IJE TRANSACTIONS C: Aspects Vol. 31, No. 6 (June 2018) 903-909   

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A. Heidari and M. Hashempour
( Received: November 05, 2017 – Accepted in Revised Form: February 07, 2018 )

Abstract    Acrylic polymer that is highly stable against chemicals and is a good choice when concrete is subject to chemical attack. In this study, self-compacting concrete (SCC) made using acrylic polymer, nanosilica and microsilica has been investigated. The results of experimental testing showed that the addition of microsilica and acrylic polymer decreased the tensile, compressive and bending strength of the concrete. The addition of nanosilica and an increase in polymer content increased the bending strength of concrete and decreased the tensile and compressive strengths. Because, in the laboratory, the number of samples were limited and the amount of variation was small, comprehensive results cannot be achieