Browse > Article
http://dx.doi.org/10.12989/cac.2019.24.2.137

Prediction of the compressive strength of self-compacting concrete using surrogate models  

Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education)
Ashrafian, Ali (Department of Civil Engineering, Tabari University of Babol)
Rezaie-Balf, Mohammad (Department of Civil Engineering, Graduate University of Advanced Technology-Kerman)
Publication Information
Computers and Concrete / v.24, no.2, 2019 , pp. 137-150 More about this Journal
Abstract
In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.
Keywords
artificial intelligence models; compressive strength; multivariate adaptive regression splines (MARS); M5P model tree; self-compacting concrete; surrogate models;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Witten, I.H., Frank, E. and Hall, M.A. (2005), "Data mining: practical machine learning tools and techniques with Java implementations", Morgan Kaufmann Series in Data Management Systems, San Francisco.
2 Zhang, W.G. and Goh, A.T.C. (2013), "Multivariate adaptive regression splines for analysis of geotechnical engineering systems", Comput. Geotech., 48, 82-95. https://doi.org/10.1016/j.compgeo.2012.09.016.   DOI
3 Zhang, W.G. and Goh, A.T.C. (2016), "Multivariate adaptive regression splines and neural network models for prediction of pile drivability", Geosci. Front., 7(1), 45-52. https://doi.org/10.1016/j.gsf.2014.10.003.   DOI
4 Abouhussien, A.A., Hassan, A.A.A. and Ismail, M.K. (2015), "Properties of semi-lightweight self-consolidating concrete containing lightweight slag aggregate", Constr. Build. Mater., 75, 63-73. https://doi.org/10.1016/j.conbuildmat.2014.10.028.   DOI
5 Aggarwal, P., Siiddique, R., Aggarwal, Y. and Gupta, S.M. (2008), "Self-compacting concrete-procedure for mix design", Leonardo Elec. J. Pract. Technol., 7(12), 15-24.
6 Ahari, R.S., Erdem, T.K. and Ramyar, K. (2015), "Timedependent rheological characteristics of self-consolidating concrete containing various mineral admixtures", Constr. Build. Mater., 88, 134-142. https://doi.org/10.1016/j.conbuildmat.2015.04.015.   DOI
7 Apostolopoulour, 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.
8 Ahmed, S. (2009), "Fresh and mechanical properties of selfconsolodating concrete incorporationg silica fume and metakaolin", Theses and dissertation of Master of Civil Engineering, Reyson University.
9 Alabi, S.A., Olanitori, L.M. and Afolayan, J.O. (2012), "Optimum mix design for minimum concrete strength requirement using akure pit-sand as fine aggregate", J. Emerg. Trend. Eng. Appl. Sci., 3(4), 718-724.
10 Alyhya, W.S. (2016), "Self-compacting concrete: mix proportioning, properties and its flow simulation in the Vfunnel", Doctoral Dissertation, Cardiff University.
11 Ashrafian, A., Amiri, M.J.T., Rezaie-Balf, M., Ozbakkaloglu, T. and Lotfi-Omran, O. (2018), "Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods", Constr. Build. Mater., 190, 479-494, https://doi.org/10.1016/j.conbuildmat.2018.09.047.   DOI
12 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.   DOI
13 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., 1-11. https://doi.org/10.1007/s00521-018-03965-1.
14 Asteris, P.G., Roussis, P.C. and Douvika, M.G. (2017), "Feedforward neural network prediction of the mechanical properties of sandcrete materials", Sens., (Switzerland), 17(6), 1344.   DOI
15 Asteris, P.G., Argyropoulos, I., Cavaleri, L., Rodrigues, H., Varum, H., Thomas, J., Paulo B. and Lourenco, P.B. (2018), "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.
16 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(sup1), s102-s122, https://doi.org/10.1080/19648189.2016.1246693.   DOI
17 Asteris, P.G., Nozhati, S., Nikoo, M., Cavaleri, L. and Nikoo, M. (2018), "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.
18 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. Intel. Neurosci., 5104907. https://doi.org/10.1155/2016/5104907.
