Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Support vector machine for prediction of the compressive strength of no-slump concrete

  • Sobhani, J. (Department of Concrete Technology, Road, Housing & Urban Development Research Center (BHRC)) ;
  • Khanzadi, M. (Department of Civil Engineering, Iran University of Science and Technology) ;
  • Movahedian, A.H. (Department of Civil Engineering, Iran University of Science and Technology)
  • Received : 2011.09.07
  • Accepted : 2012.10.09
  • Published : 2013.04.25
⟨ Previous Next ⟩

Abstract

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

Keywords

References

  1. ACI 211.3 (2002), Guide for selecting proportions for no-slump concrete, Farmington Hills (MI).
  2. Adeloye, A. and Munari, A. (2006), "Artificial neural network based generalized storage-yield-reliability models using the Levenberg-Marquardt algorithm", J. Hydrol., 326(1-4), 215-230. https://doi.org/10.1016/j.jhydrol.2005.10.033
  3. Acevedo-Rodriguez, J., Maldonado-Bascon, S., Lafuente-Arroyo, S., Siegmann, P. and Lopez-Ferreras, F. (2009), "Computational load reduction in decision functions using support vector machines", Signal Process, 89(10), 2066-2071. https://doi.org/10.1016/j.sigpro.2009.03.032
  4. American Society for Testing and Materials, (2009a), ASTM C150/C150M-09 Standard specification for Portland cement, Annual Book of ASTM Standard Vol. 4.01, Philadelphia.
  5. American Society for Testing and Materials, (2009b), ASTM C39/C39M-09a Standard test method for compressive strength of cylindrical concrete specimens, Concrete Specimens, Annual Book of ASTM Standard Vol. 4.01, Philadelphia.
  6. An, S.H., Park, U.Y., Kang, K., Cho, M.Y. and Cho, H.H. (2007), "Application of support vector machines in assessing conceptual cost estimates", ASCE J. Comput. Civil Eng., 21(4), 259-264. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:4(259)
  7. Bilim, C., Atiş, C.D., Tanyildiz, H. and Karahan, O. (2009), "Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network", Adv. Eng. Softw., 40(5), 334-340. https://doi.org/10.1016/j.advengsoft.2008.05.005
  8. Chen, B.T., Chang, T.P., Shih, J.Y. and Wang, J.J. (2009), "Estimation of exposed temperature for fire-damaged concrete using support vector machine", Comput. Mater. Sci., 44(3), 913-920. https://doi.org/10.1016/j.commatsci.2008.06.017
  9. Cheng, M.Y. and Wu, Y.W. (2009), "Evolutionary support vector machine inference system for construction management", Automat. Constr., 18(5), 597-604. https://doi.org/10.1016/j.autcon.2008.12.002
  10. Chou, J.S., Chiu, C.K., Farfoura, M. and Al-Taharwa, I. (2011), "Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques", J. Comput Civil Eng., 25(3), 242-254. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000088
  11. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Mac. Learn., 20(3), 273-297.
  12. Das, S.K., Samui, P. and Sabat, A.K. (2011), "Prediction of field hydraulic conductivity of clay liners using artificial neural network and support vector machine", ASCE Int. J. Geomech., 12(5), 606-611.
  13. Fausett, L.V. (1994), Fundamentals of neural networks: architectures, algorithms, and applications, Prentice Hall.
  14. Fazel Zarandi, M., Turksen, I.B., Sobhani, J. and Ramezanianpour, A.A. (2008), "Fuzzy polynomial neural networks for approximation of the compressive strength of concrete", Appl. Soft Comput., 8(1), 488-498. https://doi.org/10.1016/j.asoc.2007.02.010
  15. Ghaboussi, J., Garrett, J.H. and Wu, X. (1991), "Knowledge-based modeling of material behavior with neural networks", J. Eng. Mech.-ASCE, 117(1), 117-139.
  16. Hagan, M.T. and Menhaj, M.B. (1994), "Training feedforward networks with the Marquardt algorithm", IEEE T. Neural Networ., 5(6), 989-993. https://doi.org/10.1109/72.329697
  17. Lam, K.C., Palaneeswaran, E. and Yu, C.Y. (2009), "A support vector machine model for contractor prequalification", Automat. Constr., 18, 321-329. https://doi.org/10.1016/j.autcon.2008.09.007
  18. Mashford, J. and Marlow, D. (2010), "Prediction of sewer condition grade using support vector machines", J. Comput. Civil Eng., 25(4), 283-290.
  19. Neural Network Toolbox (MathWorks-a), www.mathworks.com/help/toolbox/nnet/
  20. Global Optimization Toolbox (MathWorks-b), www.mathworks.com/products/gads/
  21. Ni Hong-Guang, W.J.Z. (2000), "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30(8), 1245-1250. https://doi.org/10.1016/S0008-8846(00)00345-8
  22. Ozcan, F., Atis, C.D., Karahan, O., Uncuoglu, E. and Tanyildizi, H. (2009), "Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete", Adv. Eng. Softw., 40(9), 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005
  23. Oztas, A., Pala, M. and Ozbay, E., (2006), "Predicting the compressive strength and slump of high strength concrete using neural network", Constr. Build. Mater., 20(9), 769-775. https://doi.org/10.1016/j.conbuildmat.2005.01.054
  24. Pal, M. and Deswal, S. (2011), "Support vector regression based shear strength modelling of deep beams", Comput. Struct., 89(13-14), 1430-1439. https://doi.org/10.1016/j.compstruc.2011.03.005
  25. Rakotomamonjy, A. (2005), SVM and Kernel Methods Matlab Toolbox http://asi.insa-rouen.fr/enseignants/-arakotom/toolbox/index.html
  26. Ramezanianpour, A.A., Sobhani, M. and Sobhani, J. (2004), "Application of network based neuro-fuzzy system for prediction of the strength of high strength concrete", AKU J. Sci. Technol., 15 (59-C), 78-93.
  27. Samui, P. and Kim, D. (2012), "Utilization of support vector machine for prediction of fracture parameters of concrete", Comput. Concrete, 9(3), 215-226. https://doi.org/10.12989/cac.2012.9.3.215
  28. Sarıdemir, M. (2009), "Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic", Adv. Eng. Softw., 40(9), 920-927. https://doi.org/10.1016/j.advengsoft.2008.12.008
  29. Scholkopf, B. (1997), Support vector learning, Munich: R. Oldenbourg.
  30. Shelestynsky, E. (1972), The workability of no-slump concrete, University of Western Ontario.
  31. Sobhani, J., Najimi, M., Pourkhorshidi, A.R. and Parhizkar, T. (2010), "Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models", Constr. Build. Mater, 24(5), 709-718. https://doi.org/10.1016/j.conbuildmat.2009.10.037
  32. Tinoco, J., Gomes Correia, A. and Cortez, P. (2011), "Application of data mining techniques in the estimation of the uniaxial compressive strength of jet grouting columns over time", Constr. Build. Mater., 25(3), 1257-1262. https://doi.org/10.1016/j.conbuildmat.2010.09.027
  33. Tong, F., Xu, X.M., Luk, B.L. and Liu, K.P. (2008), "Evaluation of tile-wall bonding integrity based on impact acoustics and support vector machine", Sensor. Actuat. A-Phys., 144(1), 97-104. https://doi.org/10.1016/j.sna.2008.01.020
  34. Uysal, M. and Tanyildizi, H. (2011), "Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network", Constr. Build. Mater., 25(11), 4105-4111. https://doi.org/10.1016/j.conbuildmat.2010.11.108
  35. Vapnik, V.N. (2000), The nature of statistical learning theory, Springer Verlag.
  36. Vapnik, V.N. (1998), Statistical learning theory, Wiley-Interscience.
  37. Wan, C. and Mita, A. (2010), "Early warning of hazard for pipelines by acoustic recognition using principal component analysis and one-class Support vector machines", Smart Struct. Syst., 6(4), 405-421. https://doi.org/10.12989/sss.2010.6.4.405
  38. Wang, L., Mu, Z. and Guo, H. (2006), "Application of support vector machine in the prediction of mechanical property of steel materials", J. Univ. Sci. Tech. Beijing, Min. Met. Mater., 13(6), 512-515.
  39. Yan, K. and Shi, C. (2010), "Prediction of elastic modulus of normal and high strength concrete by support vector machine", Constr. Build. Mater, 24(8), 1479-1485. https://doi.org/10.1016/j.conbuildmat.2010.01.006
  40. Yinfeng, D.m Yingmin, L., Ming, L. and Mingkui, X. (2008), "Nonlinear structural response prediction based on support vector machines", J. Sound Vib., 311(3-5), 886-897. https://doi.org/10.1016/j.jsv.2007.09.054
  41. Zain, M. and Abd, S. (2009), "Multiple regression model for compressive strength prediction of high performance concrete", J. Appl. Sci., 9(1), 155-160. https://doi.org/10.3923/jas.2009.155.160

