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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2019, Vol. 13 Issue (1) : 215-239     https://doi.org/10.1007/s11709-018-0489-z
RESEARCH ARTICLE |
Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine
Ali Reza GHANIZADEH1(), Hakime ABBASLOU1, Amir Tavana AMLASHI1, Pourya ALIDOUST2
1. Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
2. Department of Civil Engineering, Iran University of Science & Technology, Tehran, Iran
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Abstract

Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.

Keywords bentonite/sepiolite plastic concrete      compressive strength      artificial neural network      support vector machine      parametric analysis     
Corresponding Authors: Ali Reza GHANIZADEH   
Online First Date: 01 June 2018    Issue Date: 04 January 2019
 Cite this article:   
Ali Reza GHANIZADEH,Hakime ABBASLOU,Amir Tavana AMLASHI, et al. Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine[J]. Front. Struct. Civ. Eng., 2019, 13(1): 215-239.
 URL:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-018-0489-z
http://journal.hep.com.cn/fsce/EN/Y2019/V13/I1/215
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Ali Reza GHANIZADEH
Hakime ABBASLOU
Amir Tavana AMLASHI
Pourya ALIDOUST
Cement SiO2 Al2O3 Fe2O3 CaO MgO SO3 LOI* Total
Type II 21.90 5.09 3.90 62.40 1.90 1.83 1.40 98.42
Tab.1  Chemical analysis of cement Type II.
Clay SiO2 Al2O3 Fe2O3 CaO Na2O K2O MgO TiO2 MnO P2O5 SO3 LOI*
Bentonite 66.37 13.24 2.04 1.79 1.69 0.51 2.37 0.132 0.021 0.019 0.288 10.83
Sepiolite 53.90 0.21 0.01 2.94 0.01 0.01 24.22 0.002 0.001 0.001 0.005 18.21
Tab.2  Analyzed weight percentage of the chemical compounds of the understudy minerals based on the XRF analysis.
Properties Bentonite Sepiolite
Liquid limit (%) 210 166
Plastic limit (%) 105 95
Plasticity index 105 71
Specific gravity 3.05 2.98
Optimum moisture (%) 36 39
Maximum dry density (g ?cm-3) 1.78 1.51
Permeability 1.3 × 10-8 8.1× 10-7
Cation exchangeable capacity (cmol ?kg1) 9 13
Specific surface area (m2g1) 220 180
Main mineral formulae (Al, Mg)2 Ca Na H2O12 (Si, Al)4 Mg8Si12O30(OH)4(H2O)4 .8H2O
Color Gray to light green Cream
Tab.3  Engineering properties of Bentonite and Sepiolite soils.
Fig.1  (a) Bentonite, (b) Sepiolite.
Fig.2  Details of Bentonite/Sepiolite plastic concrete mixes.
No. Gravel
(kg·m-3)
Sand
(kg·m-3)
Silty clay
(kg·m-3)
Cement
(kg·m-3)
Bentonite (kg·m-3) Water
(l·m-3)
Curing time(day) Compressive strength(Mpa)
Mix.1
1 775 775 0 252 28 190.40 7 10.79
2 775 775 0 224 56 212.80 7 9.81
3 775 775 0 196 84 219.67 7 7.85
4 775 775 0 168 112 252 7 5.79
5 775 775 0 140 140 281.04 7 4.02
6 775 775 0 112 168 302.40 7 2.75
7 775 775 0 252 28 190.40 28 12.26
8 775 775 0 224 56 212.80 28 10.69
9 775 775 0 196 84 219.67 28 9.42
10 775 775 0 168 112 252 28 8.14
11 775 775 0 140 140 281.04 28 5.59
12 775 775 0 112 168 302.40 28 3.53
13 775 775 0 252 28 190.40 90 20.99
14 775 775 0 224 56 212.80 90 19.12
15 775 775 0 196 84 219.67 90 13.73
16 775 775 0 168 112 252 90 9.41
17 775 775 0 140 140 281.04 90 7.36
18 775 775 0 112 168 302.40 90 5
Mix.2
19 295 1305 205 252 28 349.72 7 4.41
20 295 1305 205 224 56 361.63 7 3.24
21 295 1305 205 196 84 390.54 7 2.35
22 295 1305 205 168 112 410.78 7 1.57
23 295 1305 205 140 140 429.95 7 1.18
