Computers and Concrete

  • 제24권4호
  • /
  • Pages.329-345
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  • 2019
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars

  • Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Apostolopoulou, Maria (Laboratory of Materials Science and Engineering, School of Chemical Engineering, National Technical University of Athens, Zografou Campus) ;
  • Skentou, Athanasia D. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Moropoulou, Antonia (Laboratory of Materials Science and Engineering, School of Chemical Engineering, National Technical University of Athens, Zografou Campus)
  • 투고 : 2019.03.26
  • 심사 : 2019.09.05
  • 발행 : 2019.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict mortar strength based on its mix components. This limitation is due to the highly nonlinear relation between the mortar's compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the compressive strength of mortars has been investigated. Specifically, surrogate models (such as artificial neural network models) have been used for the prediction of the compressive strength of mortars (based on experimental data available in the literature). Furthermore, compressive strength maps are presented for the first time, aiming to facilitate mortar mix design. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of mortars in a reliable and robust manner.

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. http://dx.doi.org/10.12989/cac.2016.18.2.155.
  2. Acikgenc, M., Ulas, M. and Alyamac, K.E. (2015), "Using an artificial neural network to predict mix compositions of steel fiber-reinforced concrete", Arab. J. Sci. Eng., 40, 407-419. https://doi.org/10.1007/s13369-014-1549-x.
  3. Ackley, D.H., Hinton, G.E. and Sejnowski, T.J. (1985), "A learning algorithm for Boltzmann machines", Cognitive Sci., 9(1), 147-169. https://doi.org/10.1016/S0364-0213(85)80012-4.
  4. Adeli, H. (2001), "Neural networks in civil engineering: 1989- 2000", Comput. Aid. Civil Infrastr. Eng., 16, 126-142. https://doi.org/10.1111/0885-9507.00219.
  5. Adhikary, B.B. and Mutsuyoshi, H. (2006), "Prediction of shear strength of steel fiber RC beams using neural networks", Constr. Build. Mater., 20(9), 801-811. https://doi.org/10.1016/j.conbuildmat.2005.01.047.
  6. Akkurt, S., Tayfur, G. and Can, S. (2004), "Fuzzy logic model for the prediction of cement compressive strength", Cement Concrete Res., 34, 1429-1433. https://doi.org/10.1016/j.cemconres.2004.01.020.
  7. Alavi Nezhad Khalil Abad, S.V., Yilmaz, M., Jahed Armaghani, D. and Tugrul, A. (2018), "Prediction of the durability of limestone aggregates using computational techniques", Neur. Comput. Appl., 29(2), 423-433. https://doi.org/10.1007/s00521-016-2456-8.
  8. Alavi, A.H. and Amir Hossein Gandomi, A.H. (2012), "Energybased numerical models for assessment of soil liquefaction", Geosci. Frontiers, 3(4), 541-555. https://doi.org/10.1016/j.gsf.2011.12.008.
  9. Alexandridis, A. (2013), "Evolving RBF neural networks for adaptive soft-sensor design", Int. J. Neural Syst., 23, 1350029. https://doi.org/10.1142/S0129065713500299.
  10. Alkayem, N.F., Cao, M., Zhang, Y., Bayat, M. and Su, Z. (2018), "Structural damage detection using finite element model updating with evolutionary algorithms: a survey", Neur. Comput. Appl., 30(2), 389-411. https://doi.org/10.1007/s00521-017-3284-1.
  11. Altun, F., Kişi, 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.
  12. Apostolopoulou, M., Armaghani, D.J., Bakolas, A., Douvika, M.G., Moropoulou, A. and Asteris, P.G. (2019), "Compressive strength of natural hydraulic lime mortars using soft computing techniques", Procedia Struct. Integ., 17, 914-923. https://doi.org/10.1016/j.prostr.2019.08.122.
