Prediction of compressive strength of concretes containing micro silica subject to carbonation using neural network

Subject Areas : Analytical and Numerical Methods in Mechanical Design

Ali Delnavaz ¹ , ali osat akbari moghaddam ² , Seyed Amir Hossein Hashemi ³ , Seiyed Hooman Ghasemi ⁴

1 -
2 -
3 - Assistance Professor, Department of Civil Engineering, Qazvin Branch, Islamic Azad University Qazvin, Iran
4 -

Received: 2023-01-10 Accepted : 2023-02-01 Published : 2023-03-10

Keywords: Compressive Strength, Concrete, Silica Fume, Carbonation, Artificial Neural Network,

Abstract :

Concrete materials are exposed to special weather conditions, corrosion and significant damage. For this purpose, the effect of 28-day compressive strength changes on the samples studied in this study was investigated by considering the simultaneous effect of chloride ion penetration and carbonation phenomenon. For this reason, in the first case, the samples are exposed to carbon dioxide once and then to chloride ions. In the latter case, only samples under the influence of chloride infiltration are examined. To make the samples, which include 9 mixing designs, three water-to-cement ratios of 0.35, 0.4 and 0.5 and three percent of 0%, 7% and 10% silica fume have been used. Finally, an optimal model is introduced to predict the compressive strength of concrete containing micro silica exposed to carbonation using artificial neural network. Also, a relation for estimating compressive strength based on the ratio of water to cement and the amount of silica is presented.

References:

