DOI QR코드

DOI QR Code

An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils

  • Luat, Nguyen-Vu (Department of Architectural Engineering, Sejong University) ;
  • Nguyen, Van-Quang (Department of Civil and Environmental Engineering, Hanyang University) ;
  • Lee, Seunghye (Department of Architectural Engineering, Sejong University) ;
  • Woo, Sungwoo (TechSquare Ltd.) ;
  • Lee, Kihak (Department of Architectural Engineering, Sejong University)
  • 투고 : 2020.01.13
  • 심사 : 2020.05.18
  • 발행 : 2020.06.25

초록

This study is attempted to propose a new hybrid artificial intelligence model called integrative genetic algorithm with multivariate adaptive regression splines (GA-MARS) for settlement prediction of shallow foundations on sandy soils. In this hybrid model, the evolution algorithm - Genetic Algorithm (GA) was used to search and optimize the hyperparameters of multivariate adaptive regression splines (MARS). For this purpose, a total of 180 experimental data were collected and analyzed from available researches with five-input variables including the bread of foundation (B), length to width (L/B), embedment ratio (Df/B), foundation net applied pressure (qnet), and average SPT blow count (NSPT). In further analysis, a new explicit formulation was derived from MARS and its accuracy was compared with four available formulae. The attained results indicated that the proposed GA-MARS model exhibited a more robust and better performance than the available methods.

키워드

과제정보

This research was supported by Ministry of Land, Infrastructure and Transport of Korean Government (Grant 20CTAP-C143093-03).

