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Assessment of deflection of pile implanted on slope by artificial neural network

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Abstract

The influence of the proximity of slope on the laterally loaded piles has been the subject of several researches. The main purpose of this study is developing a neural model able to predict the deflection of laterally loaded piles placed near a slope. To achieve this goal, we divided this work into two parts. In the first part, we have carried out a numerical modeling of an experimental model (Bouafia and Bouguerra in Fr Geotech J 75(2):47–56, 1996) using the three-dimensional finite element method. The relative error between the results of experimental and numerical model varies from 0.055 to 0.422, which indicates a good agreement. In the second part, after having validated the numerical model, we have created a database relating the deflection of pile to their contributory parameters. Artificial neural networks used this database in order to predict the pile deflection. The results obtained are very satisfactory with very acceptable errors (R2 = 0.9647, RMSE = 0.0133 and MAE = 0.0066).

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References

  1. Bouafia A, Bouguerra A (1996) Effet de la proximite´ du talus sur un pieu court et rigide charge´ horizontalement. Fr Geotech J 75(2):47–56 (in French)

    Article  Google Scholar 

  2. Matlock H, Reese LC (1962) Generalized solutions for laterally loaded piles. Trans Am Soc Civ Eng 127(1):1220–1247

    Google Scholar 

  3. Poulos HG, Davis EH (1980) Pile foundation analysis and design. Wiley, New York

    Google Scholar 

  4. Reese LC, Cox WR, Koop FD (1974) Analysis of laterally loaded piles in sand. Offshore technology in civil engineering hall of fame papers from the early years, pp 95–105

  5. Matlock H (1970). Correlations for design of laterally loaded piles in soft clay. Offshore technology in civil engineering’s hall of fame papers from the early years, pp 77–94

  6. Gabr MA, Borden RH (1989) Lateral response of piers in sloping soil profiles. In: Congrès international de mécanique des sols et des travaux de fondations. 12, pp 1197–1200

  7. Mezazigh S, Levacher D (1998) Laterally loaded piles in sand: slope effect on py reaction curves. Can Geotech J 35(3):433–441. https://doi.org/10.1139/t98-016

    Article  Google Scholar 

  8. Sivapriya SV, Ramanathan R (2019) Load-displacement behaviour of a pile on a sloping ground for various L/D ratios. Slovak J Civ Eng 27(1):1–6. https://doi.org/10.2478/sjce-2019-0001

    Article  Google Scholar 

  9. Shahid N, Rappon T, Berta W (2019) Applications of artificial neural networks in health care organizational decision-making: a scoping review. PloS one. https://doi.org/10.1371/journal.pone.0212356

    Article  Google Scholar 

  10. Berrezzek F, Khelil K, Bouadjila T (2019) Efficient wind speed forecasting using discrete wavelet transform and artificial neural networks. Revue d’Intell Artif. https://doi.org/10.18280/ria.330607

    Article  Google Scholar 

  11. Krzywanski J, Nowak W (2012) Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach. Int J Heat Mass Transf 55(15–16):4246–4253. https://doi.org/10.1016/j.ijheatmasstransfer.2012.03.066

    Article  Google Scholar 

  12. Krzywanski J, Blaszczuk A, Czakiert T, Rajczyk R, Nowak W (2014) Artificial intelligence treatment of NOX emissions from CFBC in air and oxy-fuel conditions. In: CFB-11: proceedings of the 11th international conference on fluidized bed technology, pp 619–624

  13. Krzywanski J, Fan H, Feng Y, Shaikh AR, Fang M, Wang Q (2018) Genetic algorithms and neural networks in optimization of sorbent enhanced H2 production in FB and CFB gasifiers. Energy Convers Manag 171:1651–1661. https://doi.org/10.1016/j.enconman.2018.06.098

    Article  Google Scholar 

  14. Liukkonen M, Hiltunen T, Havia E, Leinonen H, Hiltunen Y (2009) Modeling of soldering quality by using artificial neural networks. IEEE Trans Electron Packag Manuf 32(2):89–96

    Article  Google Scholar 

  15. Liukkonen M, Heikkinen M, Hiltunen T, Hälikkä E, Kuivalainen R, Hiltunen Y (2011) Artificial neural networks for analysis of process states in fluidized bed combustion. Energy 36(1):339–347. https://doi.org/10.1016/j.energy.2010.10.033

    Article  Google Scholar 

  16. Liukkonen M, Heikkinen M, Hiltunen T, Hälikkä E, Kuivalainen R, Hiltunen Y (2009) Modeling of process states by using artificial neural networks in a fluidized bed energy plant. Proc MATHMOD 9:397–402

