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Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile

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Abstract

Prediction of pile-bearing capacity developing artificial intelligence models has been done over the last decade. Such predictive tools can assist geotechnical engineers to easily determine the ultimate pile bearing capacity instead of conducting any difficult field tests. The main aim of this study is to predict the bearing capacity of pile developing several smart models, i.e., neuro-genetic, neuro-imperialism, genetic programing (GP) and artificial neural network (ANN). For this purpose, a number of concrete pile characteristics and its dynamic load test specifications were investigated to select pile cross-sectional area, pile length, pile set, hammer weight and drop height as five input variables which have the most impacts on pile bearing capacity as the single output variable. It should be noted that all the aforementioned parameters were measured by conducting a series of pile driving analyzer tests on precast concrete piles located in Pekanbaru, Indonesia. The recorded data were used to establish a database of 50 test cases. With regard to data modelling, many smart models of neuro-genetic, neuro-imperialism, GP and ANN were developed and then evaluated based on the three most common statistical indices, i.e., root mean squared error (RMSE), coefficient determination (R2) and variance account for (VAF). Based on the simulation results and the computed indices’ values, it is observed that the proposed GP model with training and test RMSE values of 0.041 and 0.040, respectively, performs noticeably better than the proposed neuro-genetic model with RMSE values of 0.042 and 0.040, neuro-imperialism model with RMSE values of 0.045 and 0.059, and ANN model with RMSE values of 0.116 and 0.108 for training and test sets, respectively. Therefore, this GP-based model can provide a new applicable equation to effectively predict the ultimate pile bearing capacity.

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References

  1. Mayerhof GG (1976) Bearing capacity and settlement of pile foundations. J Geotech Geoenvironmental Eng 102:11962

    Google Scholar 

  2. Coyle HM, Castello RR (1981) New design correlations for piles in sand. J Geotech Geoenvironmental Eng 107:16379

    Google Scholar 

  3. Masouleh SF, Fakharian K (2008) Application of a continuum numerical model for pile driving analysis and comparison with a real case. Comput Geotech 35:406–418

    Google Scholar 

  4. Likins GE, Rausche F (2004) Correlation of CAPWAP with static load tests. In: Proceedings of the seventh international conference on the application of stresswave theory to piles, Citeseer, pp 153–165

  5. Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33:23–31. https://doi.org/10.1007/s00366-016-0453-2

    Article  Google Scholar 

  6. 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 

  7. 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 

  8. Armaghani DJ, Mohamad ET, Momeni E et al (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:48

    Google Scholar 

  9. Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Meas J Int Meas Confed 60:50–63. https://doi.org/10.1016/j.measurement.2014.09.075

    Article  Google Scholar 

  10. Koopialipoor M, Armaghani DJ, 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 

  11. Koopialipoor M, Armaghani DJ, 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 

  12. 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 Space Technol 81:112–120

    Google Scholar 

  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 23:1123–1127

    Google Scholar 

  14. Samui P, Dixon B (2012) Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs. Hydrol Process 26:1361–1369

    Google Scholar 

  15. Samui P (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech 35:419–427

    MATH  Google Scholar 

  16. Armaghani DJ, Hasanipanah M, Mahdiyar A et al (2016) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29:1–11. https://doi.org/10.1007/s00521-016-2598-8

    Article  Google Scholar 

  17. Faradonbeh RS, Armaghani DJ, Majid MZA et al (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol 13:1453–1464. https://doi.org/10.1007/s13762-016-0979-2

    Article  Google Scholar 

  18. Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819–10832

    Google Scholar 

  19. Jian Z, Shi X, Huang R et al (2016) Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines. Trans Nonferrous Met Soc China 26:1938–1945

    Google Scholar 

  20. 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 

  21. Zhou J, Aghili N, Ghaleini EN et al (2019) A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Eng Comput. https://doi.org/10.1007/s00366-019-00726-z