참고문헌

  1. Abate, G., Caruso, C., Massimino, M.R. and Maugeri, M. (2008), "Evaluation of shallow foundation settlements by an elasto-plastic kinematic-isotropic hardening numerical model for granular soil", Geomech. Geoeng., 3(1), 27-40. https://doi.org/10.1080/17486020701862174.
  2. Alpan, I., (1964), "Introductory soil mechanics and foundations / G.B. Sowers, G.F. Sowers", Civ. Eng. Public Work. Rev., 58, 1415-1418. https://doi.org/10.1097/00010694-195111000-0014.
  3. Anagnostopoulos, A.G., Papadopoulos, B.P. and Kavvadas, M.J. (1991), "Direct estimation of settlements on sand, based on SPT results", Proceedings of the 10th European Conference on Soil Mechacnics and Foundation Engineering, Florence, Italy.
  4. Anderson, J.B., Townsend, F.C. and Rahelison, L. (2007), "Load testing and settlement prediction of shallow foundation", J. Geotech. Geoenviron. Eng., 133(12), 1494-1502. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:12(1494).
  5. Arnold, M. (1980), "Prediction of footing settlement on sand", Ground Eng., 13, 40-47.
  6. Briaud, J.L. and Gibbens, R.M. (1994), "Predicted and measured behavior of five spread footings on sand", Geotech. Sp. Publ., 41, 255.
  7. 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.
  8. Bui, D.K., Nguyen, T.N., Ngo, T.D. and Nguyen-Xuan, H. (2019), "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings", Energy, 190, 116370. https://doi.org/10.1016/j.energy.2019.116370.
  9. Burland, J.B. and Burbidge, M.C. (1985), "Settlement of foundations on sand and gravel", Proc. Inst. Civ. Eng., 78(6), 1325-1381.
  10. Cheng, M.Y. and Cao M.T. (2014), "Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams", Eng. Appl. Artif. Intell., 28, 86-96. https://doi.org/10.1016/j.engappai.2013.11.001.
  11. Craven, P. and Wahba, G. (1978), "Smoothing noisy data with spline functions", Numer. Math., 31(4), 377-403. https://doi.org/10.1007/BF01404567.
  12. 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/IJGE.2007.01.01.19-29.
  13. Das, B.M. (2002), Principles of Foundation Engineering, Cengage Learning, U.S.A.
  14. Elton, D.J. (1987), "Settlement of footings on sand by CPT data", J. Comput. Civ. Eng., 1(2), 99-113. https://doi.org/10.1061/(ASCE)0887-3801(1987)1:2(99).
  15. Friedman, J.H. (1991), "Multivariate adaptive regression splines", Ann. Statics, 19(1), 1-67.
  16. Gandomi, A.H., Alavi, A.H. and Sahab, M.G. (2010), "New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming", Mater. Struct., 43(7), 963-983. https://doi.org/10.1617/s11527-009-9559-y.
  17. Gesoglu, M. and Guneyisi, E. (2007), "Prediction of load-carrying capacity of adhesive anchors by soft computing techniques", Mater. Struct., 40(9), 939-951. https://doi.org/10.1617/s11527-007-9265-6.
  18. Golafshani, E.M., Rahai, A. and Sebt, M.H. (2015), "Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete", Mater. Struct., 48(5), 1581-1602. https://doi.org/10.1617/s11527-014-0256-0.
  19. Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, 1st Ed., Addison-Wesley Longman Publishing Co., Inc., Boston, Massachusetts, U.S.A.
  20. Gomes, G.F., de Almeida, F.A., Junqueira, D.M., da Cunha Jr, S. S. and Ancelotti Jr, A.C. (2019), "Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods", Eng. Struct., 181, 111-123. https://doi.org/10.1016/j.engstruct.2018.11.081.
  21. Harr, M.E. (1966), Foundation and Theoretical Soil Mechanics, McGraw-Hill, New York, U.S.A.
  22. Holland, J.H. (1975), Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, U Michigan Press, Oxford, England, U.K.
  23. Iyer, V.H., Mahesh, S., Malpani, R., Sapre, M. and Kulkarni, A.J. (2019), "Adaptive range genetic algorithm: A hybrid optimization approach and its application in the design and economic optimization of shell-and-tube heat exchanger", Eng. Appl. Artif. Intell., 85, 444-461. https://doi.org/10.1016/j.engappai.2019.07.001.
  24. Jeyepalan, J.K. and Boehm, R. (1986), Procedures for Predicting Settlement in Sands, in Settlement of Shallow Foundations on Cohessionless Soils: Design and Performance, American Society of Civil Engineers, 1-22.
  25. Koopialipoor, M., Fallah, A., Armaghani, D.J., Azizi, A. and Mohamad, E.T. (2019), "Three hybrid intelligent models in estimating flyrock distance resulting from blasting", Eng. Comput., 35(1), 243-256. https://doi.org/10.1007/s00366-018-0596-4.
  26. Le-Duc, T., Nguyen, Q.H. and Nguyen-Xuan, H. (2020), "Balancing composite motion optimization", Inform. Sci., 520, 250-270. https://doi.org/10.1016/j.ins.2020.02.013.
  27. Luat, N.V., Lee, J., Lee, D.H. and Lee, K. (2020b), "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.
  28. 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.
