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Prediction of concrete compressive strength using non-destructive test results

  • Erdal, Hamit (Institute of Social Sciences, Ataturk University) ;
  • Erdal, Mursel (Technology Faculty, Department of Civil Engineering, Gazi University) ;
  • Simsek, Osman (Technology Faculty, Department of Civil Engineering, Gazi University) ;
  • Erdal, Halil Ibrahim (Turkish Cooperation and Coordination Agency (TIKA))
  • 투고 : 2017.07.03
  • 심사 : 2017.12.13
  • 발행 : 2018.04.25

초록

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

키워드

참고문헌

  1. Altun, F., Kisi, O. and Aydin, K. (2008), "Predicting the compressive strength of steel fiber added lightweight concrete using neural network", Comput. Mater. Sci., 42(2), 259-265. https://doi.org/10.1016/j.commatsci.2007.07.011
  2. Antonaci, P., Bruno, C.L.E., Gliozzi, A.S. and Scalerandi, M. (2010), "Monitoring evolution of compressive damage in concrete with linear and nonlinear ultrasonic methods", Cement Concrete Res., 40, 1106-1113. https://doi.org/10.1016/j.cemconres.2010.02.017
  3. Arif, M., Ishihara, T. and Inooka, H. (2001), "Incorporation of experience in iterative learning controllers using locally weighted learning", Autom, 37, 881-888. https://doi.org/10.1016/S0005-1098(01)00030-9
  4. ASTM C39 (2001), Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens, Annual Book of ASTM Standards, Philadelphia, USA.
  5. ASTM C42 (1999), Standard Test Method for Obtaining and Testing Drilled Cores and Sawed Beams of Concrete, Annual Book of ASTM Standards, Philadelphia, USA.
  6. ASTM C597 (1998), Standard Test Method for Pulse Velocity Through Concrete, Annual Book of ASTM Standards, Philadelphia, USA.
  7. ASTM C803 (1999), Standard Test Method for Penetration Resistance of Hardened Concrete, Annual Book of ASTM Standards, Philadelphia, USA.
  8. ASTM C805 (1997), Standard Test Method for Rebound Number of Hardened Concrete, Annual Book of ASTM Standards, Philadelphia, USA.
  9. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38, 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156
  10. Aydin, F. and Saribiyik, M. (2010), "Correlation between Schmidt hammer and destructive compressions testing for concretes in existing buildings", Sci. Res. Essay., 5, 1644-1648.
  11. Aydogmus, H.Y., Ekinci, A., Erdal, H.I. and Erdal, H. (2015), "Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models", J. Econ. Int. Finance, 7(5), 127-136. https://doi.org/10.5897/JEIF2014.0629
  12. Aydogmus, H.Y., Erdal, H.I., Karakurt, O., Namli, E., Turkan, Y.S. and Erdal, H. (2015), "A comparative assessment of bagging ensemble models for modeling concrete slump flow", Comput. Concrete, 16(5), 741-757. https://doi.org/10.12989/cac.2015.16.5.741
  13. Baykan, U.N., Erdal, M. and Ugur, L.O. (2017), "A fuzzy logic model for prediction of compressive strength of concrete by use of non-destructive test results", Rev. Rom. Mater., 47(1), 54-59.
  14. Betrie, G.D., Tesfamariam, S., Morin, K.A. and Sadiq, R. (2013), "Predicting copper concentrations in acid mine drainage: A comparative analysis of five machine learning techniques", Environ. Monit. Assess., 185(5), 4171-4182. https://doi.org/10.1007/s10661-012-2859-7
  15. Breiman, L. (2001), "Random forests", Mach. Learn., 45(1), 25-32.
  16. Breysse, D. (2012), "Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods", Constr. Build. Mater., 33, 139-163. https://doi.org/10.1016/j.conbuildmat.2011.12.103
  17. Cheng, M.Y. and Wu, Y.W. (2009), "Evolutionary support vector machine inference system for construction management", Automat. Constr., 18, 597-604. https://doi.org/10.1016/j.autcon.2008.12.002
  18. Cheng, M.Y., Chou, J.S., Roy, A.F.V. and Wu, Y.W. (2012), "Highperformance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model", Automat. Constr., 28, 106-115. https://doi.org/10.1016/j.autcon.2012.07.004
  19. Cleary, J.G. and Trigg, L.E. (1995), "K*: an instance-based learner using an entropic distance measure", Proceedings of the 12th International Conference on Machine Learning, 108-114.
