Browse > Article
http://dx.doi.org/10.12989/acc.2022.14.2.115

Maturation effect on strength of high-strength concretes which produced with different origin aggregates  

Kaya, Mustafa (Aksaray University, Faculty of Engineering)
Komur, M. Aydin (Aksaray University, Faculty of Engineering)
Gursel, Ercin (Aksaray University, Faculty of Engineering)
Publication Information
Advances in concrete construction / v.14, no.2, 2022 , pp. 115-130 More about this Journal
Abstract
This paper presents an application of the maturation effect on the strength of high-strength concrete which is produced with different origin aggregates. While investigating the maturation effect on HSC 384 specimens were prepared with 22 different origin aggregates. These prepared specimens were subjected to the standard compressive tests which were applied after curing for 2, 7, 28, and 56 days under appropriate conditions. The test results revealed that bright surface-low adherence behavior is valid in normal strength concretes, but is not as effective as expected in high-strength concretes. The application of artificial neural networks (ANNs) to predict 2, 7, 28, and 56 day compressive strength of HSC is also investigated in this paper. An ANN model is built, trained, and tested using the available test data gathered from experimental studies. The ANN model is found to predict 2, 7, 28, and 56 days of compressive strength of high-strength concrete well within the ranges of the input parameters considered. These comparisons show that ANNs have strong potential as a feasible tool for predicting the compressive strength of high-strength concrete within the range of the input parameters considered.
Keywords
artificial neural network; compressive strength; high-strength concrete; maturation effect;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Hakim, S.J.S., Noorzaei, J., Jaafar, M.S., Jameel, M. and Mohammadhassani, M. (2011), "Application of artificial neural networks to predict compressive strength of high-strength concrete", Int. J. Phys. Sci., 6, 975-981. https://doi.org/10.5897/IJPS11.023   DOI
2 Haykin, S. (1994), Neural Networks: A Comprehensive Foundation, Prentice-Hall, NJ, USA.
3 Hsu, K.L., Gupta, H.V. and Sorooshian, S. (1995), "Artificial neural network modeling of the rainfall-runoff process", Water Resour. Res., 31(10), 2517-2530. https://doi.org/10.1029/95WR01955   DOI
4 Jin, N.J., Seung, I., Choi, Y.S. and Yeon, J. (2017), "Prediction of early-age compressive strength of epoxy resin concrete using the maturity method", Constr. Build. Mater., 152, 990-998. https://doi.org/10.1016/j.conbuildmat.2017.07.066   DOI
5 Kaplan, M.F. (1986), "Ultrasonic pulse velocity, dynamic modulus of elasticity, poisson ratio, and strength of concrete made with thirteen different coarse aggregates", RILEM Bull., No. 1, New Series, pp. 17-28.
6 Kasabov, N.K. (1996), Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, Cambridge, MA, USA.
7 Kasperkiewicz, J., Racz, J. and Dubrawski, A. (1995), "HPC strength prediction using artificial neural network", J. Comput. Civil Eng., 9, 279-284. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(279)   DOI
8 Kosmatka, S.H., Panarese, W.C. and Kerkhoff, B. (2002), Design, and control of concrete mixtures, Vol. 5420, pp. 60077-1083, Portland Cement Association, Skokie, IL, USA.
