Geomechanics and Engineering

  • 제27권3호
  • /
  • Pages.197-211
  • /
  • 2021
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of artificial intelligence methods for predicting transient response of foundation

  • Sasmal, Suvendu K. (Department of Civil Engineering, National Institute of Technology Rourkela) ;
  • Behera, Rabi N. (Department of Civil Engineering, National Institute of Technology Rourkela)
  • 투고 : 2020.07.29
  • 심사 : 2021.09.27
  • 발행 : 2021.11.10
⟨ 이전 논문 다음 논문 ⟩

초록

The present work focuses on analysing the displacement of a shallow foundation due to sudden change in state of loading. When a dynamic load hits the foundation, before attaining steady state, the foundation undergoes sudden displacement. This displacement is a function of soil properties viz. modulus of elasticity, Poisson's ratio, loading conditions and the static load already applied on the foundation. A Finite Element based numerical model that considers soil structure interaction is simulated to generate a data set. After verification of model results, the dataset is analysed using five different methods including Levenberg Marquardt Neural Network (LMNN), Bayesian Regularization Neural network (BRNN), Support Vector Machines (SVM), Multivariate Adaptive Regression Splines (MARS) and Multi Gene Genetic Programming (MGGP). A comparative analysis of all the methods has been presented to find out the most effective method. In addition to these, sensitivity analysis is performed to find out the most influencing input parameter. The BRNN method is found to be the most efficient method and the static load on the foundation is the most influencing parameter as revealed from the rigorous statistical analysis. The outcomes will be helpful in quick analysis of shallow strip foundations.

