Geomechanics and Engineering

  • 제20권5호
  • /
  • Pages.385-397
  • /
  • 2020
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils

  • Luat, Nguyen-Vu (Department of Architectural Engineering, Sejong University) ;
  • Lee, Kihak (Department of Architectural Engineering, Sejong University) ;
  • Thai, Duc-Kien (Department of Civil and Environmental Engineering, Sejong University)
  • 투고 : 2019.08.27
  • 심사 : 2020.02.06
  • 발행 : 2020.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents an application of artificial neural networks (ANNs) in settlement prediction of a foundation on sandy soil. In order to train the ANN model, a wide experimental database about settlement of foundations acquired from available literatures was collected. The data used in the ANNs model were arranged using the following five-input parameters that covered both geometrical foundation and sandy soil properties: breadth of foundation B, length to width L/B, embedment ratio Df/B, foundation net applied pressure qnet, and average SPT blow count N. The backpropagation algorithm was implemented to develop an explicit predicting formulation. The settlement results are compared with the results of previous studies. The accuracy of the proposed formula proves that the ANNs method has a huge potential for predicting the settlement of foundations on sandy soils.

키워드

과제정보

연구 과제 주관 기관 : Ministry of Land, Infrastructure and Transport of Korean Government

The research described in this paper was financially supported by Ministry of Land, Infrastructure and Transport of Korean Government (Grant 20CTAP-C143093-03).