    Article  Google Scholar 

  22. Wang M, Shi X, Zhou J (2019) Optimal charge scheme calculation for multiring blasting using modified harries mathematical model. J Perform Constr Facil 33:4019002

    Google Scholar 

  23. Wang M, Shi X, Zhou J, Qiu X (2018) Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects. Eng Optim 50:2177–2191

    Google Scholar 

  24. Armaghani DJ, Hajihassani M, Mohamad ET et al (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396

    Google Scholar 

  25. Safa M, Shariati M, Ibrahim Z et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct 21:679–688

    Google Scholar 

  26. Shariat M, Shariati M, Madadi A, Wakil K (2018) Computational Lagrangian multiplier method by using for optimization and sensitivity analysis of rectangular reinforced concrete beams. Steel Compos Struct 29:243–256

    Google Scholar 

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

    Google Scholar 

  28. Shahin MA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7:33–44. https://doi.org/10.1016/j.gsf.2014.10.002

    Article  Google Scholar 

  29. Teh CI, Wong KS, Goh ATC, Jaritngam S (1997) Prediction of pile capacity using neural networks. J Comput Civ Eng 11:129–138

    Google Scholar 

  30. Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 36:49–62

    Google Scholar 

  31. Goh ATC (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9:143–151

    Google Scholar 

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

    Google Scholar 

  33. Pal M, Deswal S (2008) Modeling pile capacity using support vector machines and generalized regression neural network. J Geotech Geoenvironmental Eng 134:1021–1024

    Google Scholar 

  34. Benali A, Nechnech A (2011) Prediction of the pile capacity in purely coherent soils using the approach of the artificial neural networks. In: international seminar, innovation and valorization in civil engineering and construction materials, Rabat, Morocco, pp 23–25

  35. Li W-X, Dai L-F, Hou X-B, Lei W (2007) Fuzzy genetic programming method for analysis of ground movements due to underground mining. Int J Rock Mech Min Sci 44:954–961

    Google Scholar 

  36. Baykasoğlu A, Güllü H, Çanakçı H, Özbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123

    Google Scholar 

  37. Majdi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47:246–253

    Google Scholar 

  38. Asadi M, Eftekhari M, Bagheripour MH (2011) Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 11:1932–1937

    Google Scholar 

  39. Karakus M (2011) Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP). Comput Geosci 37:1318–1323

    Google Scholar 

  40. Ravandi EG, Rahmannejad R, Monfared AEF, Ravandi EG (2013) Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation. Int J Min Sci Technol 23:733–737

    Google Scholar 

  41. Faradonbeh RS, Armaghani DJ, Monjezi M, Mohamad ET (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264

    Google Scholar 

  42. Faradonbeh RS, Armaghani DJ, Monjezi M (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Environ 75:993–1006

    Google Scholar 

  43. Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2015.09.020

    Article  Google Scholar 

  44. Salgado R (2008) The engineering of foundations. McGraw-Hill, New York

    Google Scholar 

  45. Smith EAL (1960) Pile-driving analysis by the wave equation. J Soil Mech Found Div 4:35–61

    Google Scholar 

  46. Goble GG, Rausche F, Moses F (1970) Dynamics studies on the bearing capacity of piles: final report to the Ohio Department of Highways. Cleveland, Ohio Case West Reserv Univ

  47. Fellenius BH (1984) Wave equation analysis and dynamic monitoring. Deep Found J 1:49–55

    Google Scholar 

  48. Rausche F, Goble GG, Likins GE Jr (1985) Dynamic determination of pile capacity. J Geotech Eng 111:367–383

    Google Scholar 

  49. Rausche F, Richardson B, Likins G (1996) Multiple blow CAPWAP analysis of pile dynamic records. In: Proceedings of the 5th international conference on the application of stress-wave theory to piles, Orlando, pp 435–446

  50. Bradshaw AS, Baxter CDP (2006) Design and construction of driven pile foundations–lessons learned on the Central Artery/Tunnel project. No. FHWAHRT-05-159