24 295 1305 205 112 168 481.47 7 0.98
25 295 1305 205 252 28 349.72 28 7.36
26 295 1305 205 224 56 361.63 28 5.49
27 295 1305 205 196 84 390.54 28 4.22
28 295 1305 205 168 112 410.78 28 2.84
29 295 1305 205 140 140 429.95 28 2.35
30 295 1305 205 112 168 481.47 28 2.06
31 295 1305 205 252 28 349.72 90 9.81
32 295 1305 205 224 56 361.63 90 7.46
33 295 1305 205 196 84 390.54 90 5.89
34 295 1305 205 168 112 410.78 90 4.41
35 295 1305 205 140 140 429.95 90 3.43
36 295 1305 205 112 168 481.47 90 2.75
Mix.3
37 310 1290 225 180 20 340.48 7 2.26
38 310 1290 225 160 40 356.79 7 1.86
39 310 1290 225 140 60 375.36 7 1.47
40 310 1290 225 120 80 395.78 7 1.08
41 310 1290 225 100 100 405.16 7 0.98
42 310 1290 225 80 120 413.17 7 0.98
*43 310 1290 225 180 20 340.48 28 3.14
44 310 1290 225 160 40 356.79 28 2.75
45 310 1290 225 140 60 375.36 28 2.55
46 310 1290 225 120 80 395.78 28 2.26
47 310 1290 225 100 100 405.16 28 2.16
48 310 1290 225 80 120 413.17 28 2.16
49 310 1290 225 180 20 340.48 90 4.81
50 310 1290 225 160 40 356.79 90 4.51
51 310 1290 225 140 60 375.36 90 3.83
52 310 1290 225 120 80 395.78 90 3.34
53 310 1290 225 100 100 405.16 90 2.65
54 310 1290 225 80 120 413.17 90 2.35
Mix.4
55 875 875 0 162 18 152.10 7 12.75
56 875 875 0 144 36 162 7 8.24
57 875 875 0 126 54 166.67 7 6.08
58 875 875 0 108 72 190.80 7 3.63
59 875 875 0 90 90 220.80 7 2.16
60 875 875 0 72 108 243 7 1.37
61 875 875 0 162 18 152.10 28 14.72
62 875 875 0 144 36 162 28 9.90
63 875 875 0 126 54 166.67 28 6.97
64 875 875 0 108 72 190.80 28 4.71
65 875 875 0 90 90 220.80 28 3.73
66 875 875 0 72 108 243 28 2.35
67 875 875 0 162 18 152.10 90 21.78
68 875 875 0 144 36 162 90 14.91
69 875 875 0 126 54 166.67 90 13.05
70 875 875 0 108 72 190.80 90 8.53
71 875 875 0 90 90 220.80 90 6.67
72 875 875 0 72 108 243 90 3.24
Tab.4  Compressive strength test results of bentonite plastic concrete.
No. Gravel
(kg·m-3)
Sand
(kg·m-3)
Silty clay
(kg·m-3)
Cement
(kg·m-3)
Sepiolite (kg·m-3) Water
(l·m-3)
Curing time(day) Compressive strength(Mpa)
Mix.1
1 775 775 0 252 28 194.7 7 9.81
2 775 775 0 224 56 238 7 7.75
3 775 775 0 196 84 249.56 7 4.91
4 775 775 0 168 112 252 7 3.24
5 775 775 0 140 140 310 7 1.96
6 775 775 0 112 168 336 7 0.88
7 775 775 0 252 28 194.70 28 10.79
8 775 775 0 224 56 238 28 8.83
9 775 775 0 196 84 249.56 28 6.38
10 775 775 0 168 112 252 28 4.51
11 775 775 0 140 140 310 28 2.84
12 775 775 0 112 168 336 28 1.08
13 775 775 0 252 28 194.70 90 19.62
14 775 775 0 224 56 238 90 14.12
15 775 775 0 196 84 249.56 90 10.99
16 775 775 0 168 112 252 90 8.63
17 775 775 0 140 140 310 90 5.40
18 775 775 0 112 168 336 90 2.06
Mix.2
19 295 1305 205 252 28 374.80 7 3.83
20 295 1305 205 224 56 415.52 7 1.96
21 295 1305 205 196 84 441.50 7 1.37
22 295 1305 205 168 112 502.37 7 0.88
23 295 1305 205 140 140 556.79 7 0.59
24 295 1305 205 112 168 573.06 7 0.39
25 295 1305 205 252 28 374.8 28 6.38
26 295 1305 205 224 56 415.52 28 3.43
27 295 1305 205 196 84 441.50 28 2.16
28 295 1305 205 168 112 502.37 28 1.28
29 295 1305 205 140 140 556.79 28 0.88
30 295 1305 205 112 168 573.06 28 0.59
31 295 1305 205 252 28 374.80 90 9.32
32 295 1305 205 224 56 415.52 90 5.10
33 295 1305 205 196 84 441.50 90 3.43
34 295 1305 205 168 112 502.37 90 1.86
35 295 1305 205 140 140 556.79 90 1.08
36 295 1305 205 112 168 573.06 90 0.78
Mix.3
37 310 1290 225 180 20 348.50 7 2.06
