Prediction of Engineered Cementitious Composite Material Properties Using Artificial Neural Network

Document Type : Original Article

Authors

International Institute of Earthquake Engineering and Seismology, Tehran, Iran

Abstract

Cement-based composite materials like Engineered Cementitious Composites (ECCs) are applicable in the strengthening of structures because of the high tensile strength and strain. Proper mix proportion, which has the best mechanical properties, is so essential in ECC design material to use in structural components. In this paper, after finding the best mix proportion based on uniaxial tensile strength and strain, the correlation between these parameters were calculated. Since material properties depend on the content ratios, six mixtures with different Fly Ash (FA) content were considered to find the best ECC mixture called Improved ECC (IECC). Also, The influence of local fine aggregates and FA on the tensile behavior of ECC was considered to introduce IECC which has the best tensile properties. To predict the mechanical properties of ECC based on experimental results, Artificial Neural Network (ANN) was used. Training and validation of the proposed model were carried out based on 36 experimental results to find the best results. Numerical analysis is utilized to find the best mix proportion of ECC in structural design. The results show that the effects of FA and fine aggregates are considerable. Also, The proposed ANN model predicts the tensile strength and strain of ECC with different FA ratios accurately. Furthermore, the model can estimate mechanical properties of ECC in previous experimental results.