참고문헌

  1. Alavi, A.H., Gandomi, A.H., Mousavi, M. and Mollahasani, A. (2010), "High-precision modeling of uplift capacity of suction caissons using a hybrid computational method", Geomech. Eng., 2(4), 253-280. https://doi.org/10.12989/gae.2010.2.4.253.
  2. Alkroosh, I. and Nikraz, H. (2011), "Simulating pile loadsettlement behavior from CPT data using intelligent computing", Centr. Eur. J. Eng., 1(3), 295-305. https://doi.org/10.2478/s13531-011-0029-2.
  3. Alvarez Grima, M. and Babuska, R. (1999), "Fuzzy model for the prediction of unconfined compressive strength of rock samples", Int. J. Rock Mech. Min. Sci., 36(3), 339-349. https://doi.org/10.1016/S0148-9062(99)00007-8.
  4. Anagnostopoulos, A.G., Papadopoulos, B.P. and Kavvadas, M.J. (1991), "Direct estimation of settlements on sand, based on SPT results", Proceeding of the 10th European Conference on Soil Mechacnics and Foundation Engineering, Florence, Italy, May.
  5. Berardi, R., Jamiolkowski, M. and Lancellotta, R. (1991), "Settlement of shallow foundations in sand selection of stiffness on the basis of penetration resistance", Proceedings of the Geotechnical Engineering Congress, Boulder, Colorado, U.S.A., June.
  6. Bernstein, J., Wang, Y.X., Azizzadenesheli, K. and Anandkumar, A. (2018), "signSGD: Compressed optimisation for non-convex problems", arXiv preprint arXiv:1802.04434.
  7. Briaud, J.L. and Gibbens, R.M. (1994), "Predicted and measured behavior of five spread footings on sand", Geotech. Spec. Publ., 41, 255.
  8. Burbidge, M.C. (1982), "A case study review of settlement on granular soil", M.Sc. Thesis, Imperial College of Science and Technology, University of London, London, U.K.
  9. Burland, J.B. and Burbidge, M.C. (1985), "Settlement of foundations on sand and gravel", Proc. Inst. Civ. Eng., 78(6), 1325-1381. https://doi.org/10.1680/iicep.1985.1058.
  10. Celik, S. and Tan, O. (2005), "Determination of preconsolidation pressure with artificial neural network", Civ. Eng. Environ. Syst., 22(4), 217-231. https://doi.org/10.1080/10286600500383923.
  11. Chaudhary, S., Pendharkar, U. and Nagpal, A.K. (2007), "Bending moment prediction for continuouscomposite beams by neural networks", Adv. Struct. Eng., 10(4), 439-454. https://doi.org/10.1260/136943307783239390.
  12. Chern, S.G. and Lee, C.Y. (2008), "CPT-based simplified liquefaction assessment by using fuzzy-neural network", J. Mar. Sci. Technol., 16(2), 139-148.
  13. Cho, S.E. (2009), "Probabilistic stability analyses of slopes using the ANN-based response surface", Comput. Geotech., 36(5), 787-797. https://doi.org/10.1016/ j.compgeo.2009.01.003.
  14. Cybenko, G. (1989), "Approximations by super positions of sigmoidal functions", Math. Control. Signals Syst., 2(4), 303-314. https://doi.org/10.1007/bf02551274.
  15. Das, B. and Sivakugan, N. (2007), "Settlements of shallow foundations on granular soil - an overview", Int. J. Geotech. Eng., 1(1), 19-29. https://doi.org/10.3328/ 10.3328/IJGE.2007.01.01.19-29.
  16. Das, S.K. and Basudhar, P.K. (2006), "Undrained lateral load capacity of piles in clay using artificial neural network", Comput. Geotech., 33(8), 454-459. https://doi.org/10.1016/j.compgeo.2006.08.006.
  17. Das, S.K., Biswal, R.K., Sivakugan, N. and Das, B. (2011), "Classification of slopes and prediction of factor of safety using differential evolution neural networks", Environ. Earth Sci., 64(1), 201-210. https://doi.org/ 10.1007/s12665-010-0839-1.
  18. Dincer, I. (2011), "Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures", Adv. Eng. Softw., 42(4), 160-171. https://doi.org/10.1016/j.advengsoft.2011.02.001.
  19. Duchi, J., Hazan, E. and Singer, Y. (2011), "Adaptive subgradient methods for online learning and stochastic optimization", J. Mach. Learn. Res., 12, 2121-2159.
  20. Erzin, Y. and Cetin, T. (2014), "The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions", Geomech. Eng., 6(1), 1-15. https://doi.org/10.12989/gae.2014.6.1.001.
  21. Erzin, Y. and Ecemis, N. (2017), "The use of neural networks for the prediction of cone penetration resistance of silty sands", Neural Comput. Appl., 28(1), 727-736. https://doi.org/10.1007/s00521-016-2371-z.
  22. Fei, S., Tan, X., Wang, X., Du, L. and Sun, Z. (2019), "Evaluation of soil spatial variability by micro - structure simulation", Geomech. Eng., 17(6), 565-572. https://doi.org/10.12989/gae.2019.17.6.565.
  23. Gandomi, A.H. and Alavi, A.H. (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. https://doi.org/10.1007/s00521-011-0734-z.
  24. Garcia, S.R., Romo, M.P. and Figueroa-Nazuno, J. (2006), "Soil dynamic properties determination: A neurofuzzy system approach", Control Intell. Syst., 34(1), 2121-2159.
  25. Goh, A. (1994), "Seismic liquefaction potential assessed by neural networks", J. Geotech. Eng., 120(9), 1467-1480. https://doi.org/10.1061/(ASCE)0733-9410(1994)120:9(1467).
  26. Gonzalez, M.P. and Zapico, J.L. (2008), "Seismic damage identification in buildings using neural networks and modal data", Comput. Struct., 86(3), 416-426. https://doi.org/10.1016/j.compstruc.2007.02.021.
  27. Haque, M.E. and Sudhakar, K.V. (2002), "ANN back-propagation prediction model for fracture toughness in microalloy steel", Int. J. Fatigue, 24(9), 1003-1010. https://doi.org/10.1016/S0142-1123(01)00207-9.