키워드

참고문헌

  1. Alavi, A.H., Hasni, H., Zaabar, I. and Lajnef, N. (2017), "A new approach for modeling of flow number of asphalt mixtures", Arch. Civil Mech. Eng., 17(2), 326-335. https://doi.org/10.1016/j.acme.2016.06.004.
  2. Allotey, N. and El Naggar, M.H. (2008), "An investigation into the Winkler modeling of the cyclic response of rigid footings", Soil Dyn. Earthq. Eng., 28(1), 44-57. https://doi.org/10.1016/j.soildyn.2007.04.003.
  3. Behera, R.N. and Patra, C.R. (2018), "Ultimate bearing capacity prediction of eccentrically inclined loaded strip footings", Geotech. Geol. Eng., 36(5), 3029-3080. https://doi.org/10.1007/s10706-018-0521-z.
  4. Beura, S.K. and Bhuyan, P.K. (2018), "Operational analysis of signalized street segments using multi-gene genetic programming and functional network techniques", Arab. J. Sci. Eng., 43(10), 5365-5386. https://doi.org/10.1007/s13369-018-3176-4.
  5. Bui, D.K., Nguyen, T., Chou, J.S., Nguyen-Xuan, H. and Ngo, T.D. (2018), "A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete", Constr. Build. Mater., 180, 320-333. https://doi.org/10.1016/j.conbuildmat.2018.05.201.
  6. Das, B.M. (2016), Principle of Foundation Engineering, Eighth Edition, Cengage Learning.
  7. Das, B.M., Yen, S.C. and Singh, G. (1995), "Settlement of shallow foundation on sand due to cyclic loading", Proceedings of the International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics.
  8. Das, S.K. and Basudhar, P.K. (2008), "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.
  9. Das, S.K. and Sabat, A.K. (2008), "Using neural networks for prediction of some properties of fly ash", Elect. J. Geotech. Eng., 13, 1-14.
  10. Das, S.K., Samui, P. and Sabat, A.K. (2011) "Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil", Geotech. Geol. Eng., 29(3), 329-342. https://doi.org/10.1007/s10706-010-9379-4.
  11. Das, S.K., Samui, P. and Sabat, A.K. (2011), "Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine", Int. J. Geomech., 12(5), 606-611. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000129.
  12. Das, S.K., Samui, P., Sabat, A.K. and Sitharam, T.G. (2010) "Prediction of swelling pressure of soil using artificial intelligence techniques", Environ. Earth Sci., 61(2), 393-403. https://doi.org/10.1007/s12665-009-0352-6.
  13. Dimitriadou, E., Hornik, K, Leisch, F, Meyer, D, Weingessel, A. and Leisch, M.F. (2009), Package "e1071". R Software Package. http://cran.rproject.org/web/packages/e1071/index.Html.
  14. EPRI (1990), Manual on Estimating Soil Properties for Foundation Design. Electric Power Research Institute, Palo Alto, California, U.S.A.
  15. Erzin, Y, and Gul, T. (2013), "The use of neural networks for the prediction of the settlement of pad footings on cohesionless soils based on standard penetration test", Geomech. Eng., 5(6), 541-564. http://doi.org/10.12989/gae.2013.5.6.541.
  16. Erzin, Y. and Gul, T.O. (2014), "The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test", Neural Comput. Appl., 24(3-4), 891-900. https://doi.org/10.1007/s00521-012-1302-x.
  17. Friedman, J. H. (1991) "Multivariate adaptive regression splines", Annals Stat., 19(1), 1-67. https://doi.org/10.1214/aos/1176347963
  18. Gandomi, A.H. and Alavi, A.H. (2012a), "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.
  19. Gandomi, A.H. and Alavi, A.H. (2012b), "A new multi-gene genetic programming approach to non-linear system modeling. Part II, geotechnical and earthquake engineering problems", Neural Comput. Appl., 21(1), 189-201. https://doi.org/10.1007/s00521-011-0735-y.
  20. Gandomi, A.H., Yun, G.J. and Alavi, A. H. (2013), "An evolutionary approach for modeling of shear strength of RC deep beams", Mater. Struct., 46(12), 2109-2119. https://doi.org/10.1617/s11527-013-0039-z.
  21. Gao, W. and Chen, D. (2019), "Prediction model of service life for tunnel structures in carbonation environments by genetic programming", Geomech. Eng., 18(4), 373-389. https://doi.org/10.12989/gae.2019.18.4.373.
  22. Garg, A., Garg, A, Tai, K. and Sreedeep, S. (2014b), "Estimation of pore water pressure of soil using genetic programming", Geotech. Geol. Eng., 32(4), 765-772. https://doi.org/10.1007/s10706-014-9755-6.
  23. Garg, A., Garg, A, Tai, K., Barontini, S. and Stokes, A. (2014a), "A computational intelligence-based genetic programming approach for the simulation of soil water retention curves", Transport Porous Media, 103(3), 497-513. https://doi.org/10.1007/s11242-014-0313-8.
  24. Gazetas, G. (1991) "Formulas and charts for impedances of surface and embedded foundations", J. Geotech. Eng., 117(9), 1363-1381. https://doi.org/10.1061/(ASCE)0733-9410(1991)117:9(1363).
  25. Ghanizadeh, A.R., Abbaslou, H., Amlashi, A.T. and Alidoust, P. (2019), "Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine." Front. Struct. Civ. Eng., 13(1), 215-39. https://doi.org/10.1007/s11709-018-0489-z.