Keywords


1. Li, V.C., "From micromechanics to structural engineering-the
design of cementitous composites for civil engineering
applications", Journal of Structure Mechanical Earthquake
Engineering, Vol. 10, No. 2 (1993), 37-48. 
2. Weimann, M.B. and Li, V.C., "Hygral behavior of engineered
cementitious composites (ECC)", International Journal for
Restoration of Buildings and Monuments, Vol. 9, No. 5,
(2003): 513-534. https://doi.org/10.1515/rbm-2003-5791. 
3. Leung, C., "Theory of steady state and multiple cracking of
random discontinuous fiber reinforced brittle matrix
composites", ASCE Journal of Engineering. Mechanics,  Vol.
118, (1992), 2246-2264. 
4. Hung, C.-C. and Chen, Y.-S., "Innovative ecc jacketing for
retrofitting shear-deficient rc members", Construction and
Building Materials,  Vol. 111, (2016), 408-418. 
5. Dehghani, A., Fischer, G. and Alahi, F.N., "Strengthening
masonry infill panels using engineered cementitious
composites", Materials and Structures,  Vol. 48, No. 1-2,
(2015), 185-204. 
6. Dehghani, A., Nateghi-Alahi, F. and Fischer, G., "Engineered
cementitious composites for strengthening masonry infilled
reinforced concrete frames", Engineering Structures,  Vol. 105,
(2015), 197-208. 
7. De Koker, D. and Van Zijl, G., "Extrusion of engineered
cement-based composite material", Proceedings of BEFIB, 
Fibre-Reinforced Concretes; Proc. 6th RILEM Symposium on
FRC (BEFIB 2004), Varenna, Italy, (2004),1301- 1310.. 
8. Torigoe, S.-i., Horikoshi, T., Ogawa, A., Saito, T. and Hamada,
T., "Study on evaluation method for pva fiber distribution in
engineered cementitious composite", Journal of Advanced
Concrete Technology,  Vol. 1, No. 3, (2003), 265-268. 
9. Lepech, M.D. and Li, V.C., "Large-scale processing of
engineered cementitious composites", ACI Materials Journal, 
Vol. 105, No. 4, (2008), 358-366. 
10. FISCHER, G. and Shuxin, W., Design of engineered
cementitious composites (ECC) for processing and workability
requirements, in Brittle matrix composites 7. (2003), Elsevier,
29-36. 
11. Şahmaran, M. and Li, V.C., "Engineered cementitious
composites: Can composites be accepted as crack-free
concrete?", Transportation Research Record,  Vol. 2164, No. 1,
(2010), 1-8. 
12. Tosun-Felekoğlu, K., Gödek, E., Keskinateş, M. and Felekoğlu,
B., "Utilization and selection of proper fly ash in cost effective
green htpp-ecc design", Journal of Cleaner Production,  Vol.
149, (2017), 557-568. 
13. Zhu Y., Yingzi Y., Xiaojian G., Hongwei D., Yan Y.,
"Mechanical properties of engineered cementitious composites
with high volume fly ash", Journal of Wuhan University
Technology  Vol. 24, No. S1, (2009), 166-170. 
14. Li, W., Zhou, X. and Li, N., "Research on the effect of fly ash
content on the tensile properties of pva-ecc", in 2015 AsiaPacific
Energy Equipment Engineering Research Conference,
Atlantis Press., (2015). 
15. Nateghi-A, F., Ahmadi, M.H. and Dehghani, A., "Experimental
study on improved engineered cementitious composite using
local material", Materials Sciences and Applications,  Vol. 9,
No. 03, (2018), 315-329. 
16. Yang, E.-H., Yang, Y. and Li, V.C., "Use of high volumes of fly
ash to improve ecc mechanical properties and material
greenness", ACI Materials Journal,  Vol. 104, No. 6, (2007),
303-311. 
17. Zhu, Y., Yang, Y.Z. and Yao, Y., "Effect of high volumes of fly
ash on flowability and drying shrinkage of engineered
cementitious composites", in Materials Science Forum, Trans
Tech Publ. Vol. 675, (2011), 61-64. 
18. Bilim, C., Atiş, C.D., Tanyildizi, H. and Karahan, O.,
"Predicting the compressive strength of ground granulated blast
furnace slag concrete using artificial neural network", Advances
in Engineering Software,  Vol. 40, No. 5, (2009), 334-340. 
19. Demir, F., "Prediction of elastic modulus of normal and high
strength concrete by artificial neural networks", Construction
and Building Materials,  Vol. 22, No. 7, (2008), 1428-1435. 
20. Griinke, T.J., "Development of an artificial neural network (ann)
for predicting tribological properties of kenaf fibre reinforced
epoxy composites (KFRE)", University of Southern Queensland,
Australia  (2013). 
21. Lee, S.-C., "Prediction of concrete strength using artificial
neural networks", Engineering Structures,  Vol. 25, No. 7,
(2003), 849-857. 
22. Nehdi, M., El Chabib, H. and El Naggar, M.H., "Predicting
performance of self-compacting concrete mixtures using
artificial neural networks", Materials Journal,  Vol. 98, No. 5,
(2001), 394-401. 
23. Oreta, A.W. and Kawashima, K., "Neural network modeling of
confined compressive strength and strain of circular concrete
columns", Journal of Structural Engineering,  Vol. 129, No. 4,
(2003), 554-561. 
24. Shi, Z.-Q. and Chung, D., "Carbon fiber-reinforced concrete for
traffic monitoring and weighing in motion", Cement and
Concrete Research,  Vol. 29, No. 3, (1999), 435-439. 
25. Topcu, I.B. and Sarıdemir, M., "Prediction of properties of waste
aac aggregate concrete using artificial neural network",
Computational Materials Science,  Vol. 41, No. 1, (2007), 117125.
26. Wang, S.-C., Artificial neural network, in Interdisciplinary
computing in java programming. 2003, Springer, 81-100. 
27. Zhang, Z. and Friedrich, K., "Artificial neural networks applied
to polymer composites: A review", Composites Science and
Technology,  Vol. 63, No. 14, (2003), 2029-2044. 
28. Shi, L., Lin, S., Lu, Y., Ye, L. and Zhang, Y., "Artificial neural
network based mechanical and electrical property prediction of
engineered cementitious composites", Construction and
Building Materials,  Vol. 174, (2018), 667-674. 
29. Beale, M.H., Hagan, M.T. and Demuth, H.B., "Neural network
toolbox", User’s Guide, MathWorks,  Vol. 2, (2010), 77-81. 
30. Su, Z. and Ye, L., "Lamb wave propagation-based damage
identification for quasi-isotropic cf/ep composite laminates using
artificial neural algorithm: Part i-methodology and database
development", Journal of Intelligent Material Systems and
Structures,  Vol. 16, No. 2, (2005), 97-111.