19 AzariJafari, H., Amiri, M.J.T., Ashrafian, A., Rasekh, H., Barforooshi, M.J. and Berenjian, J. (2019), "Ternary blended cement: an eco-friendly alternative to improve resistivity of high-performance self-consolidating concrete against elevated temperature", J. Clean. Prod., 223, 575-586. https://doi.org/10.1016/j.jclepro.2019.03.054.   DOI
20 Badogiannis, E.G., Sfikas, I.P., Voukia, D.V., Trezos, K.G. and Tsivilis, S.G. (2015), "Durability of metakaolin self-compacting concrete", Constr. Build. Mater., 82, 133-141. https://doi.org/10.1016/j.conbuildmat.2015.02.023.   DOI
21 Dadsetan, S. and Bai, J. (2017), "Mechanical and microstructural properties of self-compacting concrete blended with metakaolin, ground granulated blast-furnace slag and fly ash", Constr. Build. Mater., 146, 658-667. https://doi.org/10.1016/j.conbuildmat.2017.04.158.   DOI
22 Chen, H., Asteris, P.G., Armaghani, D.J., Gordan, B. and Pham, B.T. (2019), "Assessing dynamic conditions of the retaining wall using two hybrid intelligent models", Appl. Sci., 9, 1042. https://doi.org/10.3390/app9061042.   DOI
23 Chitroju, S.T.D. and Yerikenaboina, A. (2018), "Study the influence of metakaolin and foundry sand on self-compacting concrete properites", Int. Res. J. Eng. Technol. (IRJET), 5(4), 4027-4033.
24 Coleman, N.J. and Page, C.L. (1997), "Aspects of the pore solution chemistry of hydrated cement pastes containing metakaolin", Cement Concrete Res., 27(1), 147-154. https://doi.org/10.1016/S0008-8846(96)00184-6.   DOI
25 Dinakar, P. and Manu, S.N. (2014), "Concrete mix design for high strength self-compacting concrete using metakaolin", Mater. Des., 60, 661-668. https://doi.org/10.1016/j.matdes.2014.03.053.   DOI
26 Ferreira, R.M., Castro-Gomes, J.P., Costa, P. and Malheiro, R. (2016), "Effect of metakaolin on the chloride ingress properties of concrete", KSCE J. Civil Eng., 20(4), 1375-1384. https://doi.org/10.1007/s12205-015-0131-8.   DOI
27 Frias, and Cabrera, J. (2000), "Pore size distribution and degree of hydration of metakaolin-cement pastes", Cement Concrete Res., 30(4), 561-569. https://doi.org/10.1016/S0008-8846(00)00203-9.   DOI
28 Friedman, J.H. (1991), "Multivariate adaptive regression splines", Ann. Statist., 19(1), 1-67.   DOI
29 Gilan, S.S., Bahrami Jovein, H. and Ramezanianpour, A.A. (2012), "Hybrid support vector regression-Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin", Constr. Build. Mater., 34, 321-329. https://doi.org/10.1016/j.conbuildmat.2012.02.038.   DOI
30 Gholampour, A.A., Gandomi, A.H. and Ozbakkaloglu, T. (2017), "New formulations for mechanical properties of recycled aggregate concrete using gene expression programming", Constr. Build. Mater., 130, 122-145. https://doi.org/10.1016/j.conbuildmat.2016.10.114.   DOI
31 Gilan, S.S., Jovein, H.B. and Ramezanianpour, A.A. (2012), "Hybrid support vector regression-particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin", Constr. Build. Mater., 34, 321-329. https://doi.org/10.1016/j.conbuildmat.2012.02.038.   DOI
32 Gill, A.S. and Siddique, R. (2018), "Durability properties of selfcompacting concrete incorporating metakaolin and rice husk ash", Constr. Build. Mater., 176, 323-332. https://doi.org/10.1016/j.conbuildmat.2018.05.054.   DOI
33 Guneyisi, E., Gesoglu, M. and O zbay, E. (2009), "Evaluating and forecasting the initial and final setting times of self-compacting concretes containing mineral admixtures by neural network", Mater. Struct., 42(4), 469-484. https://doi.org/10.1617/s11527-008-9395-5.   DOI
34 Hassan, A.A.A., Ismail, M.K. and Mayo, J. (2015), "Mechanical properties of self-consolidating concrete containing lightweight recycled aggregate in different mixture compositions", J. Build. Eng., 4, 113-126. https://doi.org/10.1016/j.jobe.2015.09.005.   DOI
35 Hassan, A.A.A., Lachemi, M. and Hossain, K.M.A. (2012a), "Effect of metakaolin and silica fume on the durability of selfconsolidating concrete", Cement Concrete Compos., 34(6), 801-807. https://doi.org/10.1016/j.cemconcomp.2012.02.013.   DOI
36 Johari, M.M., Brooks, J.J., Kabir, S. and Rivard, P. (2011), "Influence of supplementary cementitious materials on engineering properties of high strength concrete", Constr. Build. Mater., 25(5), 2639-2648. https://doi.org/10.1016/j.conbuildmat.2010.12.013.   DOI
37 Hassan, A.A.A., Lachemi, M. and Hossain, K.M.A. (2012b), "Effect of metakaolin and silica fume on the durability of selfconsolidating concrete", Cement Concrete Compos., 34(6), 801-807. https://doi.org/10.1016/j.cemconcomp.2012.02.013.   DOI