    Google Scholar 

  17. Liukkonen M, Hälikkä E, Kuivalainen R, Hiltunen Y (2010) Modeling of nitrogen oxide emissions in fluidized bed combustion using artificial neural networks. Int J Data Eng 1(2):26–35

    Google Scholar 

  18. Hamrouni A, Dias D, Sbartai B (2017) Probabilistic analysis of piled earth platform under concrete floor slab. Soils Found 57(5):828–839. https://doi.org/10.1016/j.sandf.2017.08.012

    Article  Google Scholar 

  19. Hanna A, Ural D, Saygili G (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn Earthq Eng. https://doi.org/10.1016/j.soildyn.2006.11.001

    Article  Google Scholar 

  20. Kung G, Hsiao E, Schuster M, Juang C (2007) A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays. Comput Geotech. https://doi.org/10.1016/j.compgeo.2007.05.007

    Article  Google Scholar 

  21. Acharyya R, Dey A (2019) Assessment of bearing capacity for strip footing located near sloping surface considering ANN model. Neural Comput Appl 31(11):8087–8100. https://doi.org/10.1007/s00521-018-3661-4

    Article  Google Scholar 

  22. Shahin MA, Jaksa MB, Maier HR (2002) Artificial neural network based settlement prediction formula for shallow foundations on granular soils. Aust Geomech J News Aust Geomech Soc 37(4):45

    Google Scholar 

  23. Momeni E, Nazir R, Armaghani DJ, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131. https://doi.org/10.1016/j.measurement.2014.08.007

    Article  Google Scholar 

  24. 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(6):06018009

    Article  Google Scholar 

  25. Das SK, Manna B, Baidya DK (2010) An artificial neural network approach for prediction of dynamic pile-soil-pile interaction under vertical motion. In: GeoFlorida 2010: advances in analysis, modeling & design, pp 1422–1431

  26. Khari M, Armaghani DJ, Dehghanbanadaki A (2019) Prediction of lateral deflection of small-scale piles using hybrid PSO–ANN model. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-04134-9

    Article  Google Scholar 

  27. Kavitha PE, Beena KS, Narayanan KP (2016) A review on soil–structure interaction analysis of laterally loaded piles. Innov Infrastruct Solut 1(1):14. https://doi.org/10.1007/s41062-016-0015-x

    Article  Google Scholar 

  28. Demuth HB, Beale MH, De Jess O, Hagan MT (2014) Neural network design. Martin Hagan

  29. Chandran S, Ramachandran R, Cao J, Agarwal RP, Rajchakit G (2019) Passivity analysis for uncertain bam neural networks with leakage, discrete and distributed delays using novel summation inequality. Int J Control Autom Syst 17(8):2114–2124. https://doi.org/10.1007/s12555-018-0513-z

    Article  Google Scholar 

  30. Saravanakumar R, Rajchakit G, Ali MS et al (2019) Exponential dissipativity criteria for generalized BAM neural networks with variable delays. Neural Comput Appl 31:2717–2726. https://doi.org/10.1007/s00521-017-3224-0

    Article  Google Scholar 

  31. Saravanakumar R, Rajchakit G, Ali MS et al (2018) Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays. Neural Comput Appl 30:3893–3904. https://doi.org/10.1007/s00521-017-2974-z

    Article  Google Scholar 

  32. Rajchakit G, Saravanakumar R (2018) Exponential stability of semi-Markovian jump generalized neural networks with interval time-varying delays. Neural Comput Appl 29:483–492. https://doi.org/10.1007/s00521-016-2461-y

    Article  Google Scholar 

  33. Maharajan C, Raja R, Cao J, Rajchakit G, Alsaedi A (2018) Impulsive Cohen-Grossberg BAM neural networks with mixed time-delays: an exponential stability analysis issue. Neurocomputing 275:2588–2602. https://doi.org/10.1016/j.neucom.2017.11.028

    Article  Google Scholar 

  34. Shanmuganathan S (2016). Artificial neural network modelling: an introduction. In: Artificial neural network modelling. Springer, Cham, pp 1–14

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  1. Department of Civil Engineering and Hydraulic, Université Mohamed Ben Yahia Sedikk, 18000, Jijel, Algeria

    Kamel Goudjil & Leila Arabet

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  1. Kamel Goudjil
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  2. Leila Arabet
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Correspondence to Kamel Goudjil.

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Goudjil, K., Arabet, L. Assessment of deflection of pile implanted on slope by artificial neural network. Neural Comput & Applic 33, 1091–1101 (2021). https://doi.org/10.1007/s00521-020-04985-6

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  • DOI: https://doi.org/10.1007/s00521-020-04985-6

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