Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

On the prediction of unconfined compressive strength of silty soil stabilized with bottom ash, jute and steel fibers via artificial intelligence

  • Received : 2015.01.12
  • Accepted : 2016.11.15
  • Published : 2017.03.30
⟨ Previous Next ⟩

Abstract

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

Keywords

References

  1. Aggarwal, P. and Sharma, B. (2010), "Application of jute fiber in the improvement of subgrade characteristics", Proceedings of 9th International Congress on Advances in Civil Engineering, Trabzon, Turkey, September.
  2. Akbulut, S., Kalkan, E. and Celik, S. (2003), "Artificial Neural Networks to estimate the shear strength of compacted soil samples", International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, Lefkosa, TRNC, May.
  3. Akbulut, S., Hasiloglu, A.S. and Pamukcu, S. (2004), "Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system", Soil Dyn. Earthq. Eng., 24(11), 805-814. https://doi.org/10.1016/j.soildyn.2004.04.006
  4. ASTM D-1557 (2012), Standard Test Methods for Laboratory Compaction Characteristics of Soil Using Modified Effort.
  5. ASTM D-2166 (2006), Standard Test Method for Unconfined Compressive Strength of Cohesive Soil.
  6. Baykasoglu, A., Gullu, H., Canakci, H. and Ozbakir, L. (2008), "Prediction of compressive and tensile strength of limestone via genetic programming", Expert. Syst. Appl., 35(1-2), 111-123. https://doi.org/10.1016/j.eswa.2007.06.006
  7. Broomhead, D.S. and Lowe, D. (1988), "Multi-variable functional interpolation and adaptive networks", Complex Syst., 2, 321-355.
  8. Cal, V. (1995), "Soil classification by neural-network", Adv. Eng. Softw., 22(2), 95-97. https://doi.org/10.1016/0965-9978(94)00035-H
  9. Canakci, H., Baykasoglu, A. and Gullu, H. (2009), "Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming", Neural Comput. Appl., 18, 1031-1041. https://doi.org/10.1007/s00521-008-0208-0
  10. Cigizoglu, H.K. (2003), "Estimation, forecasting and extrapolation of river flows by artificial neural networks", J. Hydrol. Sci., 48(3), 349-361. https://doi.org/10.1623/hysj.48.3.349.45288
  11. Cigizoglu, H.K. and Kisi, O. (2005), "Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data", Nordic Hydrol., 36(1), 49-64. https://doi.org/10.2166/nh.2005.0005
  12. Cybenco, G. (1989), "Approximation by superposition of a sigmoidal function", Math. Control Signals Syst., 2(4), 303-314. https://doi.org/10.1007/BF02551274
  13. Dayakar, P and Rongda, Z. (1999), "Triaxial compression behavior of sand and gravel using artificial neural networks (ANN)", Comput. Geotech., 24(3), 207-230. https://doi.org/10.1016/S0266-352X(99)00002-6
  14. Ellis, G.W., Yao, C., Zhao, R. and Penumadu, D. (1995), "Stress strain modeling of sands using artificial neural networks", ASCE J. Geotech. Eng., 121(5), 429-435. https://doi.org/10.1061/(ASCE)0733-9410(1995)121:5(429)
  15. El-Bakyr, M.Y. (2003), "Feed forward neural networks modelling for K-P interactions", Chaos Solitons Fractals, 18(5), 995-1000. https://doi.org/10.1016/S0960-0779(03)00068-7
  16. 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., Int. J., 6(1), 1-15. https://doi.org/10.12989/gae.2014.6.1.001
  17. Erzin, Y. and Gul, T.O. (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., Int. J., 5(6), 541-564. https://doi.org/10.12989/gae.2013.5.6.541
  18. Ferguson, C.J. (2009), "An effect size primer: A guide for clinicians and researchers", Prof. Psychol.: Res. Pract., 40(5), 532-538. https://doi.org/10.1037/a0015808
  19. Garson, G.D. (1991), "Interpreting neural network connection weights", AI Expert, 6, 47-51.