38 Hastie, T., Tibshirani, R. and Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd Edition, Springer.
39 Jekabsons, G. (2010), "VariReg: a software tool for regression modelling using various modeling methods", Riga Technical University, http://www.cs.rtu.lv/jekabsons/.
40 Joseph, A., Mathew, L.A. and John, R. (2017), "Performance of Metakaolin on high strength self compacting concrete", Int. J. Sci. Technol. Eng., 3(12), 110-114.
41 Justice, J.M. and Kurtis, K.E. (2007), "Influence of metakaolin surface area on properties of cement-based materials", J. Mater. Civil Eng., 19(9), 762-771. https://doi.org/10.1061/(ASCE)0899-1561(2007)19:9(762).   DOI
42 Kannan, V. and Ganesan, K. (2015), "Effect of Tricalcium aluminate on durability properties of self-compacting concrete incorporating rice husk ash and Metakaolin", J. Mater. Civil Eng., 28(1), 04015063. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001330.   DOI
43 Kavitha, O.R., Shanthi, V.M., Arulraj, G.P. and Sivakumar, P. (2015), "Fresh, micro-and macrolevel studies of metakaolin blended self-compacting concrete", Appl. Clay Sci., 114, 370-374. https://doi.org/10.1016/j.clay.2015.06.024.   DOI
44 Lenka, S. and Panda, K.C. (2017), "Effect of metakaolin on the properties of conventional and self compacting concrete", Adv. Concrete Constr., 5(1), 31-48. https://doi.org/10.12989/acc.2017.5.1.31.   DOI
45 Khatib, J.M. (2008), "Metakaolin concrete at a low water to binder ratio", Constr. Build. Mater., 22(8), 1691-1700. https://doi.org/10.1016/j.conbuildmat.2007.06.003.   DOI
46 Khatib, J.M. (2008), "Performance of self-compacting concrete containing fly ash", Constr. Build. Mater., 22(9), 1963-1971. https://doi.org/10.1016/j.conbuildmat.2007.07.011.   DOI
47 Kiani, B., Gandomi, A.H., Sajedi, S. and Liang, R.Y. (2016), "New formulation of compressive strength of preformed-foam cellular concrete: an evolutionary approach", J. Mater. Civil Eng., 28(10), 04016092. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001602.   DOI
48 Madandoust, R. and Mousavi, S.Y. (2012), "Fresh and hardened properties of self-compacting concrete containing metakaolin", Constr. Build. Mater., 35, 752-760. https://doi.org/10.1016/j.conbuildmat.2012.04.109.   DOI
49 Mansouri, I., Ozbakkaloglu, T., Kisi, O. and Xie, T. (2016), "Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques", Mater. Struct., 49(10), 4319-4334. https://doi.org/10.1617/s11527-015-0790-4.   DOI
50 Mehrinejad Khotbehsara, M., Mohseni, E., Ozbakkaloglu, T. and Ranjbar, M.M. (2017), "Durability characteristics of selfcompacting concrete incorporating pumice and metakaolin", J. Mater. Civil Eng., 29(11), 04017218. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002068.   DOI
51 Mehta, P.K. (1978), "Siliceous ashes and hydraulic cements prepared therefrom", U.S. Patent No. 4,105,459, The Regents of the University of California.