  20. Csepe, Z., Makra, L., Voukantsis, D., Matyasovszky, I., Tusnady, G., Karatzas, K. and Thibaudon, M. (2014), "Predicting daily ragweed pollen concentrations using computational intelligence techniques over two heavily polluted areas in Europe", Sci. Total Environ., 476, 542-52.
  21. Demirdogen, O., Erdal, H. and Akbaba, A.I. (2017), "Comparing various machine learning methods for prediction of patient revisit intention: A case study", SUJEST, 5(4), 386-401.
  22. Dias, W. and Pooliyadda, S. (2001), "Neural networks for predicting properties of concretes with admixtures", Constr. Build. Mater., 15(7), 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X
  23. Domone, P. and Soutsos, M. (1994), "An approach to the proportioning of high-strength concrete mixes", Concrete Int., 16, 26-31.
  24. Ekinci, S., Celebi, U.B., Bala, M., Amasyali, M.F. and Boyaci, U.K. (2011), "Predictions of oil/chemical tanker main design parameters using computational intelligence techniques", Appl. Soft. Comput., 11, 2356-2366. https://doi.org/10.1016/j.asoc.2010.08.015
  25. Erdal, H. (2015), "Makine ogrenmesi yontemlerinin insaat sektorune katkisi: Basinc dayanimi tahminlemesi", Pamukkale Univ Muh Bilim Derg, 21(3), 109-114.
  26. Erdal, H. and Karahanoglu, I. (2016), "Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks", Appl. Soft. Comput., 49, 861-867. https://doi.org/10.1016/j.asoc.2016.09.010
  27. Erdal, H.I. (2013), "Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction", Eng. Appl. Artif. Intell., 26(7), 1689-1697. https://doi.org/10.1016/j.engappai.2013.03.014
  28. Erdal, H.I. and Karakurt, O. (2013), "Advancing monthly stream flow prediction accuracy of CART models using ensemble learning paradigms", J. Hydrol., 477, 119-128. https://doi.org/10.1016/j.jhydrol.2012.11.015
  29. 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. Intell., 26(4), 1246-1254. https://doi.org/10.1016/j.engappai.2012.10.014
  30. Erdal, M. (2002), "Determination of compressive strength of concrete by some non-destructive test methods", M.Sc. Thesis, Gazi University, Ankara.
  31. Erdal, M. (2009), "Prediction of the compressive strength of vacuum processed concretes using artificial neural network and regression techniques", Sci. Res. Essay., 4(10), 1057-1065.
  32. Erdal, M. and Simsek, O. (2006), "Investigation of the performance of some non-destructive tests on the determination of compressive strength of vacuum-processed concrete", J. Fac. Eng. Arch. Gazi Univ., 21(1), 65-73.
  33. Galan, A. (1967), "Estimate of concrete strength by ultrasonic pulse velocity and damping constant", ACI J. Proc., 64(10), 678-684.
  34. Gupta, R., Kewalramani, M.A. and Goel, A. (2006), "Prediction of concrete strength using neural-expert system", J. Mater. Civil Eng., 18(3), 462-466. https://doi.org/10.1061/(ASCE)0899-1561(2006)18:3(462)
  35. Haykin, S. (1999), Neural Networks, A Comprehensive Foundation, 2nd Edition, Prentice Hall.
  36. Hola, J. and Schabowicz, K. (2005), "Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests", J. Civil Eng. Manage., 11, 23-32.
  37. Iba W.L. (1992), "Induction of one-level decision trees", Ninth International Conference on Machine Learning, Aberdeen.
  38. Jiawei, H. and Kamber, M. (2001), Data Mining: Concepts and Techniques, Morgan Kaufmann.
  39. Kewalramani, M.A. and Gupta, R. (2006), "Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks", Automat. Constr., 15, 374-379. https://doi.org/10.1016/j.autcon.2005.07.003
  40. Kheder, G.F. (1998), "A two stage procedure for assessment of in-situ concrete strength using combined non-destructive testing", Mater. Struct., 32, 410-417.
  41. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25(7), 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X
  42. Mehta, P.K. and Monterio, P.J.M. (2006), Concrete Structure, Properties and Materials, 3th Edition, Mc Graw-Hill Companies.
  43. Mielentz, F. (2008), "Phased arrays for ultrasonic investigations in concrete components", J. Nondestruct. Eval., 27, 23-33. https://doi.org/10.1007/s10921-008-0032-6
  44. Mousavi, S.M., Gandomi, A.H., Alavi, A.H. and Vesalimahmood, M. (2010), "Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares", Struct. Eng. Mech., 36, 225-241. https://doi.org/10.12989/sem.2010.36.2.225