  13. Apostolopoulou, M., Douvika, M.G., Kanellopoulos, I.N., Moropoulou, A. and Asteris, P.G. (2018), "Prediction of compressive strength of mortars using artificial neural networks", 1st International Conference TMM_CH, Transdisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage, Athens, Greece, October.
  14. Armaghani, D.J., Hatzigeorgiou, G.D., Karamani, Ch., Skentou, A., Zoumpoulaki, I. and Asteris, P.G. (2019), "Soft computingbased techniques for concrete beams shear strength", Procedia Struct. Integ., 17, 924-933. https://doi.org/10.1016/j.prostr.2019.08.123.
  15. Armaghani, D.J., Safari, V., Fahimifar, A., Mohd Amin, M.F., Monjezi, M. and Mohammadi, M.A. (2018), "Uniaxial compressive strength prediction through a new technique based on gene expression programming", Neur. Comput. Appl., 30(11), 3523-3532. https://doi.org/10.1007/s00521-017-2939-2.
  16. Asteris, P.G. and Kolovos, K.G. (2019), "Self-compacting concrete strength prediction using surrogate models", Neur. Comput. Appl., 31, 409-424. https://doi.org/10.1007/s00521-017-3007-7.
  17. Asteris, P.G. and Nikoo, M. (2019), "Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures", Neur. Comput. Appl., 31(9), 4837-4847. https://doi.org/10.1007/s00521-018-03965-1.
  18. Asteris, P.G. and Plevris, V. (2013), "Neural network approximation of the masonry failure under biaxial compressive stress", Proceedings of the 3rd South-East European Conference on Computational Mechanics (SEECCM III), an ECCOMAS and IACM Special Interest Conference, Kos Island, Greece, June.
  19. Asteris, P.G. and Plevris, V. (2017), "Anisotropic masonry failure criterion using artificial neural networks", Neur. Comput. Appl., 28(8), 2207-2229. https://doi.org/10.1007/s00521-016-2181-3.
  20. Asteris, P.G. Ashrafian, A. and Rezaie-Balf, M. (2019a), "Prediction of the compressive strength of self-compacting concrete using surrogate models", Comput. Concrete, 24(2), 137-150. https://doi.org/10.12989/cac.2019.24.2.137.
  21. Asteris, P.G., Argyropoulos, I., Cavaleri, L., Rodrigues, H., Varum, H., Thomas, J., Paulo, B. and Lourenco, P.B. (2018b), "Masonry compressive strength prediction using artificial neural networks", 1st International Conference TMM_CH, Transdisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage, Athens, Greece, October.
  22. Asteris, P.G., Kolovos, K.G., Douvika, M.G. and Roinos, K. (2016), "Prediction of self-compacting concrete strength using artificial neural networks", Eur. J. Environ. Civil Eng., 20, 102-122. https://doi.org/10.1080/19648189.2016.1246693.
  23. Asteris, P.G., Nozhati, S., Nikoo, M., Cavaleri, L. and Nikoo, M. (2019b), "Krill herd algorithm-based neural network in structural seismic reliability evaluation", Mech. Adv. Mater. Struct., 26(13), 1146-1153. https://doi.org/10.1080/15376494.2018.1430874.
  24. Asteris, P.G., Roussis, P.C. and Douvika, M.G. (2017), "Feedforward neural network prediction of the mechanical properties of sandcrete materials", Sensor., 17(6),1344. https://doi.org/10.3390/s17061344.
  25. Asteris, P.G., Tsaris, A.K., Cavaleri, L., Repapis, C.C., Papalou, A., Di Trapani, F. and Karypidis, D.F. (2016), "Prediction of the fundamental period of infilled RC frame structures using artificial neural networks", Comput. Intell. Neurosci., 2016, 5104907. https://doi.org/10.1155/2016/5104907.
  26. ASTM C 109/C 109M-02 (2002), Standard Test Method for Compressive Strength of Hydraulic Cement Mortars (Using 2- in. or [50-mm] cube specimens), Annual Book of ASTM Standards, American Society for Testing and Materials, Philadelphia, PA, USA.