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

    Google Scholar 

  52. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576

    Google Scholar 

  53. Simpson PK (1990) Artificial neural systems: foundations, paradigms, applications, and implementations. Pergamon

  54. Armaghani DJ, Hajihassani M, Bejarbaneh BY et al (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Meas J Int Meas Confed 55:487–498. https://doi.org/10.1016/j.measurement.2014.06.001

    Article  Google Scholar 

  55. Khandelwal M, Marto A, Fatemi SA et al (2018) Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng Comput 34(2):307–317. https://doi.org/10.1007/s00366-017-0541-y

    Article  Google Scholar 

  56. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Google Scholar 

  57. Dreyfus G (2005) Neural networks: methodology and applications. Springer, Berlin

    MATH  Google Scholar 

  58. Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: Proceedings of the first international conference on genetic algorithms, pp 183–187

  59. Koza JR (1992) Genetic programming II, automatic discovery of reusable subprograms. MIT Press, Cambridge

    Google Scholar 

  60. Ferreira C (2001) Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst 13:87–129

    MATH  Google Scholar 

  61. Saghatforoush A, Monjezi M, Faradonbeh RS, Armaghani DJ (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32:255–266

    Google Scholar 

  62. Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer, Berlin

    MATH  Google Scholar 

  63. Silva S, Almeida J (2003) Dynamic maximum tree depth. Genetic and evolutionary computation conference. Springer, Berlin, pp 1776–1787

    MATH  Google Scholar 

  64. Faradonbeh RS, Monjezi M, Armaghani DJ (2016) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput 32:123–133. https://doi.org/10.1007/s00366-015-0404-3

    Article  Google Scholar 

  65. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge

    MATH  Google Scholar 

  66. Chipperfield AJ, Fleming P, Pohlheim H (1994) Genetic algorithm toolbox: for use with MATLAB; user’s guide (version 1.2). In: University of Sheffield, Department of Automatic Control and Systems Engineering

  67. 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 

  68. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation, 2007. CEC 2007. IEEE Congress on IEEE, pp 4661–4667

  69. Hajihassani M, Armaghani DJ, Marto A, Mohamad ET (2014) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886. https://doi.org/10.1007/s10064-014-0657-x

    Article  Google Scholar 

  70. 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 

  71. Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

    Google Scholar 

  72. Looney CG (1996) Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8:211–226

    Google Scholar 

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

    MATH  Google Scholar 

  74. Caudill M (1988) Neural networks primer, part III. AI Expert 3:53–59

    Google Scholar 

  75. Nourani V, Baghanam AH, Adamowski J, Gebremichael M (2013) Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J Hydrol 476:228–243

    Google Scholar 

  76. Zorlu K, Gokceoglu C, Ocakoglu F et al (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158

    Google Scholar 

  77. 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 

  78. Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13:1085–1098

    Google Scholar 

  79. Marto A, Hajihassani M, Armaghani DJ et al (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J 1:1. https://doi.org/10.1155/2014/643715

    Article  Google Scholar 

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Authors and Affiliations

  1. Chongqing Jianzhu College, Chongqing, 400072, China

    Wusi Chen

  2. Department of Civil Engineering School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University Building for Shipment and Civil Engineering, 800 Dong Chuan Road, Shanghai, 200240, China

    Payam Sarir

  3. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Duc Thang ward, Bac Tu Liem district, Hanoi, Vietnam

    Xuan-Nam Bui & Hoang Nguyen

  4. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, Duc Thang ward, Bac Tu Liem district, Hanoi, Vietnam

    Xuan-Nam Bui & Hoang Nguyen

  5. UTM Construction Research Centre, Institute for Smart, Infrastructure and Innovative Construction (ISIIC), School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    M. M. Tahir

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

    Danial Jahed Armaghani

  7. Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    Danial Jahed Armaghani

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  1. Wusi Chen
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Chen, W., Sarir, P., Bui, XN. et al. Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Engineering with Computers 36, 1101–1115 (2020). https://doi.org/10.1007/s00366-019-00752-x

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