Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach

Abstract

This article presents the feasibility of using support vector regression (SVR) technique to determine the fresh and hardened properties of self-compacting concrete. Two different kernel functions, namely exponential radial basis function (ERBF) and radial basis function (RBF), were used to develop the SVR model. An experimental database of 115 data samples was collected from different literatures to develop the SVR model. The data used in SVR model have been organized in the form of six input parameters that covers dosage of binder content, fly ash, water–powder ratio, fine aggregate, coarse aggregate and superplasticiser. The above-mentioned ingredients have been taken as input variables, whereas slump flow value, L-box ratio, V-funnel time and compressive strength have been considered as output variables. The obtained results indicate that the SVR–ERBF model outperforms SVR–RBF model for learning and predicting the experimental data with the highest value of the coefficient of correlation (R) equal to 0.965, 0.954, 0.979 and 0.9773 for slump flow, L-box ratio, V-funnel and compressive strength, respectively, with small values of statistical errors. Also, the efficiency of SVR model is compared to artificial neural network (ANN) and multivariable regression analysis (MVR). In addition, a sensitivity analysis was also carried out to determine the effects of various input parameters on output. This study indicates that SVR–ERBF model can be used as an alternative approach in predicting the properties of self-compacting concrete.

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Appendix: Details of experimental variables and test results