  27. ASTM Standards (1983), ASTM Designation: C 109-80 Standard Test Method for Compressive Strength of Hydraulic Cement Mortars.
  28. Bartlett, P.L. (1998), "The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network", IEEE Tran. Inform. Theory, 44, 525-536. https://doi.org/10.1109/18.661502.
  29. Batis, G., Pantazopoulou, P., Tsivilis, S. and Badogiannis, E. (2005), "The effect of metakaolin on the corrosion behavior of cement mortars", Cement Concrete Compos., 27(1), 125-130. https://doi.org/10.1016/j.cemconcomp.2004.02.041.
  30. Baykasoglu, A., Dereli, T.U. and Tanis, S. (2004), "Prediction of cement strength using soft computing techniques", Cement Concrete Res., 34, 2083-2090. https://doi.org/10.1016/j.cemconres.2004.03.028.
  31. Belalia Douma, O., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2017), "Prediction of properties of self-compacting concrete containing fly ash using artificial neural network", Neur. Comput. Appl., 28(Suppl1), 707-718. https://doi.org/10.1007/s00521-016-2368-7.
  32. Bilgehan, M. and Turgut, P. (2010), "The use of neural networks in concrete compressive strength estimation", Comput. Concrete, 7(3), 271-283. https://doi.org/10.12989/cac.2010.7.3.271.
  33. Boukhatem, B., Kenai, S., Hamou, A.T., Ziou, D. and Ghrici, M. (2012), "Predicting concrete properties using Neural Networks (NN) with Principal Component Analysis (PCA) technique", Comput. Concrete, 10(6), 557-573. https://doi.org/10.12989/cac.2012.10.6.557.
  34. Bui, D.T., Ghareh, S., Moayedi, H. and Nguyen, H. (2019), "Finetuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-019-00850-w.
  35. Camoes, A. and Martins, F.F. (2017), "Compressive strength prediction of CFRP confined concrete using data mining techniques", Comput. Concrete, 19(3), 233-241. https://doi.org/10.12989/cac.2017.19.3.233.
  36. Cao, M., Alkayem, N.F., Pan, L. and Novak D. (2016), "Advanced methods in neural networks-based sensitivity analysis with their applications in civil engineering, artificial neural networks - Models and applications", Ed. Joao Luis Garcia Rosa, InTech, https://doi.org/10.5772/64026.
  37. Castelli, M., Goncalves, I., Popovic, A. and Trujillo, L. (2017), "An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming", Comput. Concrete, 19(6), 651-658. https://doi.org/10.12989/cac.2017.19.6.651.
  38. Cavaleri, L., Asteris, P.G., Psyllaki, P.P., Douvika, M.G., Skentou, A.D. and Vaxevanidis, N.M. (2019), "Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks", Appl. Sci., 9(14), 2788. https://doi.org/10.3390/app9142788.
  39. Cavaleri, L., Chatzarakis, G.E., Di Trapani, F.D., Douvika, M.G., Roinos, K., Vaxevanidis, N.M. and Asteris, P.G. (2017), "Modeling of surface roughness in electro-discharge machining using artificial neural networks", Adv. Mater. Res., 6(2), 169-184. https://doi.org/10.12989/amr.2017.6.2.169.
  40. Chen, H., Asteris, P.G., Armaghani, D.J., Gordan, B. and Pham, B.T. (2019), "Assessing dynamic conditions of the retaining wall: Developing two hybrid intelligent models", Appl. Sci., 9(6), 1042. https://doi.org/10.3390/app9061042.
