Computers and Concrete

  • 제30권3호
  • /
  • Pages.225-236
  • /
  • 2022
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Multi-gene genetic programming for the prediction of the compressive strength of concrete mixtures

  • Ghahremani, Behzad (Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh) ;
  • Rizzo, Piervincenzo (Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh)
  • 투고 : 2021.10.04
  • 심사 : 2022.07.23
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

키워드

참고문헌

  1. Abdollahzadeh, G., Jahani, E. and Kashir, Z. (2016), "Predicting of compressive strength of recycled aggregate concrete by genetic programming", Comput. Concrete, 18(2), 155-164. https://doi.org/10.12989/cac.2016.18.2.155.
  2. Al-Shamiri, A.K., Kim, J.H., Yuan, T.F. and Yoon, Y.S. (2019), "Modeling the compressive strength of high-strength concrete: An extreme learning approach", Constr. Build. Mater., 208, 204-219. https://doi.org/10.1016/j.conbuildmat.2019.02.165.
  3. Amiri, M., Bakhshandeh Amnieh, H., Hasanipanah, M. and Mohammad Khanli, L. (2016), "A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure", Eng. Comput., 32(4), 631-644. https://doi.org/10.1007/s00366-016-0442-5.
  4. Awoyera, P.O., Mansouri, I., Abraham, A. and Viloria, A. (2021), "A new formulation for strength characteristics of steel slag aggregate concrete using an artificial intelligence-based approach", Comput. Concrete, 24(4), 333-341. https://doi.org/10.12989/cac.2021.27.4.333.
  5. Bijen, J. and van Selst, R. (1993), "Cement equivalence factors for fly ash", Cement Concrete Res., 23(5), 1029-1039. https://doi.org/10.1016/0008-8846(93)90162-3.
  6. Chopra, P., Kumar, R. and Kumar, M. (2015), "Artificial Neural Networks for the prediction of compressive strength of concrete", Int. J. Appl. Sci. Eng., 13, 187-204.
  7. Chopra, P., Sharma, R.K. and Kumar, M. (2016), "Prediction of compressive strength of concrete using Artificial Neural Network and genetic programming", Adv. Mater. Sci. Eng., 2016, Article ID 7648467. https://doi.org/10.1155/2016/7648467.
  8. Chou, J.S. and Tsai, C.F. (2012), "Concrete compressive strength analysis using a combined classification and regression technique", Autom. Constr., 24, 52-60. https://doi.org/10.1016/j.autcon.2012.02.001.
  9. Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H. and Wu, X. (2018), "Compressive strength prediction of recycled concrete based on deep learning", Constr. Build. Mater., 175, 562-569. https://doi.org/10.1016/j.conbuildmat.2018.04.169.
  10. Ding, S., Zhao, H., Zhang, Y., Xu, X. and Nie, R. (2015), "Extreme learning machine: Algorithm, theory and applications", Artif. Intel. Rev., 44(1), 103-115. https://doi.org/10.1007/s10462-013-9405-z.
  11. Dutta, S., Ramachandra Murthy, A., Kim, D. and Samui, P. (2017), "Prediction of compressive strength of self-compacting concrete using intelligent computational modeling", Comput., Mater. Continua, 53(2), 167-185.
  12. Feng, D.C., Liu, Z.T., Wang, X.D., Chen, Y., Chang, J.Q., Wei, D.F. and Jiang, Z.M. (2020), "Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach", Constr. Build. Mater., 230, 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000.
  13. Gandomi, A.H. and Alavi, A.H. (2012), "A new multi-gene genetic programming approach to nonlinear system modeling. Part I: Materials and structural engineering problems", Neur. Comput. Appl., 21(1), 171-187. https://doi.org/10.1007/s00521-011-0734-z.
  14. Gandomi, A.H., Alavi, A.H. and Sahab, M.G. (2010), "New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming", Mater. Struct./Materiaux et Construct., 43(7), 963-983. https://doi.org/10.1617/s11527-009-9559-y.
