Browse > Article
http://dx.doi.org/10.12989/acc.2020.10.6.479

Estimating the compressive strength of HPFRC containing metallic fibers using statistical methods and ANNs  

Perumal, Ramadoss (Department of Civil Engineering, Pondicherry Engineering College)
Prabakaran, V. (Department of Civil Engineering, Pondicherry Engineering College)
Publication Information
Advances in concrete construction / v.10, no.6, 2020 , pp. 479-488 More about this Journal
Abstract
The experimental and numerical works were carried out on high performance fiber reinforced concrete (HPFRC) with w/cm ratios ranging from 0.25 to 0.40, fiber volume fraction (Vf)=0-1.5% and 10% silica fume replacement. Improvements in compressive and flexural strengths obtained for HPFRC are moderate and significant, respectively, Empirical equations developed for the compressive strength and flexural strength of HPFRC as a function of fiber volume fraction. A relation between flexural strength and compressive strength of HPFRC with R=0.78 was developed. Due to the complex mix proportions and non-linear relationship between the mix proportions and properties, models with reliable predictive capabilities are not developed and also research on HPFRC was empirical. In this paper due to the inadequacy of present method, a back propagation-neural network (BP-NN) was employed to estimate the 28-day compressive strength of HPFRC mixes. BP-NN model was built to implement the highly non-linear relationship between the mix proportions and their properties. This paper describes the data sets collected, training of ANNs and comparison of the experimental results obtained for various mixtures. On statistical analyses of collected data, a multiple linear regression (MLR) model with R2=0.78 was developed for the prediction of compressive strength of HPFRC mixes, and average absolute error (AAE) obtained is 6.5%. On validation of the data sets by NNs, the error range was within 2% of the actual values. ANN model has given the significant degree of accuracy and reliability compared to the MLR model. ANN approach can be effectively used to estimate the 28-day compressive strength of fibrous concrete mixes and is practical.
Keywords
high performance steel fiber reinforced concrete; silica fume; compressive strength; flexural strength; statistical model; neural networks; prediction; validation;
Citations & Related Records
Times Cited By KSCI : 21  (Citation Analysis)
연도 인용수 순위
1 Ramadoss, P. and Nagamani, K. (2008), "A new strength model for the high-performance fiber reinforced concrete", Comput. Concrete, 5(1), 21-36. https://doi.org/10.12989/cac.2008.5.1.021.   DOI
2 Ramadoss, P. and Nagamani, K. (2011), "Statistical methods of investigation on the compressive strength of high-performance steel fiber reinforced concrete", Comput. Concrerte, 9(2), 153-169. https://doi.org/10.12989/cac.2012.9.2.153.   DOI
3 Sardemir, M. (2016), "Empirical modeling of flexural and splitting tensile strengths of concrete containing fly ash by GEP", Comput. Concrete, 17(4), 489-498. https://doi.org/10.12989/cac.2016.17.4.489.   DOI
4 Shirkhani, A., Davarnia, D. and Farahmand Azar, B. (2019), "Prediction of bond strength between concrete and rebar under corrosion using ANN", Comput. Concrete, 23(4), 273-279. https://doi.org/10.12989/cac.2019.23.4.273   DOI
5 Wang, J.Z., Ni, H.G. and He, J.Y. (1999), "The application of automatic acquisition of knowledge to mix design of concrete", Cement Concrete Res., 29(7), 1875-1880. https://doi.org/10.1016/S0008-8846(99)00152-0.   DOI
6 Xu, B.W. and Shi, H.S. (2009), "Correlations among mechanical properties of steel fiber reinforced concrete", Constr. Build. Mater., 23(12), 3468-3474. https://doi.org/10.1016/j.conbuildmat.2009.08.017.   DOI
7 Yan, H., Sun, W. and Chen, H. (1999), "The effect of silica fume and steel fiber on the dynamic mechanical performance of highstrength concrete", Cement Concrete Res., 29(2), 423-426.   DOI
8 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
9 Chen, S.S. and Shah, K. (1992), "Neural networks dynamic analysis of bridges", ASCE, Proc. Int. Conf. Comput Civil Eng., 1010-1013.
