Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of compressive strength of GGBS based concrete using RVM

  • Prasanna, P.K. (Dept of Civil Engineering, VR Siddhartha Engineering College) ;
  • Ramachandra Murthy, A. (CSIR-Structural Engineering Research Centre) ;
  • Srinivasu, K. (RVR&JC College of Engineering)
  • Received : 2018.06.24
  • Accepted : 2018.11.07
  • Published : 2018.12.25
⟨ Previous Next ⟩

Abstract

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

Keywords

References

  1. Achmad, W., Kim, E.Y., Son, J.D., Yang, B.S., Andy, C., Tan, C., Gu, D.S., Choi, B.K. and Mathew, J. (2009), "Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine", Exp. Syst. Appl., 36(3), 7252-7261. https://doi.org/10.1016/j.eswa.2008.09.033
  2. Badogiannis, E., Papadakis, V.G., Chaniotakis, E. and Tsivilis, S. (2004), "Exploitation of poor Greek kaolins: Strength development of metakaolin concrete and evaluation by means of k-value", Cement Concrete Res., 34(6), 1035-1041. https://doi.org/10.1016/j.cemconres.2003.11.014
  3. Binici, H., Temiz, H. and Kose, M.M. (2007), "The effect of fineness on the properties of the blended cements incorporating ground granulated blast furnace slag and ground basaltic pumice", Constr. Build. Mater., 21(5), 1122-1128. https://doi.org/10.1016/j.conbuildmat.2005.11.005
  4. Chan, W.W.J. and Wu, C.M.L. (2000), "Durability of concrete with high cement replacement", Cement Concrete Res., 30(6), 865-879. https://doi.org/10.1016/S0008-8846(00)00253-2
  5. Dawei, H., Ian, C. and Weiping, K. (2012), "Flow modelling using Relevance Vector Machine (RVM)", Proceedings of the 5th International Conference on Hydroinformatics, Cardiff, U.K.
  6. Domone, P.L. and Soutsos, M.N. (1995), "Properties of high-strength concrete mixes containing PFA and GGBS", Mag. Concrete Res., 47(173), 355-367. https://doi.org/10.1680/macr.1995.47.173.355
  7. Guneyisi, E. and Gesoglu, M. (2018), "A study on durability properties of high-performance concretes incorporating high replacement levels of slag", Mater. Struct., 41(3), 479-493. https://doi.org/10.1617/s11527-007-9260-y
  8. Engin, S., Ozturk, O. and Okay, F. (2015), "Estimation of ultimate torque capacity of the SFRC beams using ANN", Struct. Eng. Mech., 53(5), 939-956. https://doi.org/10.12989/SEM.2015.53.5.939
  9. Erdem, H. (2017), "Predicting the moment capacity of RC slabs with insulation materials exposed to fire by ANN", Struct. Eng. Mech., 64(3), 339-346. https://doi.org/10.12989/SEM.2017.64.3.339
  10. Erdogan, O., Mustafa, E. and Halil, I.D. (2016), "Utilization and efficiency of ground granulated blast furnace slag on concrete properties-a review", Constr. Build. Mater., 105, 423-434. https://doi.org/10.1016/j.conbuildmat.2015.12.153
  11. Ferraris, C.H., Obla, K.H. and Hill, R. (2001), "The influence of mineral admixtures on the rheology of cement paste and concrete", Cement Concrete Res., 31(2), 245-255. https://doi.org/10.1016/S0008-8846(00)00454-3
  12. Gao, J.M., Qian, C.X., Liu, H.F., Wang, B. and Li, I.T.Z. (2005), "Microstructure of concrete containing GGBS", Cement Concrete Res., 35(7), 1299-1304. https://doi.org/10.1016/j.cemconres.2004.06.042
  13. Ghosh, S. and Mujumdar, P.P. (2008), "Statistical downscaling of GCM simulations to streamflow using relevance vector machine", Adv. Wat. Res., 31(1), 132-146. https://doi.org/10.1016/j.advwatres.2007.07.005
  14. Gopalakrishnan, S., Balasubramanian, K., Krishnamurthy, T.S. and Bharatkumar, B.H. (2001), "Investigation on the flexural behaviour of reinforced concrete beams containing supplementary cementitious materials", ACI Mater. J., 645-663.
  15. Halit, Y. (2007), "The effect of curing conditions on compressive strength of ultra high strength concrete with high volume mineral admixtures", Build. Environ., 42(5), 2083-2089. https://doi.org/10.1016/j.buildenv.2006.03.013
  16. Jaideep, K. and Kamaljit, K. (2017), "A fuzzy approach for an IoT-based automated employee performance appraisal", CMC: Comput. Mater. Contin., 53(1), 23-36.
  17. Kefei, L. and Zhisheng, X. (2011), "Traffic flow prediction of highway based on wavelet relevance vector machine", J. Informat. Comput. Sci., 8(9), 1641-1647.
  18. Liyang, W., Yongyi, Y., Robert, M.N., Miles, N.W. and Alexandra, E. (2005), "Relevance vector machine for automatic detection of clustered micro-calcifications", IEEE Trans. Med. Imag., 24(10), 1278-1285. https://doi.org/10.1109/TMI.2005.855435
  19. Mansouri, I., Safa, M., Ibrahim, Z., Kisi, O., Ahir, M.M., Baharom, S. and Azimi. (2016), "Strength prediction of rotary brace damper using MLR and MARS", Struct. Eng. Mech., 60(3), 471-488. https://doi.org/10.12989/sem.2016.60.3.471
  20. Megat Johari, M.A., Brooks, J.J., Kabir, S. and Rivard, P. (2011), "Influence of supplementary cementitious materials on engineering properties of high strength concrete", Constr. Build. Mater., 25(5), 2639-2648. https://doi.org/10.1016/j.conbuildmat.2010.12.013