  20. Gencel, O., Koksal, F., Sahin, M., Durgun, M.Y., Hagg Lobland, H.E. and Brostow, W. (2013), "Modeling of thermal conductivity of concrete with vermiculite by using artificial neural network approaches", Exp. Heat Transf., 26(4), 360-383. https://doi.org/10.1080/08916152.2012.669810
  21. Gevrey, M., Dimopoulos, I. and Lek, S. (2003), "Review and comparison of methods to study the contribution of variables in artificial neural network models", Ecol. Model., 160(3), 249-264. https://doi.org/10.1016/S0304-3800(02)00257-0
  22. Ghazavi, M. and Roustaie, M. (2010), "The influence of freeze-thaw cycles on the unconfined compressive strength of fiber-reinforced clay", Cold Reg. Sci. Technol., 61(2-3), 125-131. https://doi.org/10.1016/j.coldregions.2009.12.005
  23. Goh, A.T.C. (1995), "Back-propagation neural networks for modeling complex systems", Artif. Intell. Eng., 9(3), 143-151. https://doi.org/10.1016/0954-1810(94)00011-S
  24. Goktepe, A.B., Altun, S., Altintas, G. and Tan, O. (2008), "Shear strength estimation of plastic clays with statistical and neural approaches", Build. Environ., 43(5), 849-860. https://doi.org/10.1016/j.buildenv.2007.01.022
  25. Gray, H. and Al-Refeai, T. (1986), "Behavior of fabric versus fiber reinforced sand", ASCE J. Geotech. Eng., 112(8), 804-820. https://doi.org/10.1061/(ASCE)0733-9410(1986)112:8(804)
  26. Gullu, H. (2012), "Prediction of peak ground acceleration by genetic expression programming and regression: A comparison using likelihood-based measure", Eng. Geol., 141-142, 92-113. https://doi.org/10.1016/j.enggeo.2012.05.010
  27. Gullu, H. (2013), "On the prediction of shear wave velocity at local site of strong ground motion stations: an application using artificial intelligence", Bull. Earthq. Eng., 11(4), 969-997. https://doi.org/10.1007/s10518-013-9425-8
  28. Gullu, H. (2014), "Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom ash", Eng. Appl. Artif. Intell., 35, 143-157. https://doi.org/10.1016/j.engappai.2014.06.020
  29. Gullu, H. and Ercelebi, E. (2007), "A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey", Eng. Geol., 93(3-4), 65-81. https://doi.org/10.1016/j.enggeo.2007.05.004
  30. Gullu, H. and Girisken, S. (2013), "Performance of fine-grained soil treated with industrial wastewater sludge", Environ. Earth Sci., 70(2), 777-788. https://doi.org/10.1007/s12665-012-2167-0
  31. Gullu, H. and Hazirbaba, K. (2010), "Unconfined compressive strength and post-freeze-thaw behavior of fine-grained soils treated with geofiber and synthetic fluid", Cold. Reg. Sci. Technol., 62(2-3), 142-150. https://doi.org/10.1016/j.coldregions.2010.04.001
  32. Gullu, H. and Khudir, A. (2014), "Effect of freeze-thaw cycles on unconfined compressive strength of finegrained soil treated with jute fiber, steel fiber and lime", Cold Reg. Sci. Technol., 106-107, 55-65. https://doi.org/10.1016/j.coldregions.2014.06.008
  33. Habibagahi, G. and Bamdad, A. (2003), "A neural network framework for mechanical behavior of unsaturated soils", J. Can. Geotech., 40(3), 684-693. https://doi.org/10.1139/t03-004
  34. Hagan, M.T. and Menhaj, M.B. (1994), "Training feed forward networks with the Marquardt algorithm", IEEE Trans. Neural Netw., 5(6), 989-993. https://doi.org/10.1109/72.329697
  35. Hazirbaba, K. and Gullu, H. (2010), "California bearing ratio improvement and freeze-thaw performance of fine-grained soils treated with geofiber and synthetic fluid", Cold. Reg. Sci. Technol., 63(1-2), 50-60. https://doi.org/10.1016/j.coldregions.2010.05.006
  36. Haykin, S. (1998), Neural networks: A Comprehensive Foundation, (2nd Edition), Prentice-Hall, Upper Saddle River, NJ, USA.