10 Cheng Yeh, I.C. (1999), "Design of high-performance concrete mixtures using neural networks and nonlinear programming", ASCE, J. Comput. Civil Eng., 13(1), 36-42.   DOI
11 Gazder, U., Al-Amoudi, O.S.B., Khan, S.M.S. and Maslehuddin, M. (2017), "Predicting compressive strength of blended cement concrete with ANNs", Comput. Concrete, 20(6), 627-634. https://doi.org/10.12989/cac.2017.20.6.627.   DOI
12 Hola, J. and Schabowicz, K. (2005), "Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests", J. Civil Eng. Manage., 11(1), 23-33.   DOI
13 Gazder, U., Baghabara Al-Amoudi, O.S., Saad Khan, S.M. and Maslehuddin, M. (2017), "Predicting compressive strength of blended cement concrete with ANNs", Comput. Concrete, 20(6), 627-634. https://doi.org/10.12989/cac.2017.20.6.627.   DOI
14 Ghaboussi, J., Garrett, J.H. and Wu, X. (1991), "Knowledge based modeling of material behavior with neural networks", ASCE, J. Eng Mech., 17(1), 129-134. https://doi.org/10.1061/(ASCE)0733-9399(1991)117:1(132).   DOI
15 Hegazy, T., Moselhi, O. and Fazio, P. (1994), "Development of practical neural network applications using back propagation", Microcomput. Civil Eng., 9(2), 145-159. https://doi.org/10.1111/j.1467-8667.1994.tb00369.x.   DOI
16 Hsu, L.S. and Hsu, C.T. (1994), "Stress-strain behavior of steel fiber reinforced high- strength concrete under compression", ACI Struct. J., 91(4), 448-457.
17 Jain, J.C., Shih-Lin, H., Chi, S.Y. and Chem, C. (2002), "Neural network forecast model in deep excavation", ASCE, J. Compur. Civil Eng., 16(1), 59-65. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:1(59).   DOI
18 Kasperkiewicz, J., Racz. J. and Dubrawsk, A. (1995), "HPC strength prediction using Artificial Neural Network", ASCE, J. Comput. Civil Eng., 279-284. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(279).   DOI
19 Savino, V., Lanzoni, L., Taratino, A.M. and Vivani, M. (2017), "Simple and effective models to predict the compressive and tensile strength of HPFRC as steel fiber content and type changes", Compos. Part B, 137, 153-162. https://doi.org/10.1016/j.compositesb.2017.11.003.   DOI
20 Ji, T., Lin, T. and Lin, X (2006), "A concrete mix design algorithm based on artificial neural networks", Cement Concrete Res., 36, 1399-1408. https://doi.org/10.1016/j.cemconres.2006.01.009.   DOI
21 Khayat, K.H. and Ghezal, A. (1999), "Utility of statistical modeling in proportioning self-consolidation concrete", Proceedings RILEM Inter Symposium on Self-Compacting Concrete, Stockholm, 345-359.
22 Beale, M.H., Hagan, M.T. and Demuth, H.B. (2017), Neural Network Toolbox- User's Guide, MathWorks Inc., MA, Natick, USA.
23 Boukhatem, B., Kenai, S., Hamou, A.T. and Ghrici, M. (2012), "Predicting concrete properties using neural networks with principal component analysis technique", Comput. Concrete, 10(6), 25-32. http://dx.doi.org/10.12989/cac.2012.10.6.557.   DOI
24 Kim, J.I., Kim, D.K. and Yazdani, Fr. (2004), "Application of neural networks for estimation of concrete strength", ASCE, J. Mater. Civil Eng., 16(3), 257-264. https://doi.org/10.1061/(ASCE)0899-1561(2004)16:3(257).   DOI
25 Asteris, P.G., Armaghani, D.J., Hatzigeorgiou, G.D., Karayannis, C.G. and Pilakoutas, K. (2019), "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Comput. Concrete, 24(5), 469-488. https://doi.org/10.12989/cac.2019.24.5.469.   DOI
26 ACI 211.4R-93 (Reapproved 1998) (2006), Guide for Selecting Proportions for High Strength Concrete with Portland Cement and Fly Ash, ACI Manual of Concrete Practice (Part1).