  21. Oner, A. and Akyuz, S. (2007), "An experimental study on optimum usage of GGBS for the compressive strength of concrete", Cement Concrete Compos., 29(6), 505-514. https://doi.org/10.1016/j.cemconcomp.2007.01.001
  22. Rols, S., Mbessa, M., Ambroise, J. and Pera, J. (1999), "Influence of ultra fine particle type on properties of very-high strength concrete", Proceedings of the 2nd CANMET/ACI International Conference, RS, Brazil, ACI SP 186, 671-686.
  23. Roy, D.M., Arjunan, P. and Silsbee, M.R. (2001), "Effect of silica fume, metakaolin, and low-calcium fly ash on chemical resistance of concrete", Cement Concrete Res., 31(12), 1809-1813. https://doi.org/10.1016/S0008-8846(01)00548-8
  24. Chidiac, S.E. and Panesar, D.K. (2008), "Evolution of mechanical properties of concrete containing ground granulated blast furnace slag and effects on the scaling resistance test at 28 days", Cement Concrete Compos., 30(2), 63-71. https://doi.org/10.1016/j.cemconcomp.2007.09.003
  25. Sarat Kumar, D. and Pijush, S. (2008), "Prediction of liquefaction potential based on CPT data: A relevance vector machine approach", Proceedings of the 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), Goa, India, October.
  26. Sha, W. and Pereira, G.B. (2001), "Differential scanning calorimetry study of hydrated ground granulated blast-furnace slag", Cement Concrete Res., 31(2), 327-329. https://doi.org/10.1016/S0008-8846(00)00472-5
  27. Shantaram, P., Shreya, S., Pijush, S. and Ramachandra, M.A. (2014), "Prediction of fracture parameters of high strength and ultra high strength concrete beams using Gaussian process regression and Least squares support vector machine", CMES: Comput. Modell. Eng. Sci., 101(2), 139-158.
  28. Susanto, T., Tze, Y., Darren, L. and Bahador, S.D. (2013), "Durability and mechanical properties of high strength concrete incorporating ultra fine ground granulated blast-furnace slag", Constr. Build. Mater., 40, 875-881. https://doi.org/10.1016/j.conbuildmat.2012.11.052
  29. Susom, D., Ramachandra, M.A., Dookie, K. and Pijush, S. (2017), "Prediction of compressive strength of self-compacting concrete using intelligent computational modelling", CMC: Comput. Mater. Continua, 53(2), 157-174.
  30. Tipping, M.E. (2000), "The relevance vector machine", In S.A. Solla, T., Leen, K. and Muller, K.R. Editors, Advances in Neural Information Processing Systems, 12, 652-658.
  31. Tipping, M.E. (2001), "Sparse Bayesian learning and the relevance vector machine", J. Mach. Learn. Res., 1, 211-244.
  32. Vejmelkova, E., Pavlikova, M., Kersner, Z., Rovnanikova, P., Ondracek, M. and Sedlmajer, M. (2009), "High performance concrete containing lower slag amount: A complex view of mechanical and durability properties", Constr. Build. Mater., 23(6), 2237-2245. https://doi.org/10.1016/j.conbuildmat.2008.11.018
  33. Vishal Shreyans, S., Henyl Rakesh, S., Pijush, S. and Ramachandra, M.A. (2014), "Prediction of fracture parameters of high strength and ultra-high strength concrete beams using minimax probability machine regression and extreme learning machine", CMC: Comput. Mater. Contin., 44(2), 73-84.
  34. Wahyu, C., Achmad, W. and Bo-Suk, Y. (2010), "Application of relevance vector machine and logistic regression for machine degradation assessment", Mech. Syst. Sign. Proc., 24, 1161-1171. https://doi.org/10.1016/j.ymssp.2009.10.011
  35. Wahyu, C., Achmad, W. and Bo-Suk, Y. (2009), "Application of relevance vector machine and logistic regression for machine degradation assessment", J. Mech. Syst. Sign. Proc., 24(4), 1161-1171.
  36. Xiaodong, W., Meiying, Y. and Duanmu, C.J. (2009), "Classification of data from electronic nose using relevance vector machines", Sens. Actuat. B, 140(1), 143-148. https://doi.org/10.1016/j.snb.2009.04.030
  37. Yuvaraj, P., Ramachandra Murthy, A., Nagesh, R.I., Pijush, S. and Sekar, S.K. (2013b), "Multivariate adaptive regression splines model to predict fracture characteristics of high strength and ultra high strength concrete beams", CMC: Comput. Mater. Contin., 36(1), 73-97.
  38. Yuvaraj, P., Ramachandra Murthy, A., Nages, R.I., Pijush, S. and Sekar, S.K. (2014a), "ANN model to predict fracture characteristics of high strength and Ultra high strength concrete beams", CMC: Comput. Mater. Contin., 41(3), 193-213.
  39. Yuvaraj, P., Ramachandra Murthy, A., Nagesh, R.I., Pijush, S. and Sekar, S.K. (2014b), "Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine", Int. J. Dam. Mech., 23(7), 979-1004. https://doi.org/10.1177/1056789514520796
  40. Yuvaraj, P., Ramachandra Murthy, A., Nagesh, R.I., Sekar, S.K. and Pijush, S. (2013a), "Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams", Eng. Fract. Mech., 98, 29-43. https://doi.org/10.1016/j.engfracmech.2012.11.014

Cited by

  1. Determination of compressive strength using relevance vector machine and emotional neural network vol.20, pp.8, 2018, https://doi.org/10.1007/s42107-019-00171-9