  41. Cheng, B. and Titterington, D.M. (1994), "Neural networks: A review from a statistical perspective", Statist. Sci., 9(1), 2-30. https://doi.org/10.1214/ss/1177010638
  42. Courard, L., Darimont, A., Schouterden, M., Ferauche, F., Willem, X. and Degeimbre, R. (2003), "Durability of mortars modified with metakaolin", Cement Concrete Res., 33(9), 1473-1479. https://doi.org/10.1016/S0008-8846(03)00090-5.
  43. Dao, D.V., Ly, H.B., Trinh, S.H., Le, T.T. and Pham, B.T. (2019), "Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete", Mater., 12, 983. https://doi.org/10.3390/ma12060983.
  44. Dao, V.D., Trinh, S.H., Ly, H.B. and Pham, B.T. (2019), "Prediction of compressive strength of geopolymer concrete using entirely steel slag aggregates: Novel hybrid artificial intelligence approaches", Appl. Sci., 9(6), 1113. https://doi.org/10.3390/app9061113.
  45. Delen, D., Sharda, R. and Bessonov, M. (2006), "Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks", Accid. Anal. Prev., 38, 434-444. https://doi.org/10.1016/j.aap.2005.06.024.
  46. Demir, F. (2008), "Prediction of elastic modulus of normal and high strength concrete by artificial neural networks", Constr. Build. Mater., 22(7), 1428-1435. https://doi.org/10.1016/j.conbuildmat.2007.04.004.
  47. Dias, W.P.S. and Pooliyadda, S.P. (2001), "Neural networks for predicting properties of concretes with admixtures", Constr. Build. Mater., 15, 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X.
  48. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  49. EN 196-1 (1994), Methods of Testing Cement-Part 1: Determination of Strength.
  50. EN 197-1 (2011), Cement. Composition, Specifications and Conformity Criteria for Common Cements
  51. Erdal, H., Erdal, M., Şimşek, O. and Erdal, H.İ. (2018), "Prediction of concrete compressive strength using nondestructive test results", Comput. Concrete, 21(4), 407-417. https://doi.org/10.12989/cac.2018.21.4.407.
  52. Eskandari-Naddaf, H. and Kazemi, R. (2017), "ANN prediction of cement mortar compressive strength, influence of cement strength class", Constr. Build. Mater., 138, 1-11. https://doi.org/10.1016/j.conbuildmat.2017.01.132.
  53. Fukushima, K. (1998), "Neocognitron: A hierarchical neural network capable of visual pattern recognition", Neur. Network., 1(2), 119-130. https://doi.org/10.1016/0893-6080(88)90014-7.
  54. Gazder, U., Al-Amoudi, O.S.B., Saad Khan, S.M. and Maslehuddin, M. (2017), "Predicting compressive strength of blended cement concrete with ANNs", Comput. Concrete, 20(6), 627-634. https://doi.org/10.12989/cac.2017.20.6.627.
  55. Giovanis, D.G. and Papadopoulos, V. (2015), "Spectral representation-based neural network assisted stochastic structural mechanics", Eng. Struct., 84, 382-394. https://doi.org/10.1016/j.engstruct.2014.11.044.
  56. Golafshani, E.M. and Pazouki, G. (2018), "Predicting the compressive strength of self-compacting concrete containing fly ash using a hybrid artificial intelligence method", Comput. Concrete, 22(4), 419-437. https://doi.org/10.12989/cac.2018.22.4.419.
  57. Hinton, G.E. and Salakhutdinov, R.R. (2006), "Reducing the dimensionality of data with neural networks", Sci., 313, 504-507. https://doi.org/10.1126/science.1127647.
  58. Hinton, G.E., Osindero, S. and The, Y.W. (2006), "A fast learning algorithm for deep belief nets", Neur. Comput., 18(7), 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527.
  59. Hoang, N.D. and Bui, D.T. (2018), "Predicting earthquakeinduced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study", Bull. Eng. Geol. Environ., 77(1), 191-204. https://doi.org/10.1007/s10064-016-0924-0.