27 ACI 544.3R-93 (Reapproved 1998) (2006), Guide for Specifying, Mixing, Placing and Finishing Steel Fiber Reinforced Concrete, ACI Manual of Concrete Practice.
28 Armelin, H.S. and Helene, P. (1995), "Physical and mechanical properties of SFR dry mix shortcrete", ACI Mater. J., 92(3), 258-267.
29 Ashteyat, A.M. and Ismeik, M. (2018), "Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks", Comput. Concrete, 21(1), 47-54. https://doi.org/10.12989/cac.2018.21.1.047.   DOI
30 Asteris, P.G., Apostolopoulou, M., Skentou, A.D. and Moropoulou, A. (2019), "Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars", Comput. Concrete, 24(4), 329-345. https://doi.org/10.12989/cac.2019.24.4.329.   DOI
31 ASTM C 39-1992 (2004), Standard Test Method for Compressive Strength of Fiber Reinforced Concrete, ASTM International, American Society for Testing and Materials.
32 Lin, W.T., Huang, R., Lee, C.L. and Hsu, H.M. (2008), "Effect of steel fibers on the mechanical properties of cement based composites containing silica fume", J. Marine Sci. Technol., 16(2), 214- 221.   DOI
33 ASTM C 78-1994 (2004), Standard Test Method for Flexural Strength of Concrete Specimens, ASTM International, American Society for Testing and Materials.
34 Ayat, H, Kellouche, Y., Ghrici, M. and Boukhatem, B. (2018), "Compressive strength prediction of limestone filler concrete using artificial neural networks", Adv. Comput. Des., 3(3), 289-302. https://doi.org/10.12989/acd.2018.3.3.289.   DOI
35 Lanzi, L., Bisagni, C. and Ricci, S. (2004), "Neural network systems to reproduce crash behavior of structural components", Comput. Struct., 82(1), 93-108. https://doi.org/10.1016/j.compstruc.2003.06.001.   DOI
36 Ni, H.G. and Wang, J.Z. (2000), "Predicting of compressive strength of concrete by artificial neutral networks", Cement Concrete Res., 30, 1245-1250.   DOI
37 Mansur, M.A., Chin, M.S. and Wee, Y.H. (1999), "Stress-strain relationship of high strength fiber concrete in compression", ASCE, J. Mater. Civil Eng., 13(1), 21-29. https://doi.org/10.1061/(ASCE)0899-1561(1999)11:1(21).   DOI
38 Marante, M.E., Barreto, W.J. and Picón, R.A. (2019), "Using a feed forward ANN to model the inelastic behaviour of confined sandwich panel", Struct. Eng. Mech., 71(5), 545-552. https://doi.org/10.12989/sem.2019.71.5.545.   DOI
39 Nehdi, M., El Chabib, H. and El Naggar, M.H. (2004), "Predicting performance of self-compacting concrete mixtures using artificial neutral networks", ACI Mater. J., 98(5), 394-401.
40 Nili, M. and Afroughsabet, V. (2010), "Combined effect of silica fume and steel fibers on the impact resistance and mechanical properties of concrete", Int. J. Impact Eng., 37, 879-886. https://doi.org/10.1016/j.ijimpeng.2010.03.004.   DOI
41 Perumal, R. (2015), "Correlation of compressive strength and other engineering properties of high-performance steel fiber reinforced concrete", ASCE, J. Mater Civil Eng., 27(1), 1-7. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001050.   DOI
42 Poon, C.S., Shui, Z.H. and Lam, L. (2004), "Compressive behavior of fiber reinforced high-performance concrete subjected to elevated temperature", Cement Concrete Res., 34(12), 2215-2222. https://doi.org/10.1016/j.cemconres.2004.02.011.   DOI
43 Ramadoss, P. and Nagamani, K. (2007), "Mechanical properties of steel fiber reinforced silica fume concrete", J. Civil Eng. Res. Pract., 4(1), 27-44. https://doi.org/10.4314/jcerp.v4i1.29165.   DOI