  28. Hertz, J., Krogh, A. and Palmer, R.G. (1992), Introduction to the Theory of Neural Computation, Addison-Wesley Pub. Co., Redwood City, California, U.S.A.
  29. Javadi, A.A. and Rezania, M., (2009), "Applications of artificial intelligence and data mining techniques in soil modeling", Geomech. Eng., 1(1), 53-74. https://doi.org/10.12989/gae.2009.1.1.053.
  30. Jorden, E., (1977), "Settlement in sand-methods of calculating and factors affecting", Ground Eng., 10(1), 30-67. https://doi.org/10.1139/t06-029.
  31. Kaastra, I. and Boyd, M. (1996), "Designing a neural network for forecasting financial and economic time series", Neurocomputing, 10(3), 215-236. https://doi.org/ 10.1016/0925-2312(95)00039-9.
  32. Kamatchi, P., Rajasankar, J., Ramana, G.V. and Nagpal, A.K. (2010), "A neural network based methodology to predict sitespecific spectral acceleration values", Earthq. Eng. Eng. Vib., 9(4), 459-472. https://doi.org/10.1007/ s11803-010-0041-1.
  33. Kanellopoulos, I. and Wilkinson, G.G. (1997), "Strategies and best practice for neural network image classification", Int. J. Remote Sens., 18(4), 711-725. https://doi.org/ 10.1080/014311697218719.
  34. Kingma, D. and Ba, J. (2015), "Adam: A method for stochastic optimization", arXiv preprint arXiv:1412.6980.
  35. Luat, N.V., Lee, J., Lee, D.H. and Lee, K. (2020), "GS - MARS method for predicting the ultimate load - carrying capacity of rectangular CFST columns under eccentric loading", Comput. Concrete, 25(1), 1-14. https://doi.org/10.12989/cac.2020.25.1.001.
  36. Maail, S. (1987), "Comparion of methods of predicting foundation settlement of sand and gravel", Proceedings of the 9th Southeast Asian Geotechnical Conference, Bangkok, Thailand, December.
  37. Maugeri, M., Castelli, F., Massimino, M.R. and Verona, G. (1998), "Observed and computed settlements of twoshallow foundations on sand", J. Geotech. Geoenviron. Eng., 124(7), 595-605. https://doi.org/10.1061/(ASCE)1090-0241(1998)124:7(595).
  38. Meyerhof, G.G. (1956), "Penetration tests and bearing capacity of cohesionless soils", J. Soil Mech. Div., 82(1), 1-12. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:4(478).
  39. Meyerhof, G.G. (1965), "Shallow foundations", J. Soil Mech. Found. Div., 91(2), 21-32. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:4(478).
  40. Meyerhof, G.G. (1974), "General report: State-of-the-art of penetration testing in countries outside Europe", Proceedings of the 1st European Symposium on Penetration Testing, Stockholm, Sweden.
  41. Samu, P. and Thallak, S. (2011), "Determination of liquefaction susceptibility of soil based on field test and artificial intelligence", Int. J. Earth Sci., 4(2), 216-222.
  42. Schmertmann, J.H. (1970), "Static cone to compute static settlement over sand", J. Soil Mech. Found. Div., 96(3), 1011-1043. https://doi.org/10.1061/JSFEAQ.0001418
  43. Schultze, E. and Sherif, G. (1973), "Prediction of settlements from evaluated settlement observation for sand", Proceedings of the 8th International Conference on Soil Mechanics and Foundation Engineering, Moscow, Russia, August.
  44. Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2002), "Predicting settlement of shallow foundations using neural networks", J. Geotech. Geoenviron. Eng., 128(9), 785-793. https://doi.org/10.1061/(ASCE)1090-0241(2002)128:9(785).
  45. Shahin, Mohamed, A. (2010), "Intelligent computing for modeling axial capacity of pile foundations", Can. Geotech. J., 47(2), 230-243. https://doi.org/10.1139/T09-094.
  46. Shahrbanouzadeh, M., Barani, G.A. and Shojaee, S. (2015), "Analysis of flow through dam foundation by FEM and ANN models Case study: Shahid Abbaspour Dam", Geomech. Eng., 9(4), 465-481. https://doi.org/10.12989/gae.2015.9.4.465.
  47. Sivakugan, N. and Johnson, K. (2004), "Settlement predictions in granular soils: a probabilistic approach", Geotechnique, 54(7), 499-502. https://doi.org/10.1680/geot.2004.54.7.499.
  48. Sivapullaiah, P.V., Guru Prasad, B. and Allam, M.M. (2009), "Modeling sulfuric acid induced swell in carbonate clays using artificial neural networks", Geomech. Eng., 1(4), 307-321. https://doi.org/10.12989/gae.2009.1.4.307.
  49. Terzaghi, K. and Peck, R.B. (1968), Soil Mechanics in Engineering Practice, John Wiley & Sons, New York, U.S.A.
  50. Thai, D.K., Tu, T.M., Bui, T.Q. and Bui, T.T. (2019), "Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-019-00842-w.
  51. Tsompanakis, Y., Lagaros, N.D. and Stavroulakis, G.E. (2008), "Soft computing techniques in parameter identification and probabilistic seismic analysis of structures", Adv. Eng. Softw., 39(7), 612-624. https://doi.org/10.1016/j.advengsoft.2007.06.004.
  52. Wahls, H.E. (1997), "Settlement of shallow foundations in sandsselection of stiffness on the basis of penetration resistance", Proceeding of the 3rd International Geotechnical Engineerign Conference, Cairo, Egypt.
  53. Zeiler, M.D. (2012), "ADADELTA: An adaptive learning rate method", arXiv preprint arXiv:1212.5701.

피인용 문헌

  1. Evaluation of geological conditions and clogging of tunneling using machine learning vol.25, pp.1, 2020, https://doi.org/10.12989/gae.2021.25.1.059
  2. Ultimate axial capacity prediction of CCFST columns using hybrid intelligence models - a new approach vol.40, pp.3, 2021, https://doi.org/10.12989/scs.2021.40.3.461