  60. Hola, A. and Sadowski, L. (2019), "A method of the neural identification of the moisture content in brick walls of historic buildings on the basis of non-destructive tests", Autom. Constr., 106, 102850. https://doi.org/10.1016/j.autcon.2019.102850.
  61. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neur. Network., 2, 359-366. https://doi.org/10.1016/0893-6080(89)90020-8.
  62. Ince, R. (2004), "Prediction of fracture parameters of concrete by artificial neural networks", Eng. Fract. Mech., 71(15), 2143-2159. https://doi.org/10.1016/j.engfracmech.2003.12.004.
  63. Iruansi, O., Guadagnini, M., Pilakoutas, K. and Neocleous, K. (2010), "Predicting the shear strength of RC beams without stirrups using Bayesian neural network", Proceedings of the 4th International Workshop on Reliable Engineering Computing, Robust Design-Coping with Hazards, Risk and Uncertainty, Singapore, March.
  64. Kadri, E. H., Kenai, S., Ezziane, K., Siddique, R. and De Schutter, G. (2011), "Influence of metakaolin and silica fume on the heat of hydration and compressive strength development of mortar", Appl. Clay Sci., 53(4), 704-708. https://doi.org/10.1016/j.clay.2011.06.008.
  65. Kao, C.H., Wang, C.C. and Wang, H.Y. (2017), "A neural-based predictive model of the compressive strength of waste LCD glass concrete", Comput. Concrete, 19(5), 457-465. https://doi.org/10.12989/cac.2017.19.5.457.
  66. Karlik, B. and Olgac, A.V. (2011), "Performance analysis of various activation functions in generalized MLP architectures of neural networks", Int. J. Artif. Intell. Exp Syst., 1, 111-122.
  67. Kaveh, A., Bakhshpoori, T. and Hamze-Ziabari, S.M. (2018), "GMDH-based prediction of shear strength of FRP-RC beams with and without stirrups", Comput. Concrete, 22(2), 197-207. https://doi.org/10.12989/cac.2018.22.2.197.
  68. Kewalramani, M.A. and Gupta, R. (2006), "Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks", Autom. Constr., 15(3), 374-379. https://doi.org/10.1016/j.autcon.2005.07.003.
  69. 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, 90-99. https://doi.org/10.1007/s11709-016-0363-9.
  70. Le, L.M., Ly, H.B., Pham, B.T., Le, V.M., Pham, T.A., Nguyen, D.H., Tran, X.T. and Le, T.T. (2019), "Hybrid artificial intelligence approaches for predicting buckling damage of steel columns under axial compression", Mater., 12(10), 1670. https://doi.org/10.3390/ma12101670.
  71. LeCun, Y., Bengio, Y. and Hinton, G. (2015), "Deep learning", Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539.
  72. LeCun, Y., Botoo, L., Bengio, Y. and Haffner, P. (1998), "Gradient-based learning applied to document recognition", Proc. IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
  73. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25, 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X.
  74. Lourakis, M.I.A. (2005), "A brief description of the Levenberg- Marquardt algorithm implemented by levmar", Hellas (FORTH), Institute of Computer Science Foundation for Research and Technology, http://www.ics.forth.gr/-lourakis/levmar/levmar.
  75. Ly, H-B., Pham, B.T., Dao, D.V., Le, V.M., Le, L.M. and Le, TT. (2019), "Improvement of ANFIS model for prediction of compressive strength of manufactured sand concrete", Appl. Sci., 9(18), 3841. https://doi.org/10.3390/app9183841.
  76. Ly, H.B., Le, L.M., Duong, H.T., Nguyen, T.C., Pham, T.A., Le, T.T., Le, V.M., Nguyen-Ngoc, L. and Pham, B.T. (2019), "Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections", Appl. Sci., 9(11), 2258. https://doi.org/10.3390/app9112258.
  77. Mansouri, I. and Kisi, O. (2015), "Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches", Compos. Part B Eng., 70, 247-255. https://doi.org/10.1016/j.compositesb.2014.11.023.