9 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   DOI
10 Neville, A.M. (1995a), Properties of concrete, (4th Ed.), Pitman, London, UK.
11 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   DOI
12 Galobardes, I., Cavalaro, S.H., Goodier, C.I., Austin, S. and Rueda, A. (2015), "Maturity method to predict the evolution of the properties of sprayed concrete", Constr. Build. Mater., 79, 357-369. https://doi.org/10.1016/j.conbuildmat.2014.12.038   DOI
13 Garzon-Roca, J., Marco, C.O. and Adam, J.M. (2013a), "Compressive strength of masonry made of clay bricks, and cement mortar: Estimation based on neural networks, and fuzzy logic", Eng. Struct., 48, 21-27. https://doi.org/10.1016/j.engstruct.2012.09.029   DOI
14 Asteris, P.G. and Mokos, V.G. (2020), "Concrete compressive strength using artificial neural networks", Neural Comput. Applicat., 32, 1807-11826. https://doi.org/10.1007/s00521-019-04663-2   DOI
15 Apostolopoulou, M., Asteris, P.G., Armaghani, D.J., Douvika, M.G., Lourenco, P.B., Cavaleri, L., Bakolas, A. and Moropoulou, A. (2020), "Mapping and holistic design of natural hydraulic lime mortars", Cement Concrete Res., 136, 106167. https://doi.org/10.1016/j.cemconres.2020.106167   DOI
16 Armaghani, D.J. and Asteris, P.G. (2020), "A comparative study of ANN, and ANFIS models for the prediction of cement-based mortar materials compressive strength", Neural Comput. Applicat., 33(9), 4501-4532. https://doi.org/10.1007/s00521-020-05244-4   DOI
17 Asteris, P.G. and Kolovos, K.G. (2017), "Self-compacting concrete strength prediction using surrogate models", Neural Comput. Applicat., 31(1), 409-424. https://doi.org/10.1007/s00521-017-3007-7   DOI
18 Asteris, P.G., Roussis, P.C. and Douvika, M.G. (2017), "Feedforward neural network prediction of the mechanical properties of sandcrete materials", Sensors, 17(6), 1344. https://doi.org/10.3390/s17061344   DOI
19 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, 769-775. https://doi.org/10.1016/j.conbuildmat.2005.01.054   DOI
20 Garzon-Roca, J., Adam, J.M., Sandoval, C. and Roca, P. (2013b), "Estimation of the axial behaviour of masonry walls based on artificial neural networks", Comput. Struct., 125, 145-152. https://doi.org/10.1016/j.compstruc.2013.05.006   DOI
21 Plevris, V. and Asteris, P.G. (2014), "Modeling the 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   DOI
22 Shah, S.P. (1993), "Recent trends in the science, and technology of concrete, concrete technology, new trends, industrial applications", Proceedings of the International RILEM Workshop, E& FN Spon., London, UK, pp. 1-18.
23 Suziki, K. (2011), Artificial neural networks methodological advances, and biomedical applications, Edited Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia. ISBN: 978-953-307-243-2
24 Topcu, I.B. and Saridemir, M. (2008), "Prediction of rubberized concrete properties using artificial neural network, and fuzzy logic", Constr. Build. Mater., 22, 532-540. https://doi.org/10.1016/j.conbuildmat.2006.11.007   DOI
25 TS EN 12390-3 Testing hardened concrete-Part 3: (2010), Compressive strength of test specimens, Turkish Standardization Institute, Ankara, Turkey.
26 Yeh, I.C. (2008), "Modeling slump of concrete with fly ash and superplasticizer", Comput. Concrete, Int. J., 5(6), 559-572. https://doi.org/10.12989/cac.2008.5.6.559   DOI
27 Asteris, P.G., Apostolopoulou, M., Armaghani, D.J., Cavaleri, L., Chountalas, A.T., Guney, D., Hajihassani, M., Hasanipanah, M., Khandelwal, M., Karamani, C., Koopialipoor, M., Kotsonis, E., Le, T-T., Lourenco, P.B., Ly, H-B., Moropoulou, A., Nguyen, H., Pham, B.T., Samui, P. and Zhou, J. (2020), "On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength", Metaheur. Comput. Applicat., Int. J., 1(1), 63-99. http://doi.org/10.12989/mca.2020.1.1.063   DOI
28 Bache, H.H. (1981), "Densified cement/ultrafine particle based materials", Proceedings of the 2nd International Conference on Superplasticizers in Concrete, Ottawa, Canada.
29 Bai, J., Wild, S., Ware, J.A. and Sabir, B.B. (2003), "Using neural networks to predict workability of concrete incorporating metakaolin, and fly ash", Adv. Eng. Softw., 34, 663-669. https://doi.org/10.1016/S0965-9978(03)00102-9   DOI
30 Yang, K.H., Mun, J.S. and Cho, M.S. (2015), "Effect of curing temperature histories on the compressive strength development of high-strength concrete", Adv. Mater. Sci. Eng., 2015. https://doi.org/10.1155/2015/965471   DOI
31 Yikici, T.A. and Chen, H.L.R. (2015), "Use of maturity method to estimate compressive strength of mass concrete", Constr. Build. Mater., 95, 802-812. https://doi.org/10.1016/j.conbuildmat.2015.07.026   DOI
32 Yilmaz, M. and Tugrul, A. (2012), "The effects of different sandstone aggregates on concrete strength", Constr. Build. Mater., 35, 294-303. https://doi.org/10.1016/j.conbuildmat.2012.04.014   DOI
33 Freeman, J.A. and Skapura, D.M. (1991), Neural Networks Algorithms, Applications, and Programming Techniques, Addison-Wesley Publishing.