  15. Ghahremani, B., Bitaraf, M., Ghorbani-Tanha, A.K. and Fallahi, R. (2021), "Structural damage identification based on fast S-transform and convolutional neural networks", Struct., 29, 1199-1209. https://doi.org/10.1016/j.istruc.2020.11.068.
  16. Heaton, J.T. (2005), Introduction to Neural Networks with Java, Ed. Mary McKinnis, Heaton Research, Inc.
  17. Huang, G.B. (2003), "Learning capability and storage capacity of two-hidden-layer feedforward networks", IEEE Trans. Neur. Network., 14(2), 274-281. https://doi.org/10.1109/TNN.2003.809401.
  18. Huang, G.B., Zhu, Q.Y. and Siew, C.K. (2006), "Extreme learning machine: Theory and applications", Neurocomput., 70(1-3), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126.
  19. Humad, A.M., Kothari, A., Provis, J.L. and Cwirzen, A. (2019), "The effect of blast furnace slag/fly ash ratio on setting, strength, and shrinkage of alkali-activated pastes and concretes", Front. Mater., 6, 9. https://doi.org/10.3389/fmats.2019.00009.
  20. Jain, A.K., Mao, J. and Mohiuddin, K.M. (1996), "Artificial neural networks: A tutorial", Comput., 29(3), 31-44. https://doi.org/10.1109/2.485891.
  21. Karlik, B. (2015), "Performance analysis of various activation functions in generalized MLP architectures of neural networks", Int. J. Artif. Intel. Exp. Syst., 1(4), 111-122.
  22. Khademi, F., Akbari, M., Jamal, S.M. and Nikoo, M. (2017), "Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete", Front. Struct. Civil Eng., 11(1), 90-99. https://doi.org/10.1007/s11709-016-0363-9.
  23. Li, J., Cheng, J.H., Shi, J.Y. and Huang, F. (2012), "Brief introduction of back propagation (BP) neural network algorithm and its improvement", Adv. Intel. Soft Comput., 169(2), 553-558. https://doi.org/10.1007/978-3-642-30223-7_87.
  24. Mohammadi Bayazidi, A., Wang, G.G., Bolandi, H., Alavi, A.H. and Gandomi, A.H. (2014), "Multigene genetic programming for estimation of elastic modulus of concrete", Math. Prob. Eng., 2014, Article ID 474289. https://doi.org/10.1155/2014/474289.
  25. Moosazadeh, S., Namazi, E., Aghababaei, H., Marto, A., Mohamad, H. and Hajihassani, M. (2019), "Prediction of building damage induced by tunnelling through an optimized artificial neural network", Eng. Comput., 35(2), 579-591. https://doi.org/10.1007/s00366-018-0615-5.
  26. Mousavi, S.M., Aminian, P., Gandomi, A.H., Alavi, A.H. and Bolandi, H. (2012), "A new predictive model for compressive strength of HPC using gene expression programming", Adv. Eng. Softw., 45(1), 105-114. https://doi.org/10.1016/j.advengsoft.2011.09.014.
  27. Ni, H.G. and Wang, J.Z. (2000), "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30, 1245-1250. https://doi.org/10.1016/S0008-8846(00)00345-8.
  28. Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N. and Bhatti, M.A. (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.
  29. Prasad, B.K.R., Eskandari, H. and Reddy, B.V.V. (2009), "Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN", Constr. Build. Mater., 23(1), 117-128. https://doi.org/10.1016/j.conbuildmat.2008.01.014.
  30. Prasanna, P.K., Ramachandra Murthy, A. and Srinivasu, K. (2018), "Prediction of compressive strength of GGBS based concrete using RVM", Struct. Eng. Mech., 68(6), 691-700. https://doi.org/10.12989/sem.2018.68.6.691.
  31. Priddy, K.L. and Keller, P.E. (2005), Artificial Neural Networks: an Introduction, Vol. 68, SPIE Press.
  32. Rizzo, P., Bartoli, I., Marzani, A. and Lanza Di Scalea, F. (2005), "Defect classification in pipes by neural networks using multiple guided ultrasonic wave features extracted after wavelet processing", J. Press. Ves. Technol., Trans. ASME, 127(3), 294-303. https://doi.org/10.1115/1.1990213.