  78. Mansouri, I., Gholampour, A., Kisi, O. and Ozbakkaloglu, T. (2016), "Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques", Neur. Comput. Appl., 1-16. https://doi.org/10.1007/s00521-016-2492-4.
  79. Mardani-Aghabaglou, A., Sezer, G.İ. and Ramyar, K. (2014), "Comparison of fly ash, silica fume and metakaolin from mechanical properties and durability performance of mortar mixtures view point", Constr. Build. Mater., 70, 17-25. https://doi.org/10.1016/j.conbuildmat.2014.07.089.
  80. Mashhadban, H., Kutanaei, S.S. and Sayarinejad, M.A. (2016), "Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network", Constr. Build. Mater., 119, 277-287. https://doi.org/10.1016/j.conbuildmat.2016.05.034.
  81. Mazloom, M. and Yoosefi, M.M. (2013), "Predicting the indirect tensile strength of self-compacting concrete using artificial neural networks", Comput. Concrete, 12(3), 285-301. https://doi.org/10.12989/cac.2013.12.3.285.
  82. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Math. Biophys, 5(4), 115-133. https://doi.org/10.1007/BF02478259.
  83. Minsky, M. and Papert, S. (1969), Perceptrons: An Introduction to Computational Geometry, The MIT Press, Cambridge, MA, ISBN 0-262-63022-2.
  84. Moayedi, H., Foong, L.K., Nguyen, H., Bui, D.T., Jusoh, W.A.W. and Rashid, A.S.A. (2019), "Optimizing ANN models with PSO for predicting in short building seismic response", Eng. Comput., 36, 1-16. https://doi.org/10.1007/s00366-019-00733-0.
  85. Moayedi, H., Moatamediyan, A., Nguyen, H., Bui, XN., Bui, D.T. and Rashid, A.S.A. (2019), "Prediction of ultimate bearing capacity through various novel evolutionary and neural network models", Eng. Comput., 36, 1-17. https://doi.org/10.1007/s00366-019-00723-2.
  86. Naderpour, H. and Mirrashid, M. (2018), "An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals", J. Build. Eng., 19, 205-215. https://doi.org/10.1016/j.jobe.2018.05.012.
  87. NBN B12-208 (1969), Ciments, Essais de flexion et compression, Belgian Institute for Standardization, Brussels.
  88. Nguyen, M.D., Pham, B.T., Tuyen, T.T., Yen, H.P.H., Prakash, I., Vu, T.T., Chapi, K., Shirzadi, A., Shahabi, H., Dou, J., Quoc, N.K. and Bui, D.T. (2019), "Development of an artificial intelligence approach for prediction of consolidation coefficient of soft soil: A sensitivity analysis", Open Constr. Build. Technol. J., 13(1).
  89. Nikoo, M., Hadzima-Nyarko, M., KarloNyarko, E. and Nikoo, M. (2018), "Determining the natural frequency of cantilever beams using ANN and heuristic search", Appl. Artif. Intell., 32(3), 309-334. https://doi.org/10.1080/08839514.2018.1448003.
  90. Nikoo, M., Ramezani, F., Hadzima-Nyarko, M., Nyarko, E.K. and Nikoo, M. (2016) "Flood-routing modeling with neural network optimized by social-based algorithm", Nat. Hazard., 82(1), 1-24. https://doi.org/10.1007/s11069-016-2176-5.
  91. Nikoo, M., Sadowski, L., Khademi, F. and Nikoo, M. (2017), "Determination of damage in reinforced concrete frames with shear walls using self-organizing feature map", Appl. Comput. Intel. Soft Comput., 2017, Article ID 3508189, 10. https://doi.org/10.1155/2017/3508189.
  92. Nikoo, M., Zarfam, P. and Sayahpour, H. (2015), "Determination of compressive strength of concrete using Self Organization Feature Map (SOFM)", Eng. Comput., 31, 113-121. https://doi.org/10.1007/s00366-013-0334-x.