  45. Namli, E., Erdal, H.I. and Erdal, H. (2016), "Dalgacik donusumu ile beton basinc dayanim tahmininin iyilestirilmesi", Politeknik Dergisi, 19(4), 471-480.
  46. Neville, A.M. (1993), Properties of Concrete, 3th Edition, Longman Scientific & Technical.
  47. Painuli, S., Elangovan, M. and Sugumaran, V. (2014), "Tool condition monitoring using K-star algorithm", Exp. Syst. Appl., 41, 2638-2643. https://doi.org/10.1016/j.eswa.2013.11.005
  48. Portnoy, S. and Koenker, R. (1997), "The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators", Stat. Sci., 12(4), 279-300. https://doi.org/10.1214/ss/1030037960
  49. Qasrawi, H.Y. (2000), "Concrete strength by combined nondestructive methods simply and reliable predicted", Cement Concrete Res., 30, 739-746. https://doi.org/10.1016/S0008-8846(00)00226-X
  50. Rajasekaran, S. and Amalraj, R. (2002), "Predictions of design parameters in civil engineering problems using SLNN with a single hidden RBF neuron", Comput. Struct., 80, 2495-2505. https://doi.org/10.1016/S0045-7949(02)00213-4
  51. Rajasekaran, S. and Lavanya, S. (2007), "Hybridization of genetic algorithm with immune system for optimization problems in structural engineering", Struct. Multidisc. Optim., 34, 415-429. https://doi.org/10.1007/s00158-006-0084-0
  52. Rajasekaran, S., Suresh, D, and Pai, GAV. (2002), "Application of sequential learning neural networks to civil engineering modeling problems", Eng. Comput., 18, 138-147. https://doi.org/10.1007/s003660200012
  53. Ramyar, K. and Kol, P. (1996), "Destructive and non-destructive test methods for estimating the strength of concrete", Cement Concrete World, 2, 46-54.
  54. Saridemir, M., Topcu, I.B., Ozcan, F. and Severcan, M.H. (2009) "Prediction of longterm effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic", Constr. Build. Mater., 23(3), 1279-1286. https://doi.org/10.1016/j.conbuildmat.2008.07.021
  55. 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
  56. Solis-Carcano, R. and Moreno, EI. (2008), "Evaluation of concrete made with crushed limestone aggregate based on ultrasonic pulse velocity", Constr. Build. Mater., 22, 1225-1231. https://doi.org/10.1016/j.conbuildmat.2007.01.014
  57. Subasi, S. (2009), "Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique", Sci. Res. Essay., 4(4), 289-297.
  58. Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic", Comput. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009
  59. Trtnik, G., Kavcic, F. and Turk, G. (2009), "Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks", Ultrasonic., 49, 53-60. https://doi.org/10.1016/j.ultras.2008.05.001
  60. Tufekci, P. (2014), "Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning method", Int. J. Elect. Power Energy Syst., 60, 126-140. https://doi.org/10.1016/j.ijepes.2014.02.027
  61. Turkan, Y.S., Aydogmus, H.Y. and Erdal, H. (2016), "The prediction of the wind speed at different heights by machine learning methods", IJOCTA, 6(2), 179-187.
  62. Wang, W. and Xu, Z. (2004), "A heuristic training for support vector regression", Neurocomput., 61, 259-275. https://doi.org/10.1016/j.neucom.2003.11.012
  63. Wang, Y. and Witten, I. (1997), "Inducing model trees for continuous classes", Ninth European Conference on Machine Learning, Prague, Czech Republic.
  64. Windsor Probe Test System Inc. (1994), WPS 500 Windsor Probe Test System Operating Instructions.
  65. Witten, I.H. and Frank, E. (2005), Data Mining: Practical Machine Learning Tools And Technique, Morgan Kaufmann Publishers.
  66. Yaprakli, T.S. and Erdal, H. (2015), "Bankacilik sektorunde pazarlama karmasi elemanlarinin onceliklerinin belirlenmesi: Erzurum ili ornegi", JASSS, 38, 481-500.
  67. Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28, 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3
  68. Yeh, I.C. (2007), "Modeling slump flow of concrete using secondorder regressions and artificial neural networks", Cement Concrete Compos., 29, 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001
  69. Yeh, I.C. and Lien, L.C. (2009), "Knowledge discovery of concrete material using genetic operation trees", Exp. Syst. Appl., 36(3), 5807-5812. https://doi.org/10.1016/j.eswa.2008.07.004
  70. Zarandi, M.F., 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