Advances in Computational Design

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR

  • Kumar, Arvind (Department of Civil Engineering, Dr. B R Ambedkar National Institute of Technology) ;
  • Rupali, S. (Department of Civil Engineering, Dr. B R Ambedkar National Institute of Technology)
  • Received : 2018.11.29
  • Accepted : 2019.06.03
  • Published : 2020.04.25
⟨ Previous Next ⟩

Abstract

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

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. Aktas, G. and Ozerdem, M.S. (2016), "Prediction of behaviour of fresh concrete exposed to vibration using artificial neural networks and regression model", Struct. Eng. Mech., 60(4), 655-665. https://doi.org/10.12989/sem.2016.60.4.655.
  3. Alrubaye, A.J., Hasan, M. and Fattah, M.Y. (2017), "Stabilization of soft kaolin clay with silica fume and lime", Int J. Geotech. Engg., 11(1), 90-96. https://doi.org/10.1080/19386362.2016.1187884.
  4. Altunisik, A, C., Kalkan, E. and Basaga, H.B. (2018), "Development of engineering software to predict the structural behaviour of dams", Adv. Comp. Des., 3(1), 87-112. https://doi.org/10.12989/acd.2018.3.1.08.
  5. Amiralian, S., Chegenizadeh, A. and Nikraz H. (2012), "Laboratory investigation on the compaction properties of lime and fly ash composite", Proceedings of International Conference in Civil and Architectural Application, Phuket, Thailand, December.
  6. Amu, O.O., Ogunniyi, S.A. and Oladeji, O.O. (2011), "Geotechnical properties of lateritic soil stabilized with sugarcane straw ash", Amer J. Sci. Ind. Res., 2(2), 323-331. https://doi.org/ :10.5251/ajsir.2011.2.2.323.331.
  7. ASTM Standard C496, (2017), "Standard Test Method for Splitting Tensile Strength of Cylindrical Concrete Specimens", ASTM International, West Conshohocken, PA, https://doi.org/10.1520/C0496_C0496M-17.
  8. Asadollahfardi, G., Aria, S.H. and Abaei, M. (2016), "Modelling of dissolved oxygen (DO) in a reservoir using artificial neural networks: Abir Kabir reservoir, Iran", Adv. Env. Res., 5(3), 153-167. http://dx.doi.org/10.12989/aer.2016.5.3.153.
  9. Attom, M.F., Al-Tamimi, A.K. (2010), "Effects of polypropylene fibers on the shear strength of sandy soil", Int J. Geosci., 1(1), 44-50. https://doi.org/ 10.4236/ijg.2010.11006.
  10. Ayat, H., Kellouche, Y., Ghrici, M. and Boukhatem, B. (2018), "Compressive strength prediction of limestone filler concrete using artificial neural networks", Adv. Comp. Des., 3(3), 289-302. https://doi.org/10.12989/acd.2018.3.3.289.
  11. Ayyappan, S., Hemalatha, M. K. and Sundaram, M. (2010), "Investigation of Engineering Behavior of Soil, Polypropylene Fibers and Fly Ash-Mixtures for Road Construction", Int. J. Enviro. Sci. Develop., 1(2), 171-175.
  12. Bandopadhayay, K. and Bhattacharjee, S. (2010), "Indirect tensile strength test of stabilized fly ash", Proc. Indian Geotechnical Conference, Mumbai, India, December.
  13. Bhuvaneshwari, S., Robinson, R.G. and Gandhi, S.R. (2005), "Stabilization of expansive soils using fly ash", Fly Ash India, New Delhi, India,
  14. Brooks, R.M. (2009), "Soil stabilization with fly ash and rice husk ash", Int J. Res. Rev. App. Sci., 1(3), 209-217.
  15. Changizi, F. and Haddad, A. (2014), "Stabilization of Subgrade Soil for Highway by Recycled Polyester Fiber", J. Rehab. Civ. Engg., 2(1), 93-105. https://dx.doi.org/10.22075/jrce.2014.215.