Appendix: Details of experimental variables and test results

Author Year B P W/B F C SP D (mm) L-box V-funnel Fc28
Sahmaran et al. [41] 2009 500 0 0.35 1038 639 6.75 665 0.87 12.7 62.2
   500 30 0.34 1006 620 6.75 765 0.95 10.2 52.4
   500 30 0.35 1008 621 6.75 715 0.95 15.8 57.3
   500 40 0.35 995 613 6.75 730 0.85 10.7 59.1
   500 40 0.32 1004 618 6.75 745 0.95 11.7 52.3
   500 50 0.35 988 608 6.75 710 0.9 19.2 40.8
   500 50 0.3 1010 628 6.75 738 0.88 15.1 47.5
   500 60 0.35 979 603 6.75 740 0.85 12.8 38.1
   500 60 0.3 997 614 6.75 770 0.95 9.4 39.9
Siddique [42] 2012 550 15 0.41 910 590 10.45 590 0.95 6.5 29
   550 15 0.41 910 590 10.72 675 0.9 7.5 35.5
   550 20 0.41 910 590 6.6 600 0.7 4.8 24
   550 20 0.41 910 590 7.15 645 0.95 4.5 27
   550 20 0.41 910 590 9.9 605 0.82 7.5 32
   550 20 0.41 910 590 11 690 0.9 4.5 33.5
   550 25 0.42 910 590 7.7 600 0.6 7 26
   550 25 0.42 910 590 8.25 625 0.8 5.2 28
   550 25 0.42 910 590 9.9 605 0.6 7 32
   550 25 0.42 910 590 11 590 0.6 4.2 21.7
   550 30 0.43 910 590 7.15 610 0.87 5.4 21
   550 30 0.43 910 590 7.7 600 0.9 6.5 25.5
   550 30 0.43 910 590 8.8 605 0.7 8.9 27.5
   550 30 0.43 910 590 9.9 675 0.95 5 31
   550 35 0.44 910 590 7.15 590 0.86 6.1 17
   550 35 0.44 910 590 8.8 590 0.8 8 23
   550 35 0.44 910 590 9.35 645 0.9 9 25
   550 35 0.44 910 590 9.9 635 0.92 10 29.5
   500 30 0.35 900 600 11 660 0.9 9 29.2
   500 40 0.35 900 600 10.75 675 0.93 7 28.6
Uysal and Yilmaz [43] 2011 550 25 0.33 887 752 8.8 740 0.93 11.7 73.4
   550 35 0.33 878 742 8.8 750 0.91 17 67.5
   550 15 0.41 910 590 9.9 625 0.82 4 26.5
   550 15 0.41 910 590 10.17 675 0.8 6.6 36
Patel [44] 2003 400 30 0.39 946 900 1.4 510 0.96 4.5 45
   370 36 0.43 960 900 1.85 650 0.94 3 46
   430 36 0.43 830 900 0.86 480 0.6 2.5 36
   430 36 0.43 827 900 2.15 810 0.95 2 48
   400 45 0.45 850 900 1.4 760 1 2.5 38
   400 45 0.39 916 900 1.4 580 1 3 45
   400 45 0.39 916 900 1.4 600 1 3 47
   400 45 0.39 916 900 1.4 570 1 3 49
   400 45 0.39 916 900 1.4 590 1 3.3 49
   400 45 0.39 916 900 1.4 590 1 3.5 49
   400 45 0.39 916 900 2.4 770 1 3.5 43
   450 45 0.39 808 900 1.58 680 1 2.3 50
   370 54 0.43 930 900 0.74 600 1 2.8 31
   370 54 0.43 928 900 1.85 760 1 2.5 33
   430 54 0.34 874 900 0.86 540 0.87 3.3 46
   430 54 0.36 872 900 2.15 710 1 4 52
   400 60 0.39 886 900 1.4 630 0.91 3.5 44
Gettu et al. [45] 2002 701 37 0.27 774 723 8.1 580 0.8 10 69.5
   733 37 0.26 748 698 8.4 660 0.9 12 68.2
   550 20 0.41 910 590 11.01 690 0.95 4.5 33.2
Siddique et al. [6] 2011 550 25 0.42 910 590 9.91 603 0.85 5.2 31.5
   550 30 0.43 910 590 9.91 673 0.95 6.1 30.7
   550 35 0.44 910 590 9.91 633 0.92 10 29.6
   550 0 0.33 869 778 8.8 690 0.82 14.5 75.9
   550 15 0.33 865 762 8.8 710 0.91 9.4 74.2
Güneyisi et al. [46] 2010 550 0 0.44 826 868 3.5 670 0.71 3.2 61.5
   550 0 0.32 728 935 8.43 670 0.79 17 80.9
   550 20 0.44 813 855 3.2 675 0.71 10.4 52.1
   550 20 0.32 714 917 7.43 730 0.93 7 69.8
   550 40 0.44 801 842 2.96 730 0.8 6 44.7
   550 40 0.32 700 899 7.43 730 0.96 6 60.9
   550 60 0.44 788 829 3 720 0.95 4 30.3
   550 60 0.32 686 881 6.67 730 0.9 7 47.5
   633 0 0.27 656 875 20.58 635 0.79 13.2 86.8
Nepomuceno et al. [47] 2014 643 0 0.29 761 729 19.95 630 0.86 9.9 81.9
   670 0 0.27 695 772 21.84 620 0.81 10.4 85
   551 16 0.31 822 772 11.34 625 0.7 11.6 59.6
   564 16 0.31 841 729 11.55 630 0.77 10.3 56.8
   588 16 0.28 752 820 12.39 635 0.77 11 64.8
   604 16 0.28 772 772 12.71 625 0.8 9.7 63.1
   613 16 0.26 686 875 12.92 615 0.77 12.7 67.5
   618 16 0.28 790 729 13.02 640 0.83 11.6 63.6
   649 16 0.26 726 772 13.65 650 0.84 10 69.1
   613 24 0.26 685 875 15.33 645 0.8 13.3 78.2
   633 24 0.26 706 820 15.86 630 0.79 12.4 79.2
   649 24 0.26 726 772 16.28 655 0.84 10.5 80.3
   567 25 0.3 846 729 13.86 655 0.82 11.3 69.9
   607 25 0.27 774 772 15.12 640 0.83 10.8 74.5
   620 25 0.27 792 729 15.54 635 0.83 10.1 75.7
Bingol and Tohumcu [48] 2013 500 40 0.35 923 663 7.5 680 0.88 6.2 55
   500 55 0.35 908 652 7.5 700 0.91 7 42.7
   450 0 0.45 890 810 9.25 687 0.8 9 50
   480 0 0.4 890 810 13.3 650 0.88 12 52
Krishnapal et al. [49] 2013 450 10 0.45 890 810 8.2 689 0.79 8.6 45
   480 10 0.4 890 810 9.9 665 0.85 9 46
   450 20 0.45 890 810 6.4 690 0.78 8 41
   480 20 0.4 890 810 9.68 685 0.82 8.4 42
   450 30 0.45 890 810 4.8 695 0.78 8 39
   480 30 0.4 890 810 9.4 680 0.8 8.1 40
   575 0 0.31 794 772 17.22 645 0.75 13.3 77.8
   589 0 0.31 813 729 17.64 640 0.75 10.6 76.8
   628 0 0.29 744 772 19.53 615 0.77 11.6 82.9
Dhiyaneshwaran et al. [50] 2013 530 20 0.45 768 668 4.55 680 0.95 9.8 37.9
   530 30 0.45 768 668 4.55 690 0.95 8.5 41.4
   530 40 0.45 768 668 4.55 685 0.95 7.9 37.2
   530 50 0.45 768 668 4.55 678 0.95 7.6 35.9
   500 0 0.35 967 694 8 630 0.84 6.1 78.6
   500 25 0.35 938 673 7.5 660 0.85 7 62
Mahalingam and Nagamani [51] 2011 450 30 0.43 789 926 2.77 660 0.88 3.5 44.8
   500 30 0.39 731 862 6.15 640 0.75 2.5 53.6
   550 30 0.35 711 835 4.74 610 0.86 3.2 57.3
   450 40 0.43 780 917 2.77 650 0.88 3.7 41.3
   500 40 0.39 724 850 6.15 680 0.88 2.3 46.7
   550 40 0.35 701 823 6.77 730 0.9 3.4 54.9
   450 50 0.43 770 907 2.5 675 0.72 2.7 37.1
   500 50 0.39 714 836 4.92 730 0.88 2.9 41.8
   550 50 0.35 703 824 5.41 725 0.88 2.4 44.4
   550 15 0.41 910 590 10.73 673 0.89 7.5 35.2
Muthupriya et al. [52] 2012 500 50 0.35 900 600 10.5 680 0.95 7.2 28.7
   530 0 0.45 768 668 4.55 660 0.92 12 30
   530 10 0.45 768 668 4.55 675 0.93 10.6 32.2

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Saha, P., Debnath, P. & Thomas, P. Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach. Neural Comput & Applic 32, 7995–8010 (2020). https://doi.org/10.1007/s00521-019-04267-w

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Keywords

  • Support vector regression
  • Kernel functions
  • Self-compacting concrete
  • Compressive strength