  26. Goh, A.T., Kulhawy, F.H. and Chua, C.G. (2005), "Bayesian neural network analysis of undrained side resistance of drilled shafts", J. Geotech. Geoenviron. Eng., 131(1), 84-93. https://doi.org/10.1061/(ASCE)1090-0241(2005)131:1(84).
  27. Golafshani, E.M. and Behnood, A. (2018), "Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete", J. Cleaner Prod., 176, 1163-1176. https://doi.org/10.1016/j.jclepro.2017.11.186.
  28. Harden, C., Hutchinson, T., Martin, G. R. and Kutter, B. L. (2005), "Numerical modeling of the nonlinear cyclic response of shallow foundations", Report No 2005/04 Pacific Earthquake Engineering Research Center (PEER) Berkeley California, U.S.A.
  29. Hasanipanah, M., Keshtegar, B., Thai, D.K. and Troung, N.T. (2020), "An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting", Eng. Comput., 1-13. https://doi.org/10.1007/s00366-020-01105-9.
  30. Kaveh, A., Hamze-Ziabari, S.M. and Bakhshpoori, T. (2018), "Soft computing-based slope stability assessment: A comparative study", Geomech. Eng., 14(3), 257-269. http://doi.org/10.12989/gae.2018.14.3.257.
  31. Khademi, F., Akbari, M., Jamal, S.M. and Nikoo, M. (2017), "Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete", Front. Struct. Civ. Eng., 11(1), 90-99. https://doi.org/10.1007/s11709-016-0363-9.
  32. Khorrami, R. and Derakhshani, A. (2019), "Estimation of ultimate bearing capacity of shallow foundations resting on cohesionless soils using a new hybrid M5'-GP model", Geomech. Eng., 19(2), 127-139. https://doi.org/10.12989/gae.2019.19.2.127.
  33. Kurnaz, T. F. and Kaya, Y. (2018), "The comparison of the performance of ELM, BRNN, and SVM methods for the prediction of compression index of clays", Arab. J. Geosci., 11(24), 770. https://doi.org/10.1007/s12517-018-4143-9.
  34. Luat, N.V., Lee, J., Lee, D.H. and Lee, K. (2020d), "GS-MARS method for predicting the ultimate load-carrying capacity of rectangular CFST columns under eccentric loading", Comput. Concrete, 25(1), 1-14. doi: https://doi.org/10.12989/cac.2020.25.1.001.
  35. Luat, N.V., Lee, K. and Thai, D.K. (2020a), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", Geomech. Eng., 20(5), 385-397. https://doi.org/10.12989/gae.2020.20.5.385.
  36. Luat, N.V., Nguyen, V.Q., Lee, S., Woo, S. and Lee, K. (2020c), "An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils", Geomech. Eng., 21(6), 583-598. https://doi.org/10.12989/gae.2020.21.6.583.
  37. Luat, N.V., Shin, J. and Lee, K. (2020b), "Hybrid BART-based models optimized by nature-inspired metaheuristics to predict ultimate axial capacity of CCFST columns", Eng. Comput., 1-30. https://doi.org/10.1007/s00366-020-01115-7.
  38. Meyerhof, G.G. (1963), "Some recent research on the bearing capacity of foundations", Can. Geotech. J., 1(1), 16-26. https://doi.org/10.1139/t63-003.
  39. Milborrow, S. (2019), Package "earth", R Software Package.
  40. Muduli, P.K. and Das, S.K. (2015), "Model uncertainty of SPT-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming", Soils Found., 55(2), 258-275. https://doi.org/10.1016/j.sandf.2015.02.003.
  41. Muduli, P.K., Das, M.R., Das, S.K. and Senapati, S. (2015), "Lateral load capacity of piles in clay using genetic programming and multivariate adaptive regression spline", Ind. Geotech. J., 45(3), 349-359. https://doi.org/10.1007/s40098-014-0142-2.
  42. Naderpour, H., Poursaeidi, O. and Ahmadi, M. (2018), "Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks", Measurement, 126, 299-308. https://doi.org/10.1016/j.measurement.2018.05.051.
  43. Naderpour, H., Rafiean, A.H. and Fakharian, P. (2018), "Compressive strength prediction of environmentally friendly concrete using artificial neural networks", J. Build. Eng., 16, 213-219. https://doi.org/10.1016/j.jobe.2018.01.007.
  44. Nguyen, K.T., Nguyen, Q.D., Le, T.A., Shin, J. and Lee, K. (2020), "Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches", Constr. Build. Mater., 247, 118581. https://doi.org/10.1016/j.conbuildmat.2020.118581.
  45. Nguyen, M.S.T., Thai, D.K. and Kim, S.E. (2020), "Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network", Steel Compos. Struct., 35(3), 415-437. https://doi.org/10.12989/scs.2020.35.3.415.
  46. Olden, J.D., Joy, M.K. and Death, R.G. (2004), "An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data", Ecol. Modell., 178(3-4), 389-397. https://doi.org/10.1016/j.ecolmodel.2004.03.013.
  47. Omar, M., Hamad, K., Al Suwaidi, M. and Shanableh, A. (2018), "Developing artificial neural network models to predict allowable bearing capacity and elastic settlement of shallow foundation in Sharjah, United Arab Emirates", Arab. J. Geosci., 11(16), 464. https://doi.org/10.1007/s12517-018-3828-4.