  16. Choobbasti, A.J., Farrokhzad, F., Mashaie, A.R. and Azar, P. H. (2015), "Mapping of soil layers using artificial neural network (case study of Babol, northern Iran)", J. South Afri. Insti. Civil Engg., 57(1), 59- 66. http://dx.doi.org/10.17159/2309-8775/2015/v57n1a6.
  17. Chore, H.S. and Magar, R.B. (2017), "Prediction of Unconfined compressive and Brazilian Tensile strength of fiber reinforced cement stabilized fly ash mixes using multiple linear regression and artificial neural network", Adv. Comp. Des., 2(3), 225-240. https://doi.org/10.12989/acd.2017.2.3.225.
  18. Cordeiro, G.C. and Sales, C.P. (2015), "Pozzolanic activity of elephant grass ash and its influence on the mechanical properites of concrete", Cem Conc Comp., 55, 331-336. https://doi.org/10.1016/j.cemconcomp.2014.09.019.
  19. Gupta, D. and Kumar, A. (2017), "Performance evaluation of cement-stabilized pond ash-rice husk ash-clay mixture as a highway construction material", J. Rock Mech. Geotech. Engg., 9(1), 159-169. https://doi.org/10.1016/j.jrmge.2016.05.010.
  20. Gupta, D. and Kumar, A. (2016), "Strength Characterization of Cement Stabilized and Fiber Reinforced Clay-Pond Ash Mixes", Int J. Geosyn Grou Engg., 2(4), 32. https://doi.org/10.1007/s40891-016-0069-z.
  21. Hussain, M. and Dash, S.K. (2015), "Influence of lime on compaction behaviour of soils", Environ Geotech., 3(5), 346-352. https://www.icevirtuallibrary.com/doi/abs/10.1680/envgeo.14.00015.
  22. IS:2720(1985), Determination of Liquid and Plastic Limit, Part V, Bureau Indian Standards, New Delhi, India.
  23. IS: 2720 (2011), Determination of Moisture Content Dry Density Relation Using Light Compaction, Part VII, Bureau Indian Standards, New Delhi, India.
  24. IS: 2720 (1973), Methods of Test for Soils: Determination of Unconfined Compression Strength, Part X, Bureau Indian Standards, New Delhi, India.
  25. IS: 2720 (1977), Methods of Test for Soils: Determination of Free Swell Index of soil, Part XL, Bureau Indian Standards, New Delhi, India.
  26. Jala, S. and Goyal, D. (2006), "Fly ash as a soil ameliorant for improving crop production-A review", Biores Tech., 97(9), 1136-1147. https://doi.org/10.1016/j.biortech.2004.09.004.
  27. Kumar, A. and Gupta, D. (2016), "Behavior of cement-stabilized fiber-reinforced pond ash, rice husk ash- soil mixtures", Geotext Geomem., 44(3), 466-474. https://doi.org/10.1016/j.geotexmem.2015.07.010.
  28. Kumar, A., Walia, B.S. and Bajaj, A. (2007), "Influence of fly ash, lime and polyester fibers on compaction and strength properties of expansive soil", J. Mater. Civ. Engg, ASCE., 19(3), 242-248. https://doi.org/10.1061/(ASCE)0899-1561(2007)19:3(242).
  29. Kung, G.T.C., Hsiao, E.C.L., Schuster, M. and Juang, C.H. (2007), "A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays", Comp. Geotech., 34(5), 385-396. https://doi.org/10.1016/j.compgeo.2007.05.007.
  30. Li, J., Tang, C., Wang, D., Pei, X. and Shi, B. (2014), "Effect of discrete fiber reinforcement on soil tensile strength", J. Rock Mech. Geotech. Engg., 6(2), 133-137. https://doi.org/10.1016/j.jrmge.2014.01.003.
  31. Lime Manual, (2004), Lime-Treated Soil Construction Manual - Lime Stabilization and Lime Modification, National Lime Association, USA, Bulletin 326, January.