  37. Hejazi, S.M., Sheikhzadeh, M., Abtahi, S.M. and Zadhoush, A. (2012), "A simple review of soil reinforcement by using natural and synthetic fibers", Constr. Build. Mater., 30, 100-116. https://doi.org/10.1016/j.conbuildmat.2011.11.045
  38. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feed forward networks are universal approximators", Neural Netw., 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  39. Hossain, K.M.A. and Mol, L. (2011), "Some engineering properties of stabilized clayey soils incorporating natural pozzolans and industrial wastes", Constr. Build. Mater., 25(8), 3495-3501. https://doi.org/10.1016/j.conbuildmat.2011.03.042
  40. Islam, M. and Iwashita, K. (2010), "Earthquake resistance of adobe reinforced by low cost traditional materials", J. Nat. Disaster Sci., 32(1), 1-21. https://doi.org/10.2328/jnds.32.1
  41. Jang, J.S.R. (1993), "ANFIS: adaptive network based fuzzy inference system", IEEE Trans. Syst. Manag. Cybern., 23(3), 665-685. https://doi.org/10.1109/21.256541
  42. Jones, C.W. (1987), "Long term changes in the properties of soil linings for canal seepage control", Report No. REC-ERC-87-1; U.S. Department of the Interior, Bureau of Reclamation, Engineering and Research Center, Denver, CO, USA.
  43. Kalkan, E., Akbulut, S., Tortum, A. and Celik, S. (2009), "Prediction of the unconfined compressive strength of compacted granular soils by using inference systems", Environ. Geol., 58(7), 1429-1440. https://doi.org/10.1007/s00254-008-1645-x
  44. Karunanithi, N., Grenney, W.J., Whitley, D. and Bovee, K. (1994), "Neural networks for river flow prediction", ASCE J. Comput. Civ. Eng., 8(2), 201-220. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(201)
  45. Kayabali, K. and Bulus, G. (2000), "The usability of bottom ash as an engineering material when amended with different matrices", Eng. Geol., 56(3-4), 293-303. https://doi.org/10.1016/S0013-7952(99)00097-6
  46. Kayadelen, C., Gunaydin, O., Fener, M., Demir, A. and Ozvan, A. (2009), "Modeling of the angle of shearing resistance of soils using soft computing systems", Expert Syst. Appl., 36(9), 11814-11826. https://doi.org/10.1016/j.eswa.2009.04.008
  47. Khudir, A. (2014), "Effect of freeze-thaw cycles on the strength of fine-grained soil stabilized with bottom ash, lime, jute fiber and steel fiber", M.Sc. Dissertation; University of Gaziantep, Gaziantep, Turkey.
  48. Kim, B. (2003), "Properties of coal ash mixtures and their use in highway embankments", Ph.D. Dissertation; Purdue University, West Lafayette, IN, USA.
  49. Kim, B., Kim, S. and Kim, K. (2003), "Modelling of plasma etching using a generalized regression neural network", Vac., 71(4), 497-503. https://doi.org/10.1016/S0042-207X(03)00075-7
  50. Kim, Y.T., Lee, C. and Park, H.I. (2011), "Experimental study on engineering characteristics of composite geomaterial for recycling dredged soil and bottom ash", Mar. Georesour. Geotech., 29(1), 1-15. https://doi.org/10.1080/1064119X.2010.514237
  51. Kisi, O. (2008), "The Potential of different ANN techniques in evapotranspiration modeling", Hydrol. Processes, 22(14), 2449-2460. https://doi.org/10.1002/hyp.6837
  52. Kisi, O. (2009), "Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks", Hydrol. Processes, 23(2), 213-223. https://doi.org/10.1002/hyp.7126
  53. Kisi, O. and Cobaner, M. (2009), "Modeling river stage-discharge relationships using different neural network computing techniques", Clean Soil Air Water, 37(2), 160-169. https://doi.org/10.1002/clen.200800010
