Advances in Computational Design

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of unconfined compressive and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes using multiple linear regression and artificial neural network

  • Chore, H.S. (Department of Civil Engineering, Datta Meghe College of Engineering) ;
  • Magar, R.B. (Department of Civil Engineering, School of Engineering Technology, I. Kalsekar Technical Campus)
  • Received : 2017.03.08
  • Accepted : 2017.07.07
  • Published : 2017.07.25
⟨ Previous Next ⟩

Abstract

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

Keywords

References

  1. Abasi, N., Javadi, A.A. and Bahramloo, R. (2012), "Prediction of compression behaviour of normally consolidated fine grained soils", World Appl. Sci. J., 18(1), 6-14.
  2. Akayuli, C.F.A. and Ofosu, B. (2013), "Empirical model for estimating compression index from physical properties of weathered Birimianphyllites", E J. Geotech. Eng., 18(Z), 6135-6144.
  3. Alavi, A.H., Gandomi, A.H., Mollahassani, A., Heshmati, A.A. and Rashed, A. (2010), "Modeling of maximum dry density and optimum moisture contents of stabilized soil using artificial neural networks", J. Plant Nutr. Soil Sci., 173(3), 368-379. https://doi.org/10.1002/jpln.200800233
  4. Bandopadhayay, K. and Bhattacharjee, S. (2010), "Indirect tensile strength test of stabilized fly ash", Ind. Geotech. Conf. Mumbai, 1, 279-282.
  5. Bhatt, S. and Jain, P.K. (2014), "Prediction of California bearing ratio of soils using artificial neural networks", Am. J. Res. Sci. Technol. Eng. Math., 8(2), 156-161.
  6. Borowiec, A. and Wilk, K. (2014), "Prediction of consistency parameters of fen soils by neural networks", Comput. Assist. Meth. Eng. Sci., 21(1), 67-75.
  7. Castro, L.N. (2007), "Fundamentals of natural computing: A review", Phys. Life Rev., 4, 1-36. https://doi.org/10.1016/j.plrev.2006.10.002
  8. Chau, K.W., Wu, C.L. and Li, Y.S. (2005), "Comparison of several flood forecasting models in Yangtze river", J. Hydrol. Eng., 10(16), 485-491. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:6(485)
  9. Chore, H.S. and Vaidya, M.K. (2015), "Strength characterization of fiber reinforced cement-fly ash mixes", J. Geosynth. Ground Eng., 1(4), 30. https://doi.org/10.1007/s40891-015-0032-4
  10. Das, S.K. and Sabat, A.K. (2008), "Using neural networks for prediction of some properties of fly ash", E J. Geotech. Eng., 13(D), 1-13.
  11. 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
  12. Dutta, R.K. and Rao, G.V. (2007), "Regression model for predicting the behaviour of sand reinforced with waste plastic", Turk. J. Eng. Environ. Sci., 31(2), 119-126.
  13. Dutta, R.K. and Rao, G.V. (2009), "Regression model for predicting the behaviour of sand mixed with tire chip", J. Geotech. Eng., 3(1), 51-63. https://doi.org/10.3328/IJGE.2009.03.01.51-63
  14. Ellis, G.W., Yao, C., Zhao, R. and Penumadu, D. (1995), "Stress-strain modelling 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. Ghaboussi, J. and Sidarta, D.E. (1998), "New nested adaptive neural networks (NANN) for constitutive modelling", Comput. Geotech., 22(1), 29-52. https://doi.org/10.1016/S0266-352X(97)00034-7
  16. Goh, A.T.C. (1995), "Modelling soil correlations using neural networks", ASCE J. Comput. Civil Eng., 9(4), 275-278. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(275)
  17. Haykin, S. (2009), Neural Networks: A Comprehensive Foundation, 8th Edition, Pearson Prentice Hall, India.
  18. Hubick, K.T. (1992), Artificial Neural Networks in Australia, Department of Industry, Tech and Commerce, Commonwealth of Australia, Canberra.
  19. IS: 2720 (1965), Determination of Moisture Content Dry Density Relation Using Light Compaction, Part VII, Bureau Indian Standards, New Delhi, India.
  20. IS: 2720 (1973), Methods of Test for Soils: Determination of Unconfined Compression Strength, Part X, Bureau Indian Standards, New Delhi, India.
  21. IS: 4031 (1988), Methods of Physical Tests for Hydraulic Cement, First Rev, Bureau Indian Standards, New Delhi, India.
  22. Isik, N.S. (2009), "Estimation of swell index of fine grained soils using regression equations and artificial neural networks", J. Sci. Res. Essay, 4(10), 1047-1056.
