. Hence, the pivotal focus of the current research is on proposing reliable predictors for evaluating this parameter. Three artificial neural networks (ANNs) improved by firefly algorithm (FA), multi-tracker optimization algorithm (MTOA), and black hole algorithm (BHA) are used to estimate the FC when it is affected by four key factors, namely pile length, pile diameter, vertical effective stress, and undrained shear strength. Checking the optimization process of different population sizes revealed that the MTOA and BHA need the population twice as many as the FA does (400 vs. 200). The results showed that all three models can properly comprehend the association of FC to the mentioned parameters. According to the values of root mean square error (RMSE) as well as the coefficient of determination (R2) obtained in the prediction phase (6.9606 and 0.8827, 8.5411 and 0.8370, 6.0454 and 0.9194, respectively, for the FA-ANN, MTOA-ANN, and BHA-ANN), the BHA-ANN is more accurate than two other algorithms, however, the MTOA-ANN gained the largest accuracy of training. The suggested models proved the high efficiency of hybrid predictors and can be potential alternatives to experimental and traditional approaches.

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References

  1. Wang B, Moayedi H, Safuan A, Rashid A, Nguyen H (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 36:1–10. https://doi.org/10.1007/s00366-019-00764-7

    Article  Google Scholar 

  2. Tien Bui D, Moayedi H, MaM Abdullahi, Safuan A, Rashid A, Nguyen H (2019) Prediction of pullout behavior of belled piles through various machine learning modelling techniques. Sensors 19:3678. https://doi.org/10.3390/s19173678

    Article  Google Scholar 

  3. Moayedi H, Mu’azu MA, Kok Foong L (2019) Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles. Eng Comput. https://doi.org/10.1007/s00366-019-00885-z

    Article  Google Scholar 

  4. Liu L, Moayedi H, Rashid ASA, Rahman SSA, Nguyen H (2019) Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Eng Comput 35:1–13. https://doi.org/10.1007/s00366-019-00767-4

    Article  Google Scholar 

  5. Tarawneh B (2013) Pipe pile setup: database and prediction model using artificial neural network. Soils Found 53:607–615

    Google Scholar 

  6. Moayedi H, Hayati S (2018) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl 31:1–17. https://doi.org/10.1007/s00521-018-3555-5

    Article  Google Scholar 

  7. Toghroli A, Mohammadhassani M, Suhatril M, Shariati M, Ibrahim Z (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct 17:623–639

    Google Scholar 

  8. 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

    Google Scholar 

  9. Liu W, Zhang X, Li H, Chen J (2020) Investigation on the Deformation and Strength Characteristics of Rock Salt Under Different Confining Pressures. Geotech Geol Eng 38:1-15. https://doi.org/10.1007/s10706-020-01388-1

    Article  Google Scholar 

  10. Moayedi H, Nazir R, Gör M, Anuar Kassim K, Kok Foong L (2019) A new real-time monitoring technique in calculation of the p-y curve of single thin steel piles considering the influence of driven energy and using strain gauge sensors. Measurement. https://doi.org/10.1016/j.measurement.2019.107365

    Article  Google Scholar 

  11. Moayedi H, Kalantar B, MaM Abdullahi, Rashid ASA, Nazir R, Nguyen H (2019) Determination of young elasticity modulus in bored piles through the global strain extensometer sensors and real-time monitoring data. Appl Sci 9:3060. https://doi.org/10.3390/app9153060

    Article  Google Scholar 

  12. 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 55:265–269. https://doi.org/10.1007/s11204-018-9535-z

    Article  Google Scholar 

  13. Nazir R, Moayedi H, Mosallanezhad M, Tourtiz A (2015) Appraisal of reliable skin friction variation in a bored pile. Proc Inst Civ Eng Geotech Eng 168:75–86. https://doi.org/10.1680/geng.13.00140

    Article  Google Scholar 

  14. Goh A (1995) Empirical design in geotechnics using neural networks. Geotechnique 45:709–714

    Google Scholar 

  15. Sun G, Xu G, Jiang N (2020) A simple differential evolution with time-varying strategy for continuous optimization. Soft Comput 24:2727–2747

    Google Scholar 

  16. Tian X, Song Z, Wang B, Zhou G (2020) A theoretical calculation method of influence radius of settlement based on the slices method in tunnel construction. Math Probl Eng 2020: pp. 1-9.

