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http://dx.doi.org/10.12989/sss.2022.29.3.433

Prediction of long-term compressive strength of concrete with admixtures using hybrid swarm-based algorithms  

Huang, Lihua (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction)
Jiang, Wei (School of Intelligent Manufacturing, Zhejiang Guangsha Vocational and Technical University of Construction)
Wang, Yuling (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction)
Zhu, Yirong (Glodon Company Limited)
Afzal, Mansour (Islamic Azad University)
Publication Information
Smart Structures and Systems / v.29, no.3, 2022 , pp. 433-444 More about this Journal
Abstract
Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R2) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.
Keywords
fly ash; high strength concrete; long-term CS prediction; MARS-BBO; MARS-PSO; MARS-PSOBBO; silica fume;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Toutanji, H., Delatte, N., Aggoun, S., Duval, R. and Danson, A. (2004), "Effect of supplementary cementitious materials on the compressive strength and durability of short-term cured concrete", Cement Concrete Res., 34(2), 311-319. https://doi.org/10.1016/j.cemconres.2003.08.017   DOI
2 Turk, K., Turgut, P., Karatas, M. and Benli, A. (2010), "Mechanical properties of self-compacting concrete with silica fume/fly ash", Proceedings of the 9th International Congress on Advances in Civil Engineering, pp. 27-30.
3 Yaprak, H., Karaci, A. and Demir, I. (2013), "Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks", Neural Comput. Applicat., 22(1), 133-141. https://doi.org/10.1007/s00521-011-0671-x   DOI
4 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   DOI
5 Simon, D. (2008), "Biogeography-based optimization", IEEE Transact. Evolut. Computat., 12, 702-713. https://doi.org/10.1109/TEVC.2008.919004   DOI
6 Shariati, M., Mafipour, M.S., Mehrabi, P., Bahadori, A., Zandi, Y., Salih, M.N., Nguyen, H., Dou, J., Song, X. and Poi-Ngian, S. (2019), "Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete", Appl. Sci., 9(24), 5534. https://doi.org/10.3390/app9245534   DOI
7 Wang, C.C., Chen, T.T., Wang, H.Y. and Huang, C. (2014), "A predictive model for compressive strength of waste LCD glass concrete by nonlinear-multivariate regression", Comput. Concrete, Int. J., 13(4), 531-545. http://dx.doi.org/10.12989/cac.2014.13.4.531   DOI
8 Azimi-Pour, M., Eskandari-Naddaf, H. and Pakzad, A. (2020), "Linear and non-linear SVM prediction for fresh properties and compressive strength of high-volume fly ash self-compacting concrete", Constr. Build. Mater., 230, p. 117021. https://doi.org/10.1016/j.conbuildmat.2019.117021   DOI
9 Adamowski, J.F. (2008), "Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross wavelet analysis", J. Hydrol., 353(3-4), 247-266. https://doi.org/10.1016/j.jhydrol.2008.02.013   DOI
10 Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Expert Syst. Applicat., 38(8), 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156   DOI
11 Benemaran, R.S. and Esmaeili-Falak, M. (2020), "Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO", Comput. Concrete, Int. J., 26(4), 309-316. https://doi.org/10.12989/cac.2020.26.4.309   DOI
12 Chou, J.H. and Ghaboussi, J. (2001), "Genetic algorithm in structural damage detection", Comput. Struct., 79(14), 1335-1353. https://doi.org/10.1016/S0045-7949(01)00027-X   DOI
13 Esmaeili-Falak, M., Katebi, H. and Javadi, A.A. (2020b), "Effect of freezing on stress-strain characteristics of granular and cohesive soils", J. Cold Regions Eng., 34(2), 05020001. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000205   DOI
14 Detwiler, R.J., Bhatty, J.I. and Battacharja, S. (1996), Supplementary cementing materials for use in blended cements, No. R&D Bulletin RD112T. http://worldcat.org/isbn/0893121428
15 Esmaeili-Falak, M., Katebi, H., Javadi, A. and Rahimi, S. (2017), "Experimental investigation of stress and strain characteristics of frozen sandy soils-A case study of Tabriz subway", Modares Civil Eng. J., 17(5), 13-23. http://mcej.modares.ac.ir/article-16-7658-en.html
16 Friedman, J.H. (1991), "Multivariate adaptive regression splines", Annals Statist., 19(1), 1-67. https://www.jstor.org/stable/2241837   DOI
17 Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN'95-International Conference on Neural Networks, Volume 4, pp. 1942-1948. https://doi.org/10.1109/ICNN.1995.488968   DOI
18 Esmaeili-Falak, M., Sarkhani Benemaran, R. and Seifi, R. (2020a), "Improvement of the mechanical and durability parameters of construction concrete of the Qotursuyi Spa", Concrete Res. Quarterly J., 13(2), 81-90. https://doi.org/10.22124/JCR.2020.14518.1395   DOI
19 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
20 Zelic, J., Rusic, D. and Krstulovic, R. (2004), "A mathematical model for prediction of compressive strength in cement-silica fume blends", Cement Concrete Res., 34(12), 2319-2328. https://doi.org/10.1016/j.cemconres.2004.04.015   DOI
21 Moayedi, H., Kalantar, B., Foong, L.K., Tien Bui, D. and Motevalli, A. (2019), "Application of three metaheuristic techniques in simulation of concrete slump", Appl. Sci., 9(20), 4340. https://doi.org/10.3390/app9204340   DOI
22 Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Computat. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009   DOI
23 Craven, P. and Wahba, G. (1978), "Smoothing noisy data with spline functions", Numerische mathematik, 31(4), 377-403. https://doi.org/10.1007/BF01404567   DOI
24 Mohamed, O.A. and Najm, O.F. (2016), "Splitting tensile strength of self-consolidating concrete containing slag", Proceedings of AES-ATEMA International Conference, Advances and Trends in Engineering Materials and their Applications, pp. 109-114. https://doi.org/10.1016/j.proeng.2016.04.157   DOI
25 Cabrera, J.G. and Claisse, P.A. (1990), "Measurement of chloride penetration into silica fume concrete", Cement Concrete Compos., 12(3), 157-161. https://doi.org/10.1016/0958-9465(90)90016-Q   DOI
26 Chou, J.S. and Pham, A.D. (2013), "Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength", Constr. Build. Mater., 49, 554-563. https://doi.org/10.1016/j.conbuildmat.2013.08.078   DOI
27 Dutta S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, Int. J., 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463   DOI
28 Behnood, A., Behnood, V., Gharehveran, M.M. and Alyamac, K.E. (2017), "Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm", Constr. Build. Mater., 142, 199-207. https://doi.org/10.1016/j.conbuildmat.2017.03.061   DOI
29 Sarkhani Benemaran, R. (2017), "Experimental and analytical study of pile-stabilized layered slopes", Thesis; University of Tabriz, Tabriz, Iran.