  29. Maugeri, M., Castelli, F., Massimino, M.R. and Verona, G. (1998), "Observed and computed settlements of two shallow foundations on sand", J. Geotech. Geoenviron. Eng., 124(7), 595-605. https://doi.org/10.1061/(ASCE)1090-0241(1998)124:7(595).
  30. Meyerhof, G. (1956), "Penetration tests and bearing capacity of cohesionless soils", J. Soil Mech. Found. Div., 82(1), 1-12.
  31. Meyerhof, G. (1964), "Shallow foundations", J. Soil Mech. Found. Div., 91, 21-32. https://doi.org/10.1061/JSFEAQ.0000719
  32. Meyerhof, 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.
  33. Mullins, G., Winters, D. and Dapp, S. (2006), "Predicting end bearing capacity of post-grouted drilled shaft in cohesionless soils", J. Geotech. Geoenviron. Eng., 132(4), 478-487. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:4(478).
  34. Nehdi, M. and Nikopour, H. (2011), "Genetic algorithm model for shear capacity of RC beams reinforced with externally bonded FRP", Mater. Struct., 44(7), 1249-1258. https://doi.org/10.1617/s11527-010-9697-2.
  35. Pramanik, P. and Maiti, M.K. (2019), "An inventory model for deteriorating items with inflation induced variable demand under two level partial trade credit: A hybrid ABC-GA approach", Eng. Appl. Artif. Intell., 85, 194-207. https://doi.org/10.1016/j.engappai.2019.06.013.
  36. Qi, C. and Tang, X. (2018), "A hybrid ensemble method for improved prediction of slope stability", Int. J. Numer. Anal. Meth. Geomech., 42(15), 1823-1839. https://doi.org/10.1002/nag.2834.
  37. Ren, Y. and Bai, G. (2010), "Determination of optimal SVM parameters by using GA/PSO", J. Comput., 5(8), 1160-1168. https://doi.org/10.4304/jcp.5.8.1160-1168.
  38. Samui, P. (2011), "Multivariate adaptive regression spline applied to friction capacity of driven piles in clay", Geomech. Eng., 3(4), 285-290. https://doi.org/10.12989/gae.2011.3.4.285.
  39. Samui, P. and Sitharam, T.G. (2008), "Least-square support vector machine applied to settlement of shallow foundations on cohesionless soils", Int. J. Numer. Anal. Meth. Geomech., 32(17), 2033-2043. https://doi.org/10.1002/nag.731.
  40. 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
  41. Schultze, E. and Sherif, G. (1973), "Prediction of settlements from evaluated settlement observations for sand", Proceeding of the 8th International Conference on Soil Mechanics and Foundation Engineering, Moscow, Russia, August.
  42. Shahin, M., Jaksa, M.B. and Maier, H.R. (2005), "Neural network based stochastic design charts for settlement prediction", Can. Geotech. J., 120, 110-120. https://doi.org/10.1139/T04-096.
  43. Shahin, M.A. and Jaksa, M.B. (2006), "Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks", Can. Geotech. J., 43(6), 626-637. https://doi.org/10.1139/t06-029.
  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. 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.
  46. Tan, C.K. and Duncan, J.M. (1991), "Settlement of footings on sands: Accuracy and reliability", Proceedings of the Geotechnical Engineering Congress, Boulder, Colorado, U.S.A., June.
  47. Terzaghi, K. and Peck, R.B. (1968), Soil Mechanics in Engineering Practice, John Wiley & Sons, New York, U.S.A.
  48. Tiachacht, S., Bouazzouni, A., Khatir, S., Abdel Wahab, M., Behtani, A. and Capozucca, R. (2018), "Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm", Eng. Struct., 177, 421-430. https://doi.org/10.1016/j.engstruct.2018.09.070.
  49. Wang, Y., Huang, H., Huang, L. and Zhang, X. (2018), "Source term estimation of hazardous material releases using hybrid genetic algorithm with composite cost functions", Eng. Appl. Artif. Intell., 75, 102-113. https://doi.org/10.1016/j.engappai.2018.08.005.
  50. Xiang, Y., Goh, A.T.C., Zhang, W. and Zhang, R. (2018), "A multivariate adaptive regression splines model for estimation of maximum wall deflections induced by braced excavation", Geomech. Eng., 14(4), 315-324. https://doi.org/https://doi.org/10.12989/gae.2018.14.4.315.
  51. Zhang, W. and Goh, A.T.C. (2014), "Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns", Geomech. Eng., 7(4), 431-458. https://doi.org/10.12989/gae.2014.7.4.431.
  52. Zhang, W., Zhang, R. and Goh, A.T.C. (2018), "MARS inverse analysis of soil and wall properties for braced excavations in clays", Geomech. Eng., 16(6), 577-588. https://doi.org/10.12989/gae.2018.16.6.577.
  53. Zhang, W., Zhang, R., Wang, W., Zhang, F. and Goh, A.T.C. (2019), "A multivariate adaptive regression splines model for determining horizontal wall deflection envelope for braced excavations in clays", Tunn. Undergr. Sp. Technol., 84, 461-471. https://doi.org/10.1016/j.tust.2018.11.046.

피인용 문헌

  1. Seismic Fragility Assessment of Columns in a Piloti-Type Building Retrofitted with Additional Shear Walls vol.12, pp.16, 2020, https://doi.org/10.3390/su12166530
  2. 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
  3. 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