Advances in concrete construction

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of the compressive and tensile strength of HPC concrete with fly ash and micro-silica using hybrid algorithms

  • Received : 2020.12.29
  • Accepted : 2021.09.29
  • Published : 2021.10.25
⟨ Previous Next ⟩

Abstract

Evaluating the impact of fly ash (FA) and micro-silica (MS) on the tensile (TS) and compressive strength (CS) of concrete in different ages provokes to find the effective parameters in predicting the CS and TS, which not only could be usable in the practical works but also is extensible in the future analysis. In this study, in order to evaluate the effective parameters in predicting the CS and TS of concrete containing admixtures and to 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, hybrid genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods have been utilized to find the optimal conclusions. It could be concluded that for both predictions of CS and TS, all models have the coefficient of determination (R2) larger than 0.949 and 0.9138, respectively. Furthermore, between three hybrid algorithms, MARS-PSO could be proposed as the best model to obtain the most accuracy in the prediction of CS and TS. The usage of hybrid MARS-PSO techniques causes a noticeable improvement in the prediction procedure.

Keywords

Acknowledgement

National Natural Science Foundation of China (NO.51774173. NO.51474045)

References

  1. 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. Hydro., 353(3-4), 247-266. https://doi.org/10.1016/j.jhydrol.2008.02.013.
  2. Ahmed, H.U., Mohammed, A.S., Mohammed, A.A. and Faraj, R.H. (2021), "Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes", Plos One, 16(6), e0253006. https://doi.org/10.1371/journal.pone.0253006.
  3. Armaghani, D.J., Mirzaei, F., Shariati, M., Trung, N.T., Shariati, M. and Trnavac, D. (2020), "Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber", Geomech. Eng., 20(3), 191-205. https://doi.org/10.12989/gae.2020.20.3.191.
  4. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38(8), 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156.
  5. 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, 117021. https://doi.org/10.1016/j.conbuildmat.2019.117021.
  6. 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
  7. 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.
  8. Benhelal, E., Zahedi, G., Shamsaei, E. and Bahadori, A. (2013), "Global strategies and potentials to curb CO2 emissions in cement industry", J. Clean. Prod., 51, 142-161. https://doi.org/10.1016/j.jclepro.2012.10.049.
  9. BS EN 12390-3 (2019), Testing Hardened Concrete, Compressive Strength of Test Specimens.
  10. BS EN 12390-6 (2009), Testing Hardened Concrete, Tensile Splitting Strength of Test Specimens.
  11. 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.
  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.
  13. 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.
  14. Craven, P. and Wahba, G. (1978), "Smoothing noisy data with spline functions", Numerische Mathematik, 31(4), 377-403. https://doi.org/10.1007/BF01404567.
  15. Detwiler, R.J., Bhatty, J.I. and Battacharja, S. (1996), Supplementary Cementing Materials for Use in Blended Cements. http://worldcat.org/isbn/0893121428.
  16. 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.
  17. Esmaeili-choobar, N., Esmaeili-falak, M., Roohi-hir, M. amd Keshtzad, S. (2013), "Evaluation of collapsibility potential at Talesh, Iran", EJGE, 2561-2573.
  18. Esmaeili-Falak, M. (2017), "Effect of system's geometry on the stability of frozen wall in excavation of saturated granular soils", Ph.D. Dissertation of Philosophy, University of Tabriz.
  19. 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.
  20. Esmaeili-Falak, M., Sarkhani Benemaran, R. and Seifi, R. (2020), "Improvement of the Mechanical and Durability Parameters of Construction Concrete of the Qotursuyi Spa", Concrete Res., 13(2), 119-134. https://doi.org/10.22124/JCR.2020.14518.1395.
  21. Faraj, R.H., Mohammed, A.A., Mohammed, A., Omer, K.M. and Ahmed, H.U. (2021), "Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages," Eng. Comput., 1-24. https://doi.org/10.1007/s00366-021-01385-9.
  22. 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.
  23. Friedman, J.H. (1991), "Multivariate adaptive regression splines", Annals Statist., 1-67.
