DOI QR코드

DOI QR Code

A comparative assessment of bagging ensemble models for modeling concrete slump flow

  • Aydogmus, Hacer Yumurtaci (Department of Industrial Engineering, Alanya Alaaddin Keykubat University) ;
  • Erdal, Halil Ibrahim (Turkish Cooperation and Coordination Agency (TIKA)) ;
  • Karakurt, Onur (Department of Civil Engineering, Gazi University) ;
  • Namli, Ersin (Department of Industrial Engineering, Istanbul University Engineering Faculty) ;
  • Turkan, Yusuf S. (Department of Industrial Engineering, Istanbul University Engineering Faculty) ;
  • Erdal, Hamit (Institude of Social Sciences, Ataturk University)
  • 투고 : 2014.11.01
  • 심사 : 2015.11.06
  • 발행 : 2015.11.25

초록

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

키워드

참고문헌

  1. Aertsen, W., Kint, V., Orshoven, J., Ozkan, K. and Muys, B. (2010), "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests", Ecol. Model., 221, 1119-1130. https://doi.org/10.1016/j.ecolmodel.2010.01.007
  2. Abellan, J. and Masegosa, A.R. (2012), "Bagging schemes on the presence of class noise in classification", Exp. Syst. Appl., 39(8), 6827-6837. https://doi.org/10.1016/j.eswa.2012.01.013
  3. Anderson, D. and McNeill, G. (1992), Artificial neural networks technology. Rome Laboratory, A DACS State of the Art Report, ELIN: A011, New York.
  4. Basak, D., Pal, S. and Patranabis, D.C. (2007), "Support vector regression", Neu. Information Pro. Lett. Rev., 11(10), 203-224.
  5. Borra, S. and Di Ciaccio, A. (2010), "Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods", Comput. Stat. Data An., 54(12), 2976-2989. https://doi.org/10.1016/j.csda.2010.03.004
  6. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984), Classification and regression trees, Wadsworth, Int. Group, Belmont, California.
  7. Breiman, L. (1996), "Bagging predictors", Mach. Learn., 24(2), 123-140. https://doi.org/10.1023/A:1018054314350
  8. Breiman, L. (1999), Using adaptive bagging to debias regressions., Technical Report No. 547 of University of CA, Berkeley.
  9. Buhlmann, P. and Yu, B. (2002), "Analyzing bagging", Statist., 30(4), 927-961. https://doi.org/10.1214/aos/1031689014
  10. Chou, J.S. and Tsai, C.F. (2012), "Concrete compressive strength analysis using a combined classification and regression technique", Automat. Constr., 24, 52-60. https://doi.org/10.1016/j.autcon.2012.02.001
  11. Chou, J.S. and Pham, A.D. (2013), "Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength", Constr. Build. Mater., 49, 554-563. https://doi.org/10.1016/j.conbuildmat.2013.08.078
  12. Coppin, B. (2004), Artificial intelligence illuminated., Jones and Bartlett Publishers; Massachusetts.
  13. Cortes, C. and Vapnik, V. (1995), "Support vector networks", Mach. Learn., 20, 273-297.
  14. Erdal, H.I., Karakurt, O. and Namli, E. (2013), "High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform", Eng. Appl. Artif. Intel., 26(4), 1246-1254. https://doi.org/10.1016/j.engappai.2012.10.014
  15. Erdal, H.I. (2013), "Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction", Eng. Appl. Artif. Intel., 26(7), 1689-1697. https://doi.org/10.1016/j.engappai.2013.03.014
  16. Erdal, H.I. and Karakurt, O. (2013), "Advancing monthly streamflowprediction accuracy of CART models using ensemble learning paradigms", J. Hydrol., 477, 119-128. https://doi.org/10.1016/j.jhydrol.2012.11.015
  17. Ferraris, C.F. (1999), "Measurement of the rheological properties of high performance concrete: state of the art report", J. Res. Natl. Inst. Stan., 104(5), 461-478. https://doi.org/10.6028/jres.104.028
  18. Ferraris, C.F., Brower, L., Ozyildirim, C. and Daczko, J. (2000), "Workability of self-compacting concrete", Int'l symposium on high performance concrete proceedings, Natl. Inst. Stan, Florida.
  19. Gambhir, M.L. (2004), Concrete Technology, Tata McGraw-Hill Publishing Company Limited.
  20. Hancock, T., Put, R., Coomans, D., Vanderheyden, Y. and Everingham,Y. (2005), "A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies", Chem. Intel. Lab.Syst., 76 (2), 185-196. https://doi.org/10.1016/j.chemolab.2004.11.001
  21. Haykin, S. (1999), Neural Networks: A comprehensive foundation, 2nd Editions Pearson Education, 9th Indian reprint 2005, India.
  22. Heshmati, A.A.R., Salehzade, H., Alavi, A.H., Gandomi, A.H. and Abadi, M.M. (2008), "A multi expression programming application to high performance concrete", World Appl. Sci. J., 5(2), 215-223.
  23. Jia, J., Xiao, X., Liu, B. and Jiao, L. (2011), "Bagging-based spectral clustering ensemble selection", Pattern Recogn. Lett., 32(10),1456-1467. https://doi.org/10.1016/j.patrec.2011.04.008
  24. Karakurt, O., Erdal, H.I., Namli, E., Yumurtaci Aydogmus, H. and Turkan, Y.S. (2013), "Comparing ensembles of decision trees and neural networks for one-day-ahead streamflow prediction", Sci. Res. J. (SCIRJ), I(IV).
  25. Kewalramani, M.A. and Gupta, R. (2006), "Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks", Automat. Constr., 15(3), 374-379. https://doi.org/10.1016/j.autcon.2005.07.003
  26. Minqiang, Li., Tian, Jin. and Fuzan, Chen. (2008), "Improving multiclass pattern recognition with a co-evolutionary RBFNN", Pattern Recogn. Lett., 29(4), 392-406. https://doi.org/10.1016/j.patrec.2007.10.019
  27. Li, Y. (2006), "Predicting materials properties and behavior using classification and regression trees", Mater. Sci. Eng. A, 433, 261-268. https://doi.org/10.1016/j.msea.2006.06.100
  28. Nanthagopalan, P. and Santhanam, M. (2011), "Fresh and hardened properties of self-compacting concrete produced with manufactured sand", Cement Concrete Comp., 33(3), 353-358. https://doi.org/10.1016/j.cemconcomp.2010.11.005
  29. Orr, M.J.L. (1996), Introduction to radial basis function networks: centre for cognitive science, University of Edinburgh.
  30. Osman, I.H. and Laporte, G. (1996), "Metaheuristics: a bibliography", Ann. Oper. Res., 63, 513-623.
  31. Pino-Mejias, R., Jimenez-Gamero, M.D., Cubiles-de-la-Vega, M.D. and Pascual-Acosta, A. (2008), "Reduced bootstrap aggregating of learning algorithms", Pattern Recogn. Lett., 29, 265-271. https://doi.org/10.1016/j.patrec.2007.10.002
  32. Refaeilzadeh, P., Tang, L. and Liu, H. (2009), Cross-validation, Encyclopedia of database systems. Springer.
  33. Rodriguez, J.D., Perez, A. and Lozano, J.A. (2010), "Sensitivity analysis of k-fold cross validation in prediction error estimation", Pattern Anal. Mach. Intel., IEEE Tran., 32(3), 569-575. https://doi.org/10.1109/TPAMI.2009.187
  34. Thomas, P. and Thomas, A. (2011), "Multilayer perceptron for simulation models reduction: application to a sawmill workshop", Eng. Appl. Artif. Intel., 24, 646-657. https://doi.org/10.1016/j.engappai.2011.01.004
  35. Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009
  36. Tregger, N., Gregori, A., Ferrara, L. and Shah, S. (2012), "Correlating dynamic segregation of self-consolidating concrete to the slump-flow test", Constr. Build. Mater., 28, 499-505. https://doi.org/10.1016/j.conbuildmat.2011.08.052
  37. Vapnik, V.N. (2000), The Nature of Statistical Learning Theory, 2nd Editions, Springer-verlag New York Inc.
  38. Wang, G., Hao, J., Mab. J. and Jiang, H. (2011), "A comparative assessment of ensemble learning for credit scoring", Exp. Syst. Appl., 38, 223-230. https://doi.org/10.1016/j.eswa.2010.06.048
  39. Wang, G., Ma, J., Huang, L. and Xu, K. (2012), "Two credit scoring models based on dual strategy ensemble trees", Knowl. Based Syst., 26, 61-68. https://doi.org/10.1016/j.knosys.2011.06.020
  40. Yeh, I.C. (2007), "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement Concrete Comp., 29, 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001
  41. Yen, T., Tang, C.W., Chang, C.S. and Chen, K.H. (1999), "Flow behaviour of high strength high-performance concrete", Cement Concrete Comp., 21, 413-424. https://doi.org/10.1016/S0958-9465(99)00026-8
  42. Vapnik, V., Golowich, S.E. and Smola, A. (1996), "Support vector method for function approximation, regression estimation, and signal processing", Adv. Neu. Inform. Pr. Syst. 9.