Cited by

  1. Support vector machines in structural engineering: a review vol.21, pp.3, 2015, https://doi.org/10.3846/13923730.2015.1005021
  2. Modelling the fresh properties of self-compacting concrete using support vector machine approach vol.106, 2016, https://doi.org/10.1016/j.conbuildmat.2015.12.035
  3. Monotonicity preserving SIRMs-connected fuzzy inference system for predicting HPC compressive strength 2018, https://doi.org/10.3233/IDT-180334
  4. Wearable Glove-Type Driver Stress Detection Using a Motion Sensor vol.18, pp.7, 2017, https://doi.org/10.1109/TITS.2016.2617881
  5. Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine pp.2095-2449, 2018, https://doi.org/10.1007/s11709-018-0489-z
  6. Modeling mechanical strength of self-compacting mortar containing nanoparticles using wavelet-based support vector machine vol.18, pp.6, 2016, https://doi.org/10.12989/cac.2016.18.6.1065
  7. Regression and ANN models for durability and mechanical characteristics of waste ceramic powder high performance sustainable concrete vol.25, pp.2, 2013, https://doi.org/10.12989/cac.2020.25.2.119
  8. Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach vol.32, pp.12, 2013, https://doi.org/10.1007/s00521-019-04267-w
  9. Artificial Intelligence for Compressive Strength Prediction of Concrete vol.1004, pp.None, 2020, https://doi.org/10.1088/1757-899x/1004/1/012010
  10. Predicting the mechanical properties of cement mortar using the support vector machine approach vol.291, pp.None, 2013, https://doi.org/10.1016/j.conbuildmat.2021.123396
  11. Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach vol.301, pp.None, 2021, https://doi.org/10.1016/j.conbuildmat.2021.124152
  12. Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation vol.213, pp.None, 2022, https://doi.org/10.1016/j.matdes.2021.110334