[1] Jung, S., Y. Choi, and B. Lee. Influence of carbonation on the chloride diffusion in concrete. SB07 Seoul: Proceedings of the International Conference on Sustainable Building Asia, Seoul, Korea. 2007. https://doi.org/10.4334/JKCI.2003.15.6.829.
[2] Conciatori D, Laferrière F, Brühwiler E. Comprehensive modeling of chloride ion and water ingress into concrete considering thermal and carbonation state for real climate. Cement and Concrete Research. 2010 . 40(1). Pp 109-118. https://doi.org/10.1016/j.cemconres.2009.08.007.
[3] Rassokhin, A.S., Ponomarev, A.N., Figovsky, O.L. Silica fumes of different types for high-performance fine-grained concrete. Magazine of Civil Engineering. 2018. No. 78(2). Pp. 151–160. doi: 10.18720/MCE.78.12.
[4] Abdelgader, H.S., Fediuk, R.S., Kurpinska, M., Khatib, J., Murali, G., Baranov, A.V., Timokhin, R.A. Mechanical properties of two-stage concrete modified by silica fume. Magazine of Civil Engineering. 2019. 89(5). Pp. 26–38. DOI: 10.18720/MCE.89.3.
[5] Page, C.C., Vennesland, O. Effect of Carbonation on Chloride Binding, Materials and Constructions. 1983. 16(19).
[6] Suryavanshi, A. K., and R. Narayan Swamy. Stability of Friedel's salt in carbonated concrete structural elements. cement and Concrete Research. 26(5) 1996. Pp 729-741. https://doi.org/10.1016/S0008-8846(96)85010-1.
[7] Maekawa, K., & Ishida, T. Service Life Evaluation of Reinforced Concrete Under Coupled Forces and Environmental Actions. Proceedings of the JCI. 2002.20(2). Pp 691-696.
[8] Maekawa, K., & Ishida, T. Multi-Scale Modeling of Concrete Performance Integrated Material and Structural Mechanics. Journal of Advanced Concrete Technology. 2003. 1(2). Pp 91-126.https://doi.org/10.3151/jact.1.91.
[9] Ishida, T., Maekawa, K., & Soltani, M.,Theoretical Identified Strong Coupling of Carbonation Rate and Thermodynamic Moisture States in Microspores of Concrete. Journal of Advanced Concrete Technology.2004. 2(2). Pp 213-222. https://doi.org/10.3151/jact.2.213.
[10] Liang, M. T., & Lin, S. M. Modeling of Transport of Multiple Chemicals in Concrete Structures: Synergetic Effect Study. Cement and Concrete Research. 2003. 33(12). Pp 1917-1924. https://doi.org/10.1016/S0008-8846(03)00081-4.
[11] Isgor, O. B., & Razaqpur, A. G Advanced Modeling of Concrete Deterioration due to Reinforcement Corrosion. Canadian Journal of Civil Engineering.2006. 33(6). Pp 707-718. https://doi.org/10.1139/l06-007.
[12] Puatatsananon, W., and V. E. Saouma Nonlinear Coupling of Carbonation and Chloride Diffusion in Concrete. Journal of Materials in Civil Engineering. 2005. 17(3). Pp 264-275. https://doi.org/10.1061/(ASCE)0899-1561(2005)17:3(264).
[13] Song, H. W., Pack, S. W., Lee, C. H., & Kwon, S. J. service Life Prediction of Concrete Structures Under Marine Environment Considering Coupled Deterioration. Restoration of buildings and monuments= Bauinstandsetzen und Baudenkmalpflege. 2006. 12(4). Pp 265-284.
[14] Ameli, A. R., Parvaresh Karan, E., & Hashemi, S. A. H. Mechanical Properties and Performance of Roller Compacted Concrete (RCC) Containing High Volume of Fly Ash, Crumb Rubber and Nano Silica using Response Surface Method. Journal of Transportation Research. 2018.15(3). Pp 381-395.
[15] Yeh, I. C. Design of High-performance concrete mixture using neural networks and nonlinear programming. J. Comput. Civil Eng. 1999. 13(1). Pp 36-42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36).
[16] Yeh, I. C.Exploring concrete slump model using artificial neural networks. J Comput. Civil Eng. 2006. 20(3). Pp 217-221. https://doi.org/10.1061/(ASCE)0887-3801(2006)20:3(217).
[17] Sebastiá, M., Olmo, I. F., & Irabien, A. Neural network prediction of unconfined compressive strength of coal fly ash–cement mixtures. Cement and Concrete Research. 2003. 33(8). Pp 1137-1146. https://doi.org/10.1016/S0008-8846(03)00019-X.
[18] Topcu, I. B., & Sarıdemir, M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science. 2008. 41(3). Pp 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009.
[19] Kasperkiewicz, J., Racz, J., & Dubrawski, A. HPC strength prediction using artificial neural network. Journal of Computing in Civil Engineering. 1995. 9(4). Pp 279-284. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(279).
[20] Peng, J., Li, Z., & Ma, B. Neural network analysis of chloride diffusion in concrete. Journal of Materials in Civil Engineering. 2002. 14(4). Pp 327-333. https://doi.org/10.1061/(ASCE)0899-1561(2002)14:4(327).
[21] Ji, T., Lin, T., & Lin, X. A concrete mix proportion design algorithm based on artificial neural networks. Cement and Concrete Research. 2006. 36(7). Pp 1399-1408. https://doi.org/10.1016/j.cemconres.2006.01.009.
[22] Zarandi, M. F., Türksen, I. B., Sobhani, J., & Ramezanianpour, A. A. Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Applied Soft Computing. 2008. 8(1). Pp 488-498. https://doi.org/10.1016/j.asoc.2007.02.010.
[23] Abd Elaty, M. A. A. Compressive strength prediction of Portland cement concrete with age using a new model. HBRC journal. 2014. 10(2). Pp 145-155. https://doi.org/10.1016/j.hbrcj.2013.09.005.
[24] 24. - Neville, A.M., Brooks, J.J. Concrete Technology. The Second Edition. Pennsylvania: Trans-Atlantic Publications, 2010. 354p.
[25] Naderpour, H., Kheyroddin, A., & Amiri, G. G. Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures. 2010. 92(12). Pp 2817-2829. https://doi.org/10.1016/j.compstruct.2010.04.00