피인용 문헌

  1. Investigation of irradiated 1H-Benzo[b]pyrrole by ESR, thermal methods and learning algorithm vol.171, pp.5-6, 2016, https://doi.org/10.1080/10420150.2016.1203925
  2. Forecasting Bank Failure: Base Learners, Ensembles and Hybrid Ensembles vol.49, pp.4, 2017, https://doi.org/10.1007/s10614-016-9623-y
  3. Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks vol.49, 2016, https://doi.org/10.1016/j.asoc.2016.09.010
  4. Analysis of Poly(2-Hydroxyethyl~Methacrylate)-co- Poly(4-Vinyl Pyridine) Copolymers [COP2,4] Irradiated: an EPR study vol.130, pp.1, 2016, https://doi.org/10.12693/APhysPolA.130.167
  5. Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector pp.1572-9974, 2019, https://doi.org/10.1007/s10614-018-9814-9
  6. YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ vol.22, pp.3, 2015, https://doi.org/10.17482/uumfd.332320
  7. Prediction of concrete compressive strength using non-destructive test results vol.21, pp.4, 2015, https://doi.org/10.12989/cac.2018.21.4.407
  8. An assessment of machine learning models for slump flow and examining redundant features vol.25, pp.6, 2015, https://doi.org/10.12989/cac.2020.25.6.565
  9. Predicting the dynamic modulus of hot mix asphalt mixtures using bagged trees ensemble vol.260, pp.None, 2020, https://doi.org/10.1016/j.conbuildmat.2020.120468
  10. Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm vol.14, pp.4, 2015, https://doi.org/10.3390/ma14040794