  17. Zhang Y, Huang P (2020) Influence of mine shallow roadway on airflow temperature. Arab J Geosci 13:1–6

    Google Scholar 

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

    Google Scholar 

  19. Kardani N, Zhou A, Nazem M, Shen S-L (2020) Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotech Geol Eng 38:2271–2291. https://doi.org/10.1007/s10706-019-01085-8

    Article  Google Scholar 

  20. Amini A, Hamidi S, Shirzadi A, Behmanesh J, Akib S (2020) Efficiency of artificial neural networks in determining scour depth at composite bridge piers. Int J Riv Basin Manag 18:1–7. https://doi.org/10.1080/15715124.2020.1742138

    Article  Google Scholar 

  21. Milad F, 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 19:611–620

    Google Scholar 

  22. 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

  23. Alzo’Ubi A, Ibrahim F (2018) Predicting the pile static load test using backpropagation neural network and generalized regression neural network–a comparative study. Int J Geotech Eng 14:1–12. https://doi.org/10.1080/19386362.2018.1519975

    Article  Google Scholar 

  24. Samui P (2019) Determination of friction capacity of driven pile in clay using gaussian process regression (GPR), and minimax probability machine regression (MPMR). Geotech Geol Eng 37:4643–4647

    Google Scholar 

  25. Samui P (2011) Multivariate adaptive regression spline applied to friction capacity of driven piles in clay. Geomech Eng 3:285–290

    Google Scholar 

  26. Samadi M, Afshar MH, Jabbari E, Sarkardeh H (2020) Prediction of current-induced scour depth around pile groups using MARS, CART, and ANN approaches. Mar Georesour Geotechnol 38:1–12. https://doi.org/10.1080/1064119X.2020.1731025

    Article  Google Scholar 

  27. Zhang W, Goh AT (2016) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7:45–52

    Google Scholar 

  28. Chen H, Zhang Q, Luo J, Xu Y, Zhang X (2020) An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine. Appl Soft Comput 86:105884

    Google Scholar 

  29. Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl-Based Syst 96:61–75

    Google Scholar 

  30. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput 88:105946

    Google Scholar 

  31. Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Huang H, Tong C (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69–84

    Google Scholar 

  32. Xu X, Chen H-L (2014) Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput 18:797–807

    Google Scholar 

  33. Xu Y, Chen H, Luo J, Zhang Q, Jiao S, Zhang X (2019) Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181–203

    MathSciNet  Google Scholar 

  34. Zhao X, Li D, Yang B, Ma C, Zhu Y, Chen H (2014) Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Appl Soft Comput 24:585–596

    Google Scholar 

  35. Zhao X, Zhang X, Cai Z, Tian X, Wang X, Huang Y, Chen H, Hu L (2019) Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490

    Google Scholar 

  36. Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324

    Google Scholar 

  37. Wu D, Foong LK, Lyu Z (2020) Two neural-metaheuristic techniques based on vortex search and backtracking search algorithms for predicting the heating load of residential buildings. Eng Comput 36:1–14. https://doi.org/10.1007/s00366-020-01074-z

    Article  Google Scholar 

  38. Moayedi H, Mosallanezhad M, Mehrabi M, Safuan ARA, Biswajeet P (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984. https://doi.org/10.1007/s00366-018-0644-0

    Article  Google Scholar 

  39. Öser C, Temür R (2018) Optimization of pile groups under vertical loads using metaheuristic algorithms, Handbook of research on predictive modeling and optimization methods in science and engineering. IGI Global, pp 276–298

  40. Chou J-S, Ngo N-T, Pham A-D (2016) Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression. J Comput Civ Eng 30:04015002

    Google Scholar 

  41. Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10:10. https://doi.org/10.1007/s12517-017-3285-5

    Article  Google Scholar 

  42. Prayogo D, Susanto YTT (2018) Optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine. Adv Civ Eng 2018:1–9

    Google Scholar 

  43. Luo Z, Hasanipanah M, Amnieh HB, Brindhadevi K, Tahir M (2019) GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles. Eng Comput 36:1–9. https://doi.org/10.1007/s00366-019-00858-2

    Article  Google Scholar 

  44. Murlidhar BR, Sinha RK, Mohamad ET, Sonkar R, Khorami M (2020) The effects of particle swarm optimisation and genetic algorithm on ANN results in predicting pile bearing capacity. Int J Hydromechatron 3:69–87

    Google Scholar 

  45. Chen H, Fan DL, Fang L, Huang W, Huang J, Cao C, Yang L, He Y, Zeng L (2020) Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis. Int J Pattern Recogn Artif Intell 2058012:1–12. https://doi.org/10.1142/S0218001420580124

    Article  Google Scholar 

  46. Zhu B, Ye S, Jiang M, Wang P, Wu Z, Xie R, Chevallier J, Wei Y-M (2019) Achieving the carbon intensity target of China: a least squares support vector machine with mixture kernel function approach. Appl Energy 233:196–207