38 310 1290 225 160 40 367.61 7 1.47
39 310 1290 225 140 60 410.42 7 1.08
40 310 1290 225 120 80 433.50 7 0.69
41 310 1290 225 100 100 471.16 7 0.39
42 310 1290 225 80 120 520.93 7 0.29
*43 310 1290 225 180 20 348.50 28 2.55
44 310 1290 225 160 40 367.61 28 1.77
45 310 1290 225 140 60 410.42 28 1.47
46 310 1290 225 120 80 433.50 28 1.08
47 310 1290 225 100 100 471.16 28 0.59
48 310 1290 225 80 120 520.93 28 0.39
49 310 1290 225 180 20 348.50 90 3.92
50 310 1290 225 160 40 367.61 90 3.14
51 310 1290 225 140 60 410.42 90 2.45
52 310 1290 225 120 80 433.50 90 1.37
53 310 1290 225 100 100 471.16 90 0.981
54 310 1290 225 80 120 520.93 90 0.78
Mix.4
55 875 875 0 162 18 160.12 7 10.79
56 875 875 0 144 36 190.80 7 4.90
57 875 875 0 126 54 220.94 7 2.94
58 875 875 0 108 72 237.60 7 1.47
59 875 875 0 90 90 241.13 7 0.98
60 875 875 0 72 108 252 7 0.78
61 875 875 0 162 18 160.12 28 12.75
62 875 875 0 144 36 190.8 28 6.57
63 875 875 0 126 54 220.94 28 4.41
64 875 875 0 108 72 237.60 28 2.06
65 875 875 0 90 90 241.13 28 1.47
66 875 875 0 72 108 252 28 1.08
67 875 875 0 162 18 160.12 90 16.38
68 875 875 0 144 36 190.80 90 6.96
69 875 875 0 126 54 220.94 90 4.81
70 875 875 0 108 72 237.60 90 3.43
71 875 875 0 90 90 241.13 90 2.75
72 875 875 0 72 108 252 90 1.86
Tab.5  Compressive strength test results of sepiolite plastic concrete.
Fig.3  Summation and transfer function of a typical artificial neuron.
Fig.4  A feed-forward back-propagation network architecture with one hidden layer
Statistical parameter Gravel Sand Silty clay Cement Bentonite Water Curing time Compressive strength
Minimum 295 775 0 72 18 152.1 7 0.98
Maximum 875 1305 225 252 168 481.47 90 21.78
Mean 563.75 1061.25 107.5 152.75 82.25 304.35 41.67 5.98
Standard deviation 265.54 240.62 108.49 50.83 44.19 98.27 35.48 4.8
Median 542.5 1082.5 102.5 142 82 321.44 28 4.32
Tab.6  Prepared database of Bentonite plastic concrete.
Statistical parameter Gravel Sand Silty clay Cement Sepiolite Water Curing time Compressive strength
Minimum 295 775 0 72 18 160.12 7 0.29
Maximum 875 1305 225 252 168 573.06 90 19.62
Mean 563.75 1061.25 107.5 152.75 82.25 345.79 41.67 3.92
Standard deviation 265.54 240.62 108.49 50.83 44.19 122.25 35.48 4.09
Median 542.5 1082.5 102.5 142 82 342.25 28 2.11
Tab.7  Prepared database of Sepiolite plastic concrete.
Statistical parameter Gravel Sand Silty clay Cement Bentonit/Sepiolite Water Curing time Clay type Compressive strength
Training dataset (87 data)
Minimum 295 775 0 72 18 152.1 7 0 0.29
Maximum 875 1305 225 252 168 573.06 90 1 21.78
Mean 570.52 1051.90 103.10 154.64 84.67 325.96 40.94 - 5.25
Standard deviation 264.00 242.45 107.56 50.73 46.36 116.86 35.93 - 4.85
Median 775 875 0 144 80 336 28 1 3.43
Testing dataset (43 data)
Minimum 295 775 0 72 18 152.1 7 0 0.39
Maximum 875 1305 225 252 168 573.06 90 1 13.73
Mean 562.21 1069.19 111.39 146.42 79.63 319.11 40.86 - 4.34
Standard deviation 269.18 236.99 110.34 45.89 40.92 103.78 35.63 - 3.59
Median 310 1290 205 140 80 348.5 28 0 3.14
Validating dataset (14 data)
Minimum 295 775 0 72 20 190.4 7 0 0.39
Maximum 875 1305 225 252 140 520.93 90 1 20.99
Mean 526.43 1095 122.86 160.43 75.29 337.86 48.64 - 4.93
Standard deviation 270.54 245.09 110.69 64.57 39.81 117.00 32.46 - 5.46
Median 310 1290 205 164 84 345.1 28 0 2.65
Tab.8  Statistical parameters of training, testing and validating datasets.