  32. Lourakis, M.I.A. (2005), "A brief description of the Levenberg-Marquardt algorithm implemented by levmar", Found Res. Tech., 4(1), 1-6.
  33. Mahamaya, M., Suman, S., Anand, A. and Das, S.K. (2015), "Prediction of UCS and CBR values of cement stabilised mine overburden and fly ash mixture", Proc. Earth. Plan Sci, 11, 294-302. https://doi.org/10.1016/j.proeps.2015.06.064.
  34. Maharjan, S. and Saliq, H. (2015), "Stabilization of Clay Soil Using Fly Ash and Lime for Construction Work", Int. J. Innov. Res. Sci. Engg. Tech., 4, 11869-11874. https://doi.org/10.15680/IJIRSET.2015.0412028
  35. Malekzadeh, M. and Bilsel, H. (2012), "Effect of polypropylene fiber on mechanical behavior of expansive soils", Elec. J. Geotech. Engg., 17, 55-63.
  36. MATLAB and Statistics Toolbox Release 2015a, The MathWorks, Inc., Natick, Massachusetts, United States.
  37. Orlov, M.L. (1996), "Multiple linear regression analysis using Microsoft Excel", Chemistry Department, Oregon State University.
  38. Prasad, C.R.V. and Sharma, R.K. (2014), "Influence of sand and fly ash on clayey soil stabilization", IOSR J. Mech. Civ. Engg., 4, 36-40.
  39. Saha, P., M.L.V., Prasad and Kumar R.P. (2017), "Predicting strength of SCC using artificial neural network and multivariable regression analysis", Comput. Conc., 20(1), 31-38. https://doi.org/10.12989/cac.2017.20.1.031.
  40. Sahoo, J.P., Sahoo, S. and Yadav, V.K. (2010), "Strength characteristics of fly ash mixed with lime stabilized soil", Proceedings of the Indian Geotechnical Conference, Mumbai, India, December.
  41. Shahin, M.A. and Jaksa, M.B. (2005), "Neural Network prediction of pullout capacity of marquee ground anchors", Comp. Geotech., 32(3), 153-163. https://doi.org/10.1016/j.compgeo.2005.02.003.
  42. Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2003), "Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models", Comp. Geotech., 30(8), 637-647. https://doi.org/10.1016/j.compgeo.2003.09.004.
  43. Shahin, M.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.
  44. Shahu, J.T., Patel, S. and Senapati, A. (2013), "Engineering Properties of Copper Slag-Fly Ash-Dolime Mix and Its Utilization in the Base Course of Flexible Pavements", J. Mater Civ. Engg. ASCE., 25(12), 1871-1879. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000756.
  45. Soundara, B. and Senthilkumar KP. (2015), "Effect of Fibers on Properties of Clay", Int J. Engg. App. Sci., 5(2), 8-14.
  46. Sunny, M.R., Mulani, S.B., Sanyal, S. and Kapania, R.K. (2016), "An artificial neural network residual kriging based surrogate model for curvilinearly stiffened panel optimization", Adv. Comp. Des., 1(3), 235-251. https://doi.org/10.12989/acd.2016.1.3.235.
  47. Tang, C., Shi, B., Gao, W., Chen, F. and Cai, Y. (2007), "Strength and mechanical behavior of short polypropylene fiber reinforced and cement stabilized clayey soil", Geotext Geomem., 25(3), 194-202. https://doi.org/10.1016/j.geotexmem.2006.11.002.
  48. 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.
  49. 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.

Cited by

  1. A neuro-fuzzy approach to predict the shear contribution of end-anchored FRP U-jackets vol.26, pp.5, 2020, https://doi.org/10.12989/cac.2020.26.5.397