52 Rezaie-Balf, M., Zahmatkesh, Z. and Kim, S. (2017), "Soft computing techniques for rainfall-runoff simulation: Local nonparametric paradigm vs. model classification methods", Water Resour. Manage., 31(12), 3843-3865. https://doi.org/10.1007/s11269-017-1711-9.   DOI
53 Poon, C.S., Kou, S.C. and Lam, L. (2006), "Compressive strength, chloride diffusivity and pore structure of high performance metakaolin and silica fume concrete", Constr. Build. Mater., 20(10), 858-865. https://doi.org/10.1016/j.conbuildmat.2005.07.001.   DOI
54 Quinlan, J.R. (1992), "Learning with continuous classes", Ed. Adams, S., Proceedings of AI'92. World Scientific, 343-348.
55 Ramezanianpour, A.A. and Jovein, H.B. (2012), "Influence of metakaolin as supplementary cementing material on strength and durability of concretes", Constr. Build. Mater., 30, 470-479. https://doi.org/10.1016/j.conbuildmat.2011.12.050.   DOI
56 Sabir, B.B., Wild, S. and Bai, J. (2001), "Metakaolin and calcined clays as pozzolans for concrete: a review", Cement Concrete Compos., 23(6), 441-454. https://doi.org/10.1016/S0958-9465(00)00092-5.   DOI
57 Sattar, A.M.A. and Gharabaghi, B. (2015), "Gene expression models for prediction of longitudinal dispersion coefficient in streams", J. Hydrol., 524, 587-596. https://doi.org/10.1016/j.jhydrol.2015.03.016.   DOI
58 Sfikas, I.P., Badogiannis, E.G. and Trezos, K.G. (2014), "Rheology and mechanical characteristics of self-compacting concrete mixtures containing metakaolin", Constr. Build. Mater., 64, 121-129. https://doi.org/10.1016/j.conbuildmat.2014.04.048.   DOI
59 Sipos, T K , ilicevic, I and Siddique, R. (2017), "Model for mix design of brick aggregate concrete based on neural network modelling", Constr. Build. Mater., 148, 757-769. https://doi.org/10.1016/j.conbuildmat.2017.05.111.   DOI
60 Sonebi, M., Cevik, A., Grunewald, S. and Walravan, J. (2016b), "Modelling the fresh properties of self-compacting concrete using support vector machine approach", Constr. Build. Mater., 106, 55-64. https://doi.org/10.1016/j.conbuildmat.2015.12.035.   DOI
61 Sonebi, M., Grunewald, S., Cevik, A. and Walraven, J. (2016a), "Modelling fresh properties of self-compacting concrete using Neural network technique", Comput. Concrete, 18(4), 903-921. https://doi.org/10.12989/cac.2016.18.4.903.   DOI
62 Tropsha, A., Gramatica, P. and Gombar, V.K. (2003), "The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models", QSAR Combin. Sci., 22(1), 69-77. https://doi.org/10.1002/qsar.200390007.   DOI
63 Wang, B., Man, T. and Jin, H. (2015), "Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems", Constr. Build. Mater., 84, 184-191. https://doi.org/doi.org/10.1016/j.conbuildmat.2015.03.059.   DOI
64 Wang, Y. and Witten, I.H. (1997), "Induction of model trees for predicting continuous lasses", Proceedings of the Poster Papers of the European Conference on Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague.
65 Wild, S., Khatib, J.M. and Jones, A. (1996), "Relative strength, pozzolanic activity and cement hydration in superplasticised metakaolin concrete", Cement Concrete Res., 26(10), 1537-1544. https://doi.org/10.1016/0008-8846(96)00148-2.   DOI
66 Wild, S., Khatib, J.M. and Jones, A. (1996), "Relative strength, pozzolanic activity and cement hydration in superplasticised metakaolin concrete", Cement Concrete Res., 26(10), 1537-1544. https://doi.org/10.1016/0008-8846(96)00148-2.   DOI