  93. Oh, T.K., Kim, J., Lee, C. and Park, S. (2017), "Nondestructive concrete strength estimation based on electro-mechanical impedance with artificial neural network", J. Adv. Concr. Technol., 15, 94-102. https://doi.org/10.3151/jact.15.94.
  94. Ongpeng, J., Soberano, M., Oreta, A. and Hirose, S. (2017), "Artificial neural network model using ultrasonic test results to predict compressive stress in concrete", Comput. Concrete, 19(1), 59-68. https://doi.org/10.12989/cac.2017.19.1.059.
  95. Onyari, E.K. and Ikotun, B.D. (2018), "Prediction of compressive and flexural strengths of a modified zeolite additive mortar using artificial neural network", Constr. Build. Mater., 187, 1232-1241. https://doi.org/10.1016/j.conbuildmat.2018.08.079.
  96. O zcan, 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, 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005.
  97. Pala, M., O zbay, E., O ztas, A. and Yuce, M.I. (2007), "Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks", Constr. Build. Mater., 21(2), 384-394. https://doi.org/10.1016/j.conbuildmat.2005.08.009.
  98. Parande, A.K., Ramesh Babu, B., AswinKarthik, M., Deepak Kumaar, K.K. and Palaniswamy, N. (2008), "Study on strength and corrosion performance for steel embedded in metakaolin blended concrete/mortar", Constr. Build. Mater., 22(3), 127-134. https://doi.org/10.1016/j.conbuildmat.2006.10.003.
  99. Peng, C.H., Yeh, I.C. and Lien, L.C. (2009), "Modeling strength of high-performance concrete using genetic operation trees with pruning techniques", Comput. Concrete, 6(3), 203-223. https://doi.org/10.12989/cac.2009.6.3.203.
  100. Pham, B.T., Tien Bui, D. and Prakash, I. (2017), "Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: A comparative study", Geotech. Geolog. Eng., 35(6), 2597-2611. https://doi.org/10.1007/s10706-017-0264-2.
  101. Plevris, V. and Asteris, P. (2015), "Anisotropic failure criterion for brittle materials using artificial beural betworks", Proceedings of the COMPDYN 2015-5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Crete Island, Greece, May.
  102. Plevris, V. and Asteris, P.G. (2014), "Modeling of masonry failure surface under biaxial compressive stress using neural networks", Constr. Build. Mater., 55, 447-461. https://doi.org/10.1016/j.conbuildmat.2014.01.041.
  103. Potgieter-Vermaak, S.S. and Potgieter, J.H. (2006), "Metakaolin as an extender in South African cement", J. Mater. Civil Eng., 18(4), 619-623. https://doi.org/10.1061/(ASCE)0899-1561(2006)18:4(619).
  104. Rashid, K. and Rashid, T. (2017), "Fuzzy logic model for the prediction of concrete compressive strength by incorporating green foundry sand", Comput. Concrete, 19(6), 617-623. https://doi.org/10.12989/cac.2017.19.6.617.
  105. Reddy, T.C.S. (2017), "Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network", Front. Struct. Civil Eng., 1-14. https://doi.org/10.1007/s11709-017-0445-3.
  106. Ripley, B.D. (1996), Pattern Recognition and Neural Networks, 1st Edition, Cambridge University Press, Cambridge, United Kingdom.
  107. Rosenblatt, F. (1958), "The perceptron: A probabilistic model for information storage and organization in the brain", Psycholog. Rev., 65(6), 386-408. http://dx.doi.org/10.1037/h0042519.
  108. Sadowski, L., Nikoo, M. and Nikoo, M. (2015), "Principal component analysis combined with a self organization feature map to determine the pull-off adhesion between concrete layers", Constr. Build. Mater., 78, 386-396. https://doi.org/10.1016/j.conbuildmat.2015.01.034.