  33. Rizzo, P. and Lanza di Scalea, F. (2007), "Wavelet-based unsupervised and supervised learning algorithms for ultrasonic structural monitoring of waveguides", Progress in Smart Materials and Structures Research, 227-290.
  34. Saridemir, M. (2010), "Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash", Constr. Build. Mater., 24, 1911-1919. https://doi.org/10.1016/j.conbuildmat.2010.04.011.
  35. Searson, D., Willis, M. and Montague, G. (2007), "Co-evolution of non-linear PLS model components", J. Chemometr., 21(12), 592-603. https://doi.org/10.1002/cem.1084.
  36. Shah, V.S., Shah, H.R., Samui, P. and Ramachandra Murthy, A. (2014), "Prediction of fracture parameters of high strength and ultra-high strength concrete beams using minimax probability machine regression and extreme learning machine", Comput., Mater. Continua, 44(2), 73-84.
  37. Siddique, R., Aggarwal, P. and Aggarwal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks", Adv. Eng. Softw., 42(10), 780-786. https://doi.org/10.1016/j.advengsoft.2011.05.016.
  38. Tahwia, A.M., Heniegal, A., Elgamal, M.S. and Tayeh, B.A. (2021), "The prediction of compressive strength and non-destructive tests of sustainable concrete by using artificial neural networks", Comput. Concrete, 27(1), 21-28. https://doi.org/10.12989/cac.2021.27.1.021.
  39. Tavakkol, S., Alapour, F., Kazemian, A., Hasaninejad, A., Ghanbari, A. and Ramezanianpour, A.A. (2013), "Prediction of lightweight concrete strength by categorized regression, MLR and ANN", Comput. Concrete, 12(2), 151-167. https://doi.org/10.12989/cac.2013.12.2.151.
  40. Yaseen, Z.M., Deo, R.C., Hilal, A., Abd, A.M., Bueno, L.C., Salcedo-Sanz, S. and Nehdi, M.L. (2018), "Predicting compressive strength of lightweight foamed concrete using extreme learning machine model", Adv. Eng. Softw., 115, 112-125. https://doi.org/10.1016/j.advengsoft.2017.09.004.
  41. Yaswanth, K.K., Revathy, J. and Gajalakshmi, P. (2021), "Artificial intelligence for the compressive strength prediction of novel ductile geopolymer composites", Comput. Concrete, 28(1), 55-68. https://doi.org/10.12989/cac.2021.28.1.055.
  42. Yeh, I.C. (2003), "A mix proportioning methodology for fly ash and slag concrete using Artificial Neural Networks", Chung Hua J. Sci. Eng., 1(1), 77-84.
  43. Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3.
  44. Yeh, I.C. (1998), "Modeling concrete strength with augment-neuron networks", J. Mater. Civil Eng., 10(4), 263-268. https://doi.org/10.1061/(ASCE)0899-1561(1998)10:4(263).
  45. Yeh, I.C. (2006), "Analysis of strength of concrete using design of experiments and neural networks", J. Mater. Civil Eng., 18(4), 597-604. https://doi.org/10.1061/(ASCE)0899-1561(2006)18:4(597).
  46. Yeh, I.C. (1999), "Design of high-performance concrete mixture using neural networks and nonlinear programming", J. Comput. Civil Eng., 13, 36-42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36).
  47. Yeh, I.C. (2003), "Prediction of strength of fly Ash and Slag concrete by the use of artificial neural networks", J. Chin. Inst. Civil Hydraul. Eng, 15(4), 659-663.
  48. Yoon, J.Y., Kim, H., Lee, Y.J. and Sim, S.H. (2019), "Prediction model for mechanical properties of lightweight aggregate concrete using artificial neural network", Mater., 12(7), 2678. https://doi.org/10.3390/ma12172678.
  49. Yuvaraj, P., Murthy, A.R., Iyer, N.R., Sekar, S.K. and Samui, P. (2014), "ANN model to predict fracture characteristics of high strength and ultra high strength concrete beams", Comput., Mater. Continua, 41(3), 193-213.
  50. Yuvaraj, P., Ramachandra Murthy, A., Iyer, N.R., Sekar, S.K. and Samui, P. (2013), "Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams", Eng. Fract. Mech., 98(1), 29-43. https://doi.org/10.1016/j.engfracmech.2012.11.014.