Fig.5  Optimal architecture of ANN-B/S
Fig.6  ANN-B/S model performance for training (a), testing (b) and validating (c) datasets.
Fig.7  Comparison of experimental values with results of ANN-B/S model (training and testing)
Fig.8  SVM-B/S model performance for training (a) and testing (b) and validating (c) datasets.
Fig.9  Comparison of experimental values with results of SVM-B/S model (training and testing)
Model Architecture Training Testing Validating
Number of data R2 RMSE MAD MAPE Number of data R2 RMSE MAD MAPE Number of data R2 RMSE MAD MAPE
ANN-B 7-43-1 43 0.9931 0.4318 0.1912 3.0119 22 0.9906 0.4510 0.1700 2.9039 7 0.8940 0.4249 0.2643 6.4812
ANN-S 7-14-1 43 0.9963 0.2975 0.1584 3.5822 22 0.9836 0.3153 0.1826 5.6276 7 0.9822 0.2811 0.2057 6.9939
ANN-B/S 8-15-1 87 0.9968 0.2757 0.1557 2.9656 43 0.9886 0.3805 0.2332 5.3707 14 0.9989 0.1767 0.1321 2.6817
SVM-B C=39.9964, γ=0.0001, ϵ=0.0001 43 0.9917 0.4883 0.1452 2.2865 22 0.9672 0.8439 0.6156 10.5158 7 0.8069 0.4552 0.4033 9.8887
SVM-S C=16.4245, γ=0.00001, ϵ=0.00001 43 0.9478 1.3699 0.7300 16.5225 22 0.9550 0.6613 0.4288 13.2138 7 0.9860 1.3015 1.0116 34.3914
SVM-B/S C=29.6282, γ=1.9013, ϵ=0.0062 87 0.9885 0.5353 0.1559 2.9696 43 0.9648 0.6744 0.5619 12.9434 14 0.9885 0.7331 0.6057 12.2955
Tab.9  Statistical parameters of developed models for training, testing and validating datasets.
Equation Input No. Reference R2 RMSE MAD MAPE
CS=0.0981×52.083 ( WaterCement ) 1.4121 2 [17] 0.7358 4.8487 3.4970 58.5077
CS=0.0981×255.2 ((BentoniteCement)×100) 0.9243 2 [17] 0.2857 6.6438 5.0106 83.8309
CS=0.0981×(8.017+0.077×C ur in g time+2.563× ( BentoniteSand)) 3 [12] 0.1324 6.7423 4.8551 81.2312
CS=0.0981×306.7 e 1.35( WaterCement) 2 [18] 0.7502 3.9123 2.9186 48.8309
CS=0.0981×118.5 e 0.10( ( BentoniteCement)×100) 2 [18] 0.2343 6.6932 5.2135 87.2267
ANN-B 7 This paper 0.9918 0.4371 0.1918 3.2108
ANN-B/S 8 This paper 0.9960 0.3014 0.1850 3.0954
SVM-B 7 This paper 0.9839 0.6166 0.3140 5.2537
SVM-B/S 8 This paper 0.9820 0.6907 0.3691 6.1760
Tab.10  Statistical parameters values of built models in this study compared to empirical equations.
Fig.10  Influence of each input parameter on the compressive strength of bentonite (a) and sepiolite (b) plastic concretes.
Fig.11  Influence of measurement error of input parameters (change in input) on the output MAPE of bentonite and sepiolite plastic concretes.
Fig.12  Variations of compressive strength of bentonite plastic concrete with different sand and gravel amount versus silty clay.
Fig.13  Variations of compressive strength sepiolite plastic concrete with different sand and gravel amount versus silty clay.
Fig.14  Variations of compressive strength of plastic concrete with different cement amount versus bentonite.
Fig.15  Variations of compressive strength of plastic concrete with different cement amount versus sepiolite.
Fig.16  Variations of compressive strength of plastic concrete with different cement and bentonite amount versus water.
Fig.17  Variations of compressive strength of plastic concrete with different cement and sepiolite amount versus water.
Fig.18  Variations of compressive strength of plastic concrete with different cement and bentonite amount versus curing time.
Fig.19  Variations of compressive strength of plastic concrete with different cement and sepiolite amount versus curing time.
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