  24. Hubertova, M. and Hela, R. (2007), "The effect of metakaolin and silica fume on the properties of lightweight self-consolidating concrete", Spec. Pub., 243, 35-48.
  25. 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.
  26. 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.
  27. 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.
  28. Liu, F., Ding, W. and Qiao, Y. (2019), "Experimental investigation on the flexural behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder", Constr. Build. Mater., 228, 116706. https://doi.org/10.1016/j.conbuildmat.2020.118000.
  29. 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.
  30. Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey wolf optimizer", Adv. Eng. Softw., 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
  31. Mohamed, O.A., Syed, Z.I. and Najm, O.F. (2016), "Splitting tensile strength of sustainable self-consolidating concrete", Procedia Eng., 145, 1218-1225. http://doi.org/10.1016/j.proeng.2016.04.157.
  32. 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.
  33. Muro, C., Escobedo, R., Spector, L. and Coppinger, R.P. (2011), "Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations", Behav. Proc., 88(3), 192-197. https://doi.org/10.1016/j.beproc.2011.09.006.
  34. 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.
  35. 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.
  36. Patel, P.J. (2014), "Health analysis of high performance concrete by using waste material", Ph.D. Dissertation of Philosophy, Ganpat University.
  37. Pham, B.T., Qi, C., Ho, L.S., Nguyen-Thoi, T., Al-Ansari, N., Nguyen, M.D. and Prakash, I. (2020), "A novel hybrid soft computing model using random forest and particle swarm optimization for estimation of undrained shear strength of soil", Sustain., 12(6), 2218. https://doi.org/10.3390/su12062218.
  38. Qiu, M., Ming, Z., Li, J., Gai, K. and Zong, Z. (2015), "Phase-change memory optimization for green cloud with genetic algorithm", IEEE Transac. Comput., 64(12), 3528-3540. https://doi.org/10.1109/TC.2015.2409857.
  39. Sarkhani Benemaran, R., Esmaeili-Falak, M. and Katebi, H. (2020), "Physical and numerical modelling of pile-stabilised saturated layered slopes", Proc. Inst. Civil Eng. Geotech. Eng., 1-16. https://doi.org/10.1680/jgeen.20.00152.
  40. Sarkhani, B.R. and Esmaeili-falak M. (2020), "Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO", Comput. Concrete, 26(4), 309-316. https://doi.org/10.12989/cac.2020.26.4.000.
  41. Shariati, M., Mafipour, M.S., Ghahremani, B., Azarhomayun, F., Ahmadi, M., Trung, N.T. and Shariati, A. (2020), "A novel hybrid extreme learning machine-grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement", Eng. Comput., 1-23. https://doi.org/10.1007/s00366-020-01081-0.
  42. 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.
  43. 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.
  44. Topcu, 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(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009.
  45. 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.
  46. Turk, K., Turgut, P., Karatas, M. and Benli, A. (2010), "Mechanical properties of selfcompacting concrete with silica fume/fly ash", 9th Int. Cong. Adv. Civil Eng., 27-30.
  47. 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, 13(4), 531-545. http://doi.org/10.12989/cac.2014.13.4.531.
  48. 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", Neur. Comput. Appl., 22(1), 133-141. https://doi.org/10.1007/s00521-011-0671-x.
  49. 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.
  50. Yu, Z., Shi, X., Zhou, J., Rao, D., Chen, X., Dong, W. and Ipangelwa, T. (2019), "Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models", Eng. Comput., 1-16. https://doi.org/10.1007/s00366-019-00868-0.
  51. Zain, M.F.M., Mahmud, H.B., Ilham, A. and Faizal, M. (2002), "Prediction of splitting tensile strength of high-performance concrete", Cement Concrete Res., 32(8), 1251-1258. https://doi.org/10.1016/S0008-8846(02)00768-8.
  52. 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.
  53. Zhou, J., Enming, L., Haixia, W., Chuanqi, L., Qiuqiu, Q. and Armaghani, D.J. (2019), "Random forests and cubist algorithms for predicting shear strengths of rockfill materials", Appl. Sci., 9(8), 1621. https://doi.org/10.3390/app9081621.