  54. Kisi, O. and Fedakar, H.I. (2014), "Modeling of suspended sediment concentration carried in natural streams using fuzzy genetic approach", Comput. Intell. Tech. Earth Environ. Sci., 175-196.
  55. Kiszka, J.B., Kochanskia, M.E. and Sliwinska, D.S. (1985), "The influence of some fuzzy implication operators on the accuracy of fuzzy model-part II", Fuzzy Sets Syst., 15(3), 223-240. https://doi.org/10.1016/0165-0114(85)90016-8
  56. Kocabas, F. and Unal, S. (2010), "Compared techniques for the critical submergence of an intake in water flow", Adv. Eng. Softw., 41(5), 802-809. https://doi.org/10.1016/j.advengsoft.2009.12.021
  57. Lambe, T.W. and Kaplar, T.W. (1971a), "Additives for modifying the frost susceptibility of soils", Technical Report No. 123, Part 1, USA Cold Regions Research and Engineering Laboratory, Hanover, NH, USA.
  58. Lambe, T.W., Kaplar, C.W. and Lambe, T.J. (1971b), "Additives for modifying the frost susceptibility of soils", Technical Report No. 123; Part 2, USA Cold Regions Research and Engineering Laboratory, Hanover, NH, USA.
  59. Lee, G.C. and Chang, S.H. (2003), "Radial basis function networks applied to DNBR calculation in digital core protection systems", Ann. Nucl. Energy, 30(15), 1561-1572. https://doi.org/10.1016/S0306-4549(03)00099-9
  60. Lee, S.J., Lee, S.R. and Kim, Y.S. (2003), "An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation", Comput. Geotech., 30(6), 489-503. https://doi.org/10.1016/S0266-352X(03)00058-2
  61. Leonard, J.A., Kramer, M.A. and Unga, L.H. (1992), "Using radial basis functions to approximate a function and its error bounds", IEEE Trans. Neural Netw., 3(4), 624-627. https://doi.org/10.1109/72.143377
  62. Levine, E.R., Kimes, D.S. and Sigillito, V.G. (1996), "Classification soil structure using neural networks", Ecol. Model., 92(1), 101-108. https://doi.org/10.1016/0304-3800(95)00199-9
  63. Looney, C.G. (1996), "Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes", IEEE Trans. Knowl. Data. Eng., 8(2), 211-226. https://doi.org/10.1109/69.494162
  64. Marquardt, D. (1963), "An algorithm for least squares estimation of non-linear parameters", J. Soc. Ind. Appl. Math., 11(2), 431-441. https://doi.org/10.1137/0111030
  65. Modi, O.P., Mondal, D.P., Prasad, B.K., Singh, M. and Khaira, H.K. (2003), "Abrasive wear behavior of high carbon steel: Effects of microstructure and experimental parameters and correlation with mechanical properties", Mater. Sci. Eng.:A, 343(1-2), 235-242. https://doi.org/10.1016/S0921-5093(02)00384-2
  66. Murray, T. and Farrar, M. (1988), "Temperature distributions in reinforced soil retaining walls", Geotext. Geomembr., 7(1-2), 33-50. https://doi.org/10.1016/0266-1144(88)90017-9
  67. Najjar, Y.M., Basheer, I.A. and Naous, W.A. (1996), "On the identification of compaction characteristics by neuronets", Comput. Geotech., 18(3), 167-187. https://doi.org/10.1016/0266-352X(95)00030-E
  68. Narendra, B.S., Sivapullaiah, P.V., Suresh, S. and Omkar, S.N. (2006), "Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study", Comput. Geotech., 33(3), 196-208. https://doi.org/10.1016/j.compgeo.2006.03.006
  69. Poggio, T. and Girosi, F. (1990), "Regularization algorithms for learning that are equivalent to multilayer networks", Sci., 247(4945), 978-982. https://doi.org/10.1126/science.247.4945.978
  70. Rifai, A., Yasufuku, N. and Tsuji, K. (2009), "Characterization and effective utilization of coal ash as soil stabilization on road application", In: (C.F. Leung, J. Chu and R.F. Shen Eds.), Ground Improvement Technologies and Case Histories, Research Publishing Services, pp. 469-474.
  71. Rumsey, D.J. (2011), Statistics for Dummies, (2nd Edition), John Wiley & Sons, NJ, USA.