34 Davies, D.E. and Alexander, M.G. (2012), "Properties of aggregate in concrete (Part 2)", Hippo Quarrie, Sandton, South Africa: Hippo Quarries Technical Publication; 1992. Fig. 9(a) SEM image of KM5 sample, and cement interface and (b) EDS images of KM5 sample, and cement.
35 de Larrard, F. and Belloc, A. (1997), "The influence of aggregate on the compressive strength of normal, and high-strength concrete", ACI Mater. J., 94, 417-425.
36 Fausett, L. (1994), Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Prentice Hall, NJ, USA.
37 Kumar, S. and Barai, S.V. (2010), "Neural networks modeling of shear strength of SFRC corbels without stirrups", Appl. Soft Comput., 10, 135-148. https://doi.org/10.1016/j.asoc.2009.06.012   DOI
38 Mazloom, M. and Yoosefi, M.M. (2013), "Predicting the indirect tensile strength of self-compacting concrete using artificial neural networks", Comput. Concrete, Int. J., 12(3), 285-301. https://doi.org/10.12989/cac.2013.12.3.285   DOI
39 Adeli, H. and Yeh, C. (1989), "Perceptron learning in engineering design", Comput. Aided Civil Infrastruct. Eng., 4, 247-256. https://doi.org/10.1111/j.1467-8667.1989.tb00026.x   DOI
40 Lai, S. and Serra, M. (1997), "Concrete strength prediction by means of neural network", Constr. Build. Mater., 11, 93-98. https://doi.org/10.1016/S0950-0618(97)00007-X   DOI
41 Mehta, P.K. and Monteiro, P.J. (2006), Concrete: microstructure, properties, and materials, (3rd Ed.), The McGraw-Hill, New York, USA.
42 Neville, A.M. (1995b), Properties of concrete, Addison-Wesley Longman, Essex, UK.
43 Oreta, A.W. and Ongpeng, J. (2011), "Modeling the confined compressive strength of hybrid circular concrete columns using neural networks", Comput. Concrete, Int. J., 8(5), 597-616. https://doi.org/10.12989/cac.2011.8.5.597   DOI
44 Ozturan, T. and Cecen, C. (1997), "Effect of coarse aggregate type on mechanical properties of concretes with different strength", Cement Concrete Res., 27, 165-170. https://doi.org/10.1016/S0008-8846(97)00006-9   DOI
45 Parichatprecha, R. and Nimityongskul, P. (2009), "Analysis of durability of high performance concrete using artificial neural networks", Constr. Build. Mater., 23, 910-917. https://doi.org/10.1016/j.conbuildmat.2008.04.015   DOI
46 Rumelhart, D.E. and MacClelland, J.L. (1986), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, MA, USA.
47 Aulia, T.B. and Deutschmann, K. (1999), "Effect of mechanical properties of aggregate on the ductility of high performance concrete", LACER, 4, 133-147.
48 Armaghani, D.J., Momeni, E. and Asteris, P.G. (2020), "Application of group method of data handling technique in assessing deformation of rock mass", Metaheur. Comput. Applicat., Int. J., 1(1), 1-18. http://doi.org/10.12989/mca.2020.1.1.001   DOI
49 Asteris, P.G., Tsaris, A.K., Cavaleri, L., Repapis, C.C., Papalou, A., Di Trapani, F. and Karypidis, D.F. (2016a), "Prediction of the fundamental period of infilled RC frame structures using artificial neural networks", Comput. Intell. Neurosci., 12, 5104907. [PubMed] https://doi.org/10.1155/2016/5104907   DOI
50 Asteris, P.G., Kolovos, K.G., Douvika, M.G. and Roinos, K. (2016b), "Prediction of self-compacting concrete strength using artificial neural networks", Eur. J. Environ. Civil Eng., 20, s102-s122. https://doi.org/10.1080/19648189.2016.1246693   DOI
51 Benaicha, M., Burtschell, Y. and Alaoui, A.H. (2016), "Prediction of compressive strength at early age of concrete - application of maturity", J. Build. Eng., 6, 119-125. https://doi.org/10.1016/j.jobe.2016.03.003   DOI
52 Adhikary, B.B. and Mutsuyoshi, H. (2006), "Prediction of shear strength of steel fiber RC beams using neural networks", Constr. Build. Mater., 20, 801-811. https://doi.org/10.1016/j.conbuildmat.2005.01.047   DOI
53 Asteris, P.G. and Plevris, V. (2016), "Anisotropic masonry failure criterion using artificial neural networks", Neural Comput. Applicat., 28(8), 2207-2229. https://doi.org/10.1007/s00521-016-2181-3   DOI