  48. OpenSees [Computer Program]. Open System for Earthquake Engineering Simulation, Pacific Earthquake Engineering Research Center (PEER), University of California, Berkeley, U.S.A. http,//OpenSees.berkeley.edu.
  49. Patra, C., Behera, R., Sivakugan, N. and Das, B. (2012), "Ultimate bearing capacity of shallow strip foundation under eccentrically inclined load, Part I", Int. J. Geotech. Eng., 6(3), 343-352. https://doi.org/10.3328/IJGE.2012.06.03.343-352 .
  50. Rabiei, M. and Choobbasti, A.J. (2020), "Innovative piled raft foundations design using artificial neural network", Front. Struct. Civ. Eng., 14(1), 138-146. https://doi.org/10.1007/s11709-019-0585-8.
  51. Raychowdhury, P. (2008), "Nonlinear Winkler-based shallow foundation model for performance assessment of seismically loaded structures", Ph. D Dissertation. University of California, San Diego, San Diego, California, U.S.A.
  52. Raymond, G.P. and Komos, F.E. (1978), "Repeated load testing of a model plane strain footing", Can. Geotech. J., 15(2), 190-201. https://doi.org/10.1139/t78-019.
  53. Sahu, R., Patra, C.R., Das, B.M. and Sivakugan, N. (2016), "Bearing capacity of shallow strip foundation on geogrid-reinforced sand subjected to inclined load", Int. J. Geotech. Eng., 10(2), 183-189. https://doi.org/10.1080/19386362.2015.1105622.
  54. Samadi, M., Jabbari, E., Azamathulla, H.M. and Mojallal, M. (2015), "Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks", Eng. Appl. Comput. Fluid Mech. 9(1), 291-300. https://doi.org/10.1080/19942060.2015.1011826.
  55. Samui, P. (2008), "Support vector machine applied to settlement of shallow foundations on cohesionless soils", Comput. Geotech., 35(3), 419-427. https://doi.org/10.1016/j.compgeo.2007.06.014.
  56. Samui, P., Sitharam, T.G. and Kurup, P.U. (2008), "OCR prediction using support vector machine based on piezocone data", J. Geotech. Geoenviron. Eng., 134(6), 894-898. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:6(894).
  57. Sasmal, S.K. and Behera, R.N. (2018), "Prediction of combined static and cyclic load-induced settlement of shallow strip footing on granular soil using artificial neural network", Int. J. Geotech. Eng. https,//doi.org/10.1080/19386362.2018.1557384.
  58. Searson, D.P. (2009), GPTIPS, Genetic Programming and Symbolic Regression for MATLAB.
  59. Searson, D.P., Leahy, D.E. and Willis, M.J. (2010), "GPTIPS an open source genetic programming toolbox for multigene symbolic regression", Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong, China, March.
  60. Shahin, M.A. (2015), "A review of artificial intelligence applications in shallow foundations", Int. J. Geotech. Eng., 9(1), 49-60. https://doi.org/10.1179/1939787914Y.0000000058.
  61. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2002), "Artificial neural network based settlement prediction formula for shallow foundations on granular soils", Australian Geomech. J. News Australian Geomech. Soc., 37(4), 45-52.
  62. Smola, A.J. and Scholkopf, B. (2014), "A tutorial on support vector regression", Statist. Comput., 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.
  63. Tran, V. L., Thai, D.K. and Kim, S.E. (2019b) "A new empirical formula for prediction of the axial compression capacity of CCFT columns", Steel Compos. Struct., 33(2), 181-194. https://doi.org/10.12989/scs.2019.33.2.181.
  64. Tran, V. L., Thai, D.K. and Nguyen, D.D. (2020), "Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete", Thin-Walled Struct., 151, 106720. https://doi.org/10.1016/j.tws.2020.106720.
  65. Tran, V.L., Thai, D.K. and Kim, S.E. (2019a), "Application of ANN in predicting ACC of SCFST column", Compos. Struct., 228, 111332. https://doi.org/10.1016/j.compstruct.2019.111332.
  66. Vapnik, V., Golowich, S.E. and Smola, A.J. (1997), Support Vector Method for Function Approximation, Regression Estimation and Signal Processing, in Advances in Neural Information Processing Systems, 281-287.
  67. Won, J. and Shin, J. (2021), "Machine Learning-Based Approach for Seismic Damage Prediction Method of Building Structures Considering Soil-Structure Interaction", Sustainability, 13(8), 4334. https://doi.org/10.3390/su13084334.
  68. Xu, J., Ren, Q. and Shen, Z. (2017), "Sensitivity analysis of the influencing factors of slope stability based on LS-SVM", Geomech. Eng., 13(3), 447-458. https://doi.org/10.12989/gae.2017.13.3.447.
  69. Zhang, C., Ji, J., Gui, Y., Kodikara, J., Yang, S. Q. and He, L. (2016), "Evaluation of soil-concrete interface shear strength based on LS-SVM", Geomech. Eng., 11(3), 361-372. http://doi.org/10.12989/gae.2016.11.3.361.
  70. Zhang, W. and Goh, A.T. (2016), "Multivariate adaptive regression splines and neural network models for prediction of pile drivability", Geosci. Front., 7(1), 45-52. https://doi.org/10.1016/j.gsf.2014.10.003.