  109. Sadowski, L., Nikoo, M. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concrete, 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355.
  110. Safiuddin, M., Raman, S.N., Salam, M.A. and Jumaat, M.Z. (2016), "Modeling of compressive strength for selfconsolidating high-strength concrete incorporating palm oil fuel ash", Mater., 9, 396. https://doi.org/10.3390/ma9050396.
  111. Saha, P., Prasad, M.L.V. and RathishKumar, P. (2017), "Predicting strength of SCC using artificial neural network and multivariable regression analysis", Comput. Concrete, 20(1), 31-38. https://doi.org/10.12989/cac.2017.20.1.031.
  112. Salehi, H. and Burgueno, R. (2018), "Emerging artificial intelligence methods in structural engineering", Eng. Struct., 171, 170-189. https://doi.org/10.1016/j.engstruct.2018.05.084.
  113. Saridemir, 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.
  114. Sarir, P., Chen, J., Asteris, P.G., Armaghani, D.J. and Tahir, M.M. (2019), "Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns", Eng. Comput., 1-19. https://doi.org/10.1007/s00366-019-00808-y.
  115. Schmidhuber, J. (2015), "Deep learning in neural networks: An overview", Neur. Network., 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003.
  116. Sumasree, C. and Sajja, S. (2016), "Effect of metakaolin and cerafibermix on mechanical and durability properties of mortars", Int. J. Sci. Eng. Technol., 4(3), 501-506.
  117. Topçu, I.B. and Saridemir, M. (2007), "Prediction of properties of waste AAC aggregate concrete using artificial neural network", Comput. Mater. Sci., 41(1), 117-125. https://doi.org/10.1016/j.commatsci.2007.03.010.
  118. Topçu, 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, 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009.
  119. Trtnik, G., Kavcic, F. and Turk, G. (2009), "Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks", Ultrasonics, 49, 53-60. https://doi.org/10.1016/j.ultras.2008.05.001.
  120. Tsai, H.C. and Liao, M.C. (2019), "Knowledge-based learning for modeling concrete compressive strength using genetic programming", Comput. Concrete, 23(4), 255-265. https://doi.org/10.12989/cac.2019.23.4.255.
  121. Turkmen, I., Bingol, A.F., Tortum, A., Demirboga, R. and Gul, R. (2017), "Properties of pumice aggregate concretes at elevated temperatures and comparison with ANN models", Fire Mater., 41, 142-153. https://doi.org/10.1002/fam.2374.
  122. Vu, D.D., Stroeven, P. and Bui, V.B. (2001), "Strength and durability aspects of calcined kaolin-blended Portland cement mortar and concrete", Cement Concrete Compos., 23(6), 471-478. https://doi.org/10.1016/S0958-9465(00)00091-3.
  123. Waszczyszyn, Z. and Ziemiański, L. (2001), "Neural networks in mechanics of structures and materials-New results and prospects of applications", Comput. Struct., 79, 2261-2276. https://doi.org/10.1016/S0045-7949(01)00083-9.
  124. Widrow, B. and Lehr, M.A. (1990), "30 years of adaptive neural networks: Perceptron, madaline, and backpropagation", Proc. IEEE, 78(9), 1415-1442. https://doi.org/10.1109/5.58323
  125. Xu, H., Zhou, J., Asteris, P.G., Armaghani, D.J. and Tahir, M.Md. (2019), "Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate", Appl. Sci., 9, 3715. https://doi.org/10.3390/app9183715.
  126. Xue, X. (2018), "Evaluation of concrete compressive strength based on an improved PSO-LSSVM model", Comput. Concrete, 21(5), 505-511. https://doi.org/10.12989/cac.2018.21.5.505.
  127. Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998), "Forecasting with artificial neural networks: The state of the art", Int. J. Forecast., 14(1), 35-62. https://doi.org/10.1016/S0169-2070(97)00044-7.

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