  23. Khan, S.Z., Suman S., Pavani, M. and Das, S.K. (2015), "Prediction of the residual strength of clay using functional networks", Geosci. Front., 7(1), 67-74. https://doi.org/10.1016/j.gsf.2014.12.008
  24. Kurup, P.U. and Dudani, N.K. (2002), "Neural networks for profiling stress history of clays from PCPT data", ASCE J. Geotech. Geo-Environ. Eng., 128(7), 569-579. https://doi.org/10.1061/(ASCE)1090-0241(2002)128:7(569)
  25. Najjar, Y.M., Basheer, I.A. and Naouss, W.A. (1996), "On the compaction characteristics by nueonets", Comput. Geotech., 18(3), 167-187. https://doi.org/10.1016/0266-352X(95)00030-E
  26. Narendra, B.S., Puvvadi, S., Sundaram, 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
  27. Ozer, M., Isik, N.S. and Orhan, M. (2008), "Statistical and neural network assessment of the compression index of clay bearing soils", Bullet. Eng. Geo-Environ., 67(4), 537-545. https://doi.org/10.1007/s10064-008-0168-8
  28. Pennumadu, D. and Zhao, R. (1999), "Triaxial compression behaviour 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
  29. Penumadu, D. and Jean-Lou, C. (1997), "Geometrical modelling using artificial neural networks", Proceedings of the Artificial Neural Networks for Civil Engineers-Fundamentals and Applications, ASCE, 160-184.
  30. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2001), "Artificial neural network applications in geotechnical engineering", Austr. Geomech., 36(1), 49-62.
  31. Sidarta, D.E. and Ghaboussi, J. (1998), "Constitutive modelling of geo-materials from non-uniform material tests", Comput. Geotech., 22(10), 53-71. https://doi.org/10.1016/S0266-352X(97)00035-9
  32. Srinivasulu, S. and Jain, A. (2006), "A comparative analysis of training methods for artificial neural network rainfall-runoff models", Appl. Soft Comput., 6(3), 295-306. https://doi.org/10.1016/j.asoc.2005.02.002
  33. Subasi, S. (2009), "Prediction of mechanical properties of cement containing class C fly ash using artificial neural networks and regression technique", J. Sci. Res. Essay, 4(4), 289-297.
  34. Sulewska, M.J. (2011), "Applying artificial neural networks for the analysis of geotechnical problems", J. Comput. Assist. Meth. Eng. Sci., 18, 231-241.
  35. Suman, S., Mahmaya, M. and Das, S.K. (2016), "Prediction of maximum dry density and unconfined compressive strength of cement stabilized soil using artificial intelligence techniques", J. Geosynth. Ground Eng., 2(2), 11. https://doi.org/10.1007/s40891-016-0051-9
  36. Viji, V.K., Lissy, K.F., Shobha, C. and Benny, M.A. (2013), "Predictions on compaction characteristics of fly ashes using regression analysis and artificial neural network analysis", J. Geotech. Eng., 7(3), 282-292. https://doi.org/10.1179/1938636213Z.00000000036
  37. Willmott, C.J. and Matsuura, K. (2005), "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance", Clim. Res., 30(1), 79-82. https://doi.org/10.3354/cr030079
  38. Yildirim, B. and Gunaydin, O. (2011), "Estimation of California bearing ratio by using soft computing systems", Exp. Syst. Appl., 38(5), 6381-6391. https://doi.org/10.1016/j.eswa.2010.12.054
  39. Yoon, G.L. and Kim, B.T. (2004), "Regression analysis of compression index for Kwangyang marine clay", KSCE J. Civil Eng., 10(6), 415-418. https://doi.org/10.1007/BF02823980
  40. Yoon, G.L., Kim, B.T. and Jeon, S.S. (2004), "Empirical correlations of compression index for marine clay from regression analysis", Can. Geotech. J., 41(6), 1213-1221. https://doi.org/10.1139/t04-057

Cited by

  1. Spatial interpolation of SPT data and prediction of consolidation of clay by ANN method vol.8, pp.6, 2017, https://doi.org/10.12989/csm.2019.8.6.523
  2. Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR vol.5, pp.2, 2017, https://doi.org/10.12989/acd.2020.5.2.195
  3. Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review vol.38, pp.7, 2017, https://doi.org/10.1108/ec-10-2020-0576
  4. Artificial Intelligence for Prediction of Physical and Mechanical Properties of Stabilized Soil for Affordable Housing vol.11, pp.16, 2021, https://doi.org/10.3390/app11167503