30 Babu, K.G. and Rao, G.S.N. (1994), "Early strength behavior of fly ash concretes", Cement Concrete Res., 24(2), 277-284. https://doi.org/10.1016/0008-8846(94)90053-1   DOI
31 Felekoglu, B., Turkel, S. and Baradan, B. (2007), "Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete", Build. Environ., 42(4), 1795-1802. https://doi.org/10.1016/j.buildenv.2006.01.012   DOI
32 Esmaeili-Falak, M. (2017), "Effect of system's geometry on the stability of frozen wall in excavation of saturated granular soils", Doctoral Dissertation; University of Tabriz, Tabriz, Iran.
33 Esmaeili-Falak, M., Katebi, H. and Javadi, A. (2018), "Experimental study of the mechanical behavior of frozen soils-A case study of tabriz subway", Periodica Polytech. Civil Eng., 62(1), 117-125. https://doi.org/10.3311/PPci.10960   DOI
34 Esmaeili-Falak, M., Katebi, H., Vadiati, M. and Adamowski, J. (2019), "Predicting triaxial compressive strength and Young's modulus of frozen sand using artificial intelligence methods", J. Cold Regions Eng., 33(3), 04019007. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000188   DOI
35 Benhelal, E., Zahedi, G., Shamsaei, E. and Bahadori, A. (2013), "Global strategies and potentials to curb CO2 emissions in cement industry", J. Cleaner Prod., 51, 142-161. https://doi.org/10.1016/j.jclepro.2012.10.049   DOI
36 Kjellsen, K.O., Wallevik, O.H. and Hallgren, M. (1999), "On the compressive strength development of high-performance concrete and paste-effect of silica fume", Mater. Struct., 32(1), 63. https://doi.org/10.1007/BF02480414   DOI
37 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 Software, 45(1), 105-114. https://doi.org/10.1016/j.advengsoft.2011.09.014   DOI
38 Pala, M., Ozbay, E., Oztas, 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   DOI
39 Guo, W.A., Li, W.Z., Zhang, Q., Wang, L., Wu, Q.D. and Ren, H.L. (2014), "Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems", Eng. Optimiz., 46(11), 1465-1484. https://doi.org/10.1080/0305215X.2013.854349   DOI
40 Hubertova, M. and Hela, R. (2007), "The effect of metakaolin and silica fume on the properties of lightweight self-consolidating concrete", Special Publication, 243, 35-48.
41 Siddique, R. (2004), "Performance characteristics of high-volume Class F fly ash concrete", Cement Concrete Res., 34(3), 487-493. https://doi.org/10.1016/j.cemconres.2003.09.002   DOI
42 Lam, L., Wong, Y.L. and Poon, C.S. (1998), "Effect of fly ash and silica fume on compressive and fracture behaviors of concrete", Cement Concrete Res., 28(2), 271-283. https://doi.org/10.1016/S0008-8846(97)00269-X   DOI
43 Sarkhani Benemaran, R., Esmaeili-Falak, M. and Katebi, H. (2020), "Physical and numerical modelling of pile-stabilised saturated layered slopes", Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, pp. 1-16. https://doi.org/10.1680/jgeen.20.00152   DOI
44 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   DOI
45 Nochaiya, T., Wongkeo, W. and Chaipanich, A. (2010), "Utilization of fly ash with silica fume and properties of Portland cement-fly ash-silica fume concrete", Fuel, 89(3), 768-774. https://doi.org/10.1016/j.fuel.2009.10.003   DOI
46 Poorjafar, A., Esmaeili-Falak, M. and Katebi, H. (2021), "Pile-soil interaction determined by laterally loaded fixed head pile group", Geomech. Eng., Int. J., 26(1), 13-25. https://doi.org/10.12989/gae.2021.26.1.013   DOI