    Google Scholar 

  47. Sun G, Hasanipanah M, Amnieh HB, Foong LK (2020) Feasibility of indirect measurement of bearing capacity of driven piles based on a computational intelligence technique. Measurement 156:107577

    Google Scholar 

  48. Moayedi H, Mu’azu MA, Foong LK (2019) Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles. Eng Comput 37:1–17. https://doi.org/10.1007/s00366-019-00885-z

    Article  Google Scholar 

  49. Pang R, Xu B, Kong X, Zou D (2018) Seismic fragility for high CFRDs based on deformation and damage index through incremental dynamic analysis. Soil Dyn Earthq Eng 104:432–436

    Google Scholar 

  50. Song Z, Cheng Y, Tian X, Wang J, Yang T (2020) Mechanical properties of limestone from Maixi tunnel under hydro-mechanical coupling. Arab J Geosci 13:402

    Google Scholar 

  51. Tian X, Song Z, Wang J (2019) Study on the propagation law of tunnel blasting vibration in stratum and blasting vibration reduction technology. Soil Dyn Earthq Eng 126:105813

    Google Scholar 

  52. Wang B, Jiang Q, Jiang P (2019) A combined forecasting structure based on the L1 norm: application to the air quality. J Environ Manage 246:299–313

    Google Scholar 

  53. Warnana DD (2018) Black hole algorithm for determining model parameter in self-potential data. J Appl Geophys 148:189–200

    Google Scholar 

  54. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2:359–366

    MATH  Google Scholar 

  55. Tien Bui D, Moayedi H, Anastasios D, Kok Foong L (2019) Predicting heating and cooling loads in energy-efficient buildings using two hybrid intelligent models. Appl Sci 9:3543

    Google Scholar 

  56. Seyedashraf O, Mehrabi M, Akhtari AA (2018) Novel approach for dam break flow modeling using computational intelligence. J Hydrol 559:1028–1038

    Google Scholar 

  57. Margenot A, O’Neill T, Sommer R, Akella V (2020) Predicting soil permanganate oxidizable carbon (POXC) by coupling DRIFT spectroscopy and artificial neural networks (ANN). Comput Electron Agric 168:105098

    Google Scholar 

  58. Anderson D, McNeill G (1992) Artificial neural networks technology. Kaman Sci Corp 258:1–83

    Google Scholar 

  59. Yang X-S (2008) Firefly algorithm. Nature-inspired metaheuristic algorithms, vol 20. Luniver Press, Frome, pp 79–90

    Google Scholar 

  60. Gálvez A, Iglesias A (2013) Firefly algorithm for polynomial Bézier surface parameterization. J Appl Math 2013:1–9. https://doi.org/10.1155/2013/237984

    Article  MATH  Google Scholar 

  61. Zakeri E, Moezi SA, Bazargan-Lari Y, Zare A (2017) Multi-tracker optimization algorithm: a general algorithm for solving engineering optimization problems. Iran J Sci Technol Trans Mech Eng 41:315–341

    Google Scholar 

  62. Zakeri E, Moezi SA, Eghtesad M (2019) Optimal interval type-2 fuzzy fractional order super twisting algorithm: a second order sliding mode controller for fully-actuated and under-actuated nonlinear systems. ISA Trans 85:13–32

    Google Scholar 

  63. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

    MathSciNet  Google Scholar 

  64. Farahmandian M, Hatamlou A (2015) Solving optimization problems using black hole algorithm. J Adv Comput Sci Technol 4:68

    Google Scholar 

  65. Soto R, Crawford B, Olivares R, Barraza J, Figueroa I, Johnson F, Paredes F, Olguín E (2017) Solving the non-unicost set covering problem by using cuckoo search and black hole optimization. Nat Comput 16:213–229

    MathSciNet  MATH  Google Scholar 

  66. Goh AT (1996) Pile driving records reanalyzed using neural networks. J Geotech Eng 122:492–495

    Google Scholar 

  67. Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22

    Google Scholar 

  68. Duan Q, Gupta VK, Sorooshian S (1993) Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76:501–521

    MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Civil Engineering, Central South University, Changsha, China

    Sihao Liang

  2. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loke Kok Foong

  3. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loke Kok Foong

  4. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam

    Zongjie Lyu

  5. Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Zongjie Lyu

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  1. Sihao Liang
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Liang, S., Foong, L.K. & Lyu, Z. Determination of the friction capacity of driven piles using three sophisticated search schemes. Engineering with Computers 38, 1515–1527 (2022). https://doi.org/10.1007/s00366-020-01118-4

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