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
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.
T WKahl, J L Kauschinger, E B Perry. Repair, Evaluation, Maintenance, and Rehabilitation Research Program: Plastic Concrete Cutoff Walls for Earth Dams, DTIC Document, 1991.
2
LMHu, DY Gao, YZLi, SQSong. Analysis of the Influence of Long Curing Age on the Compressive Strength of Plastic Concrete。 Advanced Materials Research, Trans Tech Publ, 2012, pp. 200–203.
3
PZhang, Q Y Guan, Q F Li. Mechanical Properties of Plastic Concrete Containing Bentonite, Research Journal of Applied Sciences. Engineering and Technology, 2013, 5(4): 1317–1322
4
YYu, J Pu, KUgai. Study of mechanical properties of soil-cement mixture for a cut-off wall. Soil and Foundation, 1997, 37(4): 93–103 https://doi.org/10.3208/sandf.37.4_93
5
ICOLD. Filling materials for watertight cut off walls. Bulletin, International Committee of Large Dams, Paris, France, 1995.
6
S LGarvin, C S Hayles. The chemical compatibility of cement–bentonite cut-off wall material. Construction & Building Materials, 1999, 13(6): 329–341 https://doi.org/10.1016/S0950-0618(99)00024-0
7
DKoch. Bentonites as a basic material for technical base liners and site encapsulation cut-off walls. Applied Clay Science, 2002, 21(1-2): 1–11 https://doi.org/10.1016/S0169-1317(01)00087-4
8
A AAta, T N Salem, N M Elkhawas. Properties of soil–bentonite–cement bypass mixture for cutoff walls. Construction & Building Materials, 2015, 93: 950–956 https://doi.org/10.1016/j.conbuildmat.2015.05.064
9
J LGarcía-Siñeriz, M VVillar, MRey, B Palacios. Engineered barrier of bentonite pellets and compacted blocks: State after reaching saturation. Engineering Geology, 2015, 192: 33–45 https://doi.org/10.1016/j.enggeo.2015.04.002
E IStavridakis. Presentation and assessment of clay influence on engineering parameters of cement treated clayey mixtures. Electronic Journal of Geotechnical Engineering, 2005, paper NO.: 508
12
ATahershamsi, A Bakhtiary, NBinazadeh. Effects of clay mineral type and content on compressive strength of plastic concrete. Iranian Journal of Mining Engineering, 2009, 4(7): 35–42
13
ASinger, W Kirsten, CBühmann. Fibrous clay minerals in the soils of Namaqualand, South Africa: characteristics and formation. Geoderma, 1995, 66(1-2): 43–70 https://doi.org/10.1016/0016-7061(94)00052-C
14
HYalçin, Ö Bozkaya. Sepiolite-palygorskite from the Hekimhan region (Turkey). Clays and Clay Minerals, 1995, 43(6): 705–717 https://doi.org/10.1346/CCMN.1995.0430607
15
AAkbulut, S Kadir. The geology and origin of sepiolite, palygorskite and saponite in Neogene lacustrine sediments of the Serinhisar-Acipayam Basin, Denizli, SW Turkey. Clays and Clay Minerals, 2003, 51(3): 279–292 https://doi.org/10.1346/CCMN.2003.0510304
16
HAbbaslou, A R Ghanizadeh, A T Amlashi. The compatibility of bentonite/sepiolite plastic concrete cut-off wall material. Construction & Building Materials, 2016, 124: 1165–1173 https://doi.org/10.1016/j.conbuildmat.2016.08.116
17
APashazadeh, M Chekaniazar. Estimating an Appropriate Plastic Concrete Mixing Design for Cutoff Walls to Control Leakage under the Earth Dam. Journal of Basic and Applied Scientific Research, 2011, 1(9): 1295–1299
18