  72. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2001), "Artificial neural network applications in geotechnical engineering", Aust. Geomech., 36(1), 49-62.
  73. Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2003), "Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models", Comput. Geotech., 30(8), 637-647. https://doi.org/10.1016/j.compgeo.2003.09.004
  74. Sinha, S.K. and Wang, M.C. (2008), "Artificial neural network prediction models for soil compaction and permeability", Geotech. Geol. Eng., 26(1), 47-64. https://doi.org/10.1007/s10706-007-9146-3
  75. 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., Int. J., 1(4), 307-321. https://doi.org/10.12989/gae.2009.1.4.307
  76. Sivrikaya, O., Kiyildi, K.R. and Karaca, Z. (2014), "Recycling waste from natural stone processing plants to stabilise clayey soil", Environ. Earth Sci., 71(10), 4397-4407. https://doi.org/10.1007/s12665-013-2833-x
  77. Specht, D.F. (1991), "A general regression neural network", IEEE Trans. Neural Netw., 2(6), 568-576. https://doi.org/10.1109/72.97934
  78. Stegemann, J.A. and Buenfeld, N.R. (2003), "Prediction of unconfined compressive strength of cement paste containing industrial wastes", Waste Manag., 23(4), 321-332. https://doi.org/10.1016/S0956-053X(02)00062-4
  79. Tsoukalas, L.H. and Uhrig, R.E. (1997), Fuzzy and Neural Approaches in Engineering, Wiley, NY, USA.
  80. Tutumluer, E. and Seyhan, U. (1998), "Neural network modeling of anisotropic aggregate behavior from repeated load triaxial tests", J. Transport. Res. Board, 1615, 86-93. https://doi.org/10.3141/1615-12
  81. Tutumluer, E., Santoni, R.L. and Kim, I.T. (2004), "Modulus anisotropy and shear stability of geofiberstabilized sands", Transportation Research Board, National Research Council; Transportation Research Record 1874, Washington, D.C., USA, pp. 125-135. https://doi.org/10.3141/1874-14
  82. Unal, B., Mamak, M., Seckin, G. and Cobaner, M. (2010), "Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels", Adv. Eng. Softw., 41(2), 120-129. https://doi.org/10.1016/j.advengsoft.2009.10.002
  83. Wasserman, P.D. (1993), Advanced Methods in Neural Computing, Van Nostrand Reinhold, NY, USA.
  84. Yilmaz, I and Kaynar, O. (2011), "Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils", Expert Syst. Appl., 38(5), 5958-5966. https://doi.org/10.1016/j.eswa.2010.11.027
  85. Zaimoglu, A.S. (2010), "Freezing-thawing behavior of fine-grained soils reinforced with polypropylene fibers", Cold Reg. Sci. Technol., 60(1), 63-65. https://doi.org/10.1016/j.coldregions.2009.07.001

Cited by

  1. Effect of Fine-Grain Percent on Soil Strength Properties Improved By Biological Method vol.35, pp.8, 2018, https://doi.org/10.1080/01490451.2018.1454554
  2. Strength retrieval of artificially cemented bauxite residue using machine learning: an alternative design approach based on response surface methodology pp.1433-3058, 2018, https://doi.org/10.1007/s00521-018-3482-5
  3. Knowledge-based learning for modeling concrete compressive strength using genetic programming vol.23, pp.4, 2017, https://doi.org/10.12989/cac.2019.23.4.255
  4. Effect of strain level on strength evaluation of date palm fiber-reinforced sand vol.21, pp.4, 2017, https://doi.org/10.12989/gae.2020.21.4.327
  5. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP vol.289, pp.None, 2017, https://doi.org/10.1016/j.jenvman.2021.112420
  6. Quantitative evaluation of the characteristics of air voids and their relationship with the permeability and salt freeze–thaw resistance of hybrid steel-polypropylene fiber–reinforced conc vol.125, pp.None, 2017, https://doi.org/10.1016/j.cemconcomp.2021.104292