SBahrami, A A Dezfouli, M Oulapour. Selection of the most suitable plastic concrete mix design using a multi-criteria decision-making approach. First Iranian National Conference on Civil Engineering and Sustainable Development Mehrarvand institute of technology, IRAN, 2015
19
N NEldin, A B Senouci. Measurement and prediction of the strength of rubberized concrete. Cement and Concrete Composites, 1994, 16(4): 287–298 https://doi.org/10.1016/0958-9465(94)90041-8
20
M HFazel Zarandi, I BTürksen, JSobhani, A ARamezanianpour. Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Applied Soft Computing, 2008, 8(1): 488–498 https://doi.org/10.1016/j.asoc.2007.02.010
21
D KKim, J J Lee, J H Lee, S K Chang. Application of Probabilistic Neural Networks for Prediction of Concrete Strength. Journal of Materials in Civil Engineering, 2005, 17(3): 353–362 https://doi.org/10.1061/(ASCE)0899-1561(2005)17:3(353)
22
J JLee, D Kim, S KChang, C F MNocete. An improved application technique of the adaptive probabilistic neural network for predicting concrete strength. Computational Materials Science, 2009, 44(3): 988–998 https://doi.org/10.1016/j.commatsci.2008.07.012
23
RMadandoust, R Ghavidel, NNariman Zadeh. Evolutionary design of generalized GMDH-type neural network for prediction of concrete compressive strength using UPV. Computational Materials Science, 2010, 49(3): 556–567 https://doi.org/10.1016/j.commatsci.2010.05.050
24
RMadandoust, J H Bungey, R Ghavidel. Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models. Computational Materials Science, 2012, 51(1): 261–272 https://doi.org/10.1016/j.commatsci.2011.07.053
25
CBilim, C D Atiş, H Tanyildizi, OKarahan. Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Advances in Engineering Software, 2009, 40(5): 334–340 https://doi.org/10.1016/j.advengsoft.2008.05.005
26
PChopra, R K Sharma, M Kumar. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Advances in Materials Science and Engineering, 2016, (2): 1–10
27
A T ADantas, MBatista Leite, Kde Jesus Nagahama. Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Construction & Building Materials, 2013, 38: 717–722 https://doi.org/10.1016/j.conbuildmat.2012.09.026
28
Z HDuan, S C Kou, C S Poon. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction & Building Materials, 2013, 40: 1200–1206 https://doi.org/10.1016/j.conbuildmat.2012.04.063
29
HNaderpour, A Kheyroddin, G GAmiri. Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures, 2010, 92(12): 2817–2829 https://doi.org/10.1016/j.compstruct.2010.04.008
30
MSarıdemir. Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks. Advances in Engineering Software, 2009, 40(5): 350–355 https://doi.org/10.1016/j.advengsoft.2008.05.002
31
RSiddique, P Aggarwal, YAggarwal. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 2011, 42(10): 780–786 https://doi.org/10.1016/j.advengsoft.2011.05.016
32
MUysal, H Tanyildizi. Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network. Construction & Building Materials, 2011, 25(11): 4105–4111 https://doi.org/10.1016/j.conbuildmat.2010.11.108
33
S SGilan, A M Ali, A Ramezanianpour. Evolutionary Fuzzy Function with Support Vector Regression for the Prediction of Concrete Compressive Strength. Fifth UK Sim European Symposium on Computer Modeling and Simulation (EMS), 2011, 263–268.
34
M YCheng, J S Chou, A F Roy, Y W Wu. High-performance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model. Automation in Construction, 2012, 28: 106–115 https://doi.org/10.1016/j.autcon.2012.07.004
35
JSobhani, M Khanzadi, AMovahedian. Support vector machine for prediction of the compressive strength of no-slump concrete. Computers and Concrete, 2013, 11(4): 337–350 https://doi.org/10.12989/cac.2013.11.4.337
36
A MAbd, S M Abd. Modelling the strength of lightweight foamed concrete using support vector machine (SVM). Case Studies in Construction Materials, 2017, 6: 8–15 https://doi.org/10.1016/j.cscm.2016.11.002
37
R AMozumder, B Roy, A ILaskar. Support Vector Regression Approach to Predict the Strength of FRP Confined Concrete. Arabian Journal for Science and Engineering, 2017, 42(3): 1129–1146 https://doi.org/10.1007/s13369-016-2340-y
38
K MHamdia, T Lahmer, TNguyen-Thoi, TRabczuk. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313 https://doi.org/10.1016/j.commatsci.2015.02.045
39
S SHaykin. Neural networks: a comprehensive foundation. Tsinghua University Press, 2001
40
J AFreeman, D M Skapura. Neural Networks: Algorithms, Applications and Programming Techniques, Addison-Wesley Publishing Company, 1992
41
PWerbos. Beyond regression: new tools for prediction and analysis in the behavioral sciences(PHD Dissertation). Harvard University, Cambridge, 1974
42
D ERumelhart, G EHinton, R JWilliams. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533–536 https://doi.org/10.1038/323533a0
43
VVapnik, S E Golowich, A Smola. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 1997, 9: 281–287
44
V NVapnik, V Vapnik. Statistical learning theory, Wiley New York, 1998
45
KLevenberg. A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 1944, 2(2): 164–168 https://doi.org/10.1090/qam/10666
46
D WMarquardt. An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 1963, 11(2): 431–441 https://doi.org/10.1137/0111030
47
YYang, Q Zhang. A hierarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering, 1997, 30(4): 207–222 https://doi.org/10.1007/BF01045717
48
MNaderi. Effect of different constituent materials on the properties of plastic concrete. International Journal of Civil Engineering, 2005, 3(1): 10–19
49
Y PPisheh, S M M Hosseini. Stress-strain behavior of plastic concrete using monotonic triaxial compression tests. Journal of Central South University, 2012, 19(4): 1125–1131 https://doi.org/10.1007/s11771-012-1118-y
50
QYGuan, P Zhang. Effect of Clay Dosage on Mechanical Properties of Plastic Concrete. Advanced Materials Research, Trans Tech Publ, 2011, pp. 664–667
51
AMahboubi, M Anari. Effects of Mixing Proportions and Sample Age on Mechanical Properties of Plastic Concrete; An Experimental Study. 1st International Conference on Concrete Technology, Tabriz, Iran, 2009
52
N SApebo, A J Shiwua, A Agbo, JEzeokonkwo, PAdeke. Effect of Water-Cement Ratio on the Compressive Strength of gravel-crushed over burnt bricks concrete. Civ Environ Res, 2013, 3: 74–81
53
ANeville. Properties of Concrete. Fourth and Final Edition Standards, Pearson, Prentice Hall, ISBN 0-582-23070-5, OCLC, 1996
54
DYGao, SQ Song, LMHu, Relationships of strengths and dimensional effect of plastic concrete, Advanced Materials Research, Trans Tech Publ, 2011, pp. 1029–1037
55
LMHu, XL Lv, DYGao, KBYan, SQ Song. Analysis of the Influence of Clay Dosage and Curing Age on the Strength of Plastic Concrete. Advanced Materials Research, Trans Tech Publ, 2014, pp. 1433–1437.
56
NVu-Bac, T Lahmer, HKeitel, JZhao, X Zhuang, TRabczuk. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84 https://doi.org/10.1016/j.mechmat.2013.07.021
57
NVu-Bac, T Lahmer, YZhang, XZhuang, TRabczuk. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites. Part B, Engineering, 2014, 59: 80–95 https://doi.org/10.1016/j.compositesb.2013.11.014
58
NVu-Bac, R Rafiee, XZhuang, TLahmer, TRabczuk. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464 https://doi.org/10.1016/j.compositesb.2014.09.008
59
NVu-Bac, M Silani, TLahmer, XZhuang, TRabczuk. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535 https://doi.org/10.1016/j.commatsci.2014.04.066
60
NVu-Bac, T Lahmer, XZhuang, TNguyen-Thoi, TRabczuk. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31 https://doi.org/10.1016/j.advengsoft.2016.06.005
61
K MHamdia, M Silani, XZhuang, PHe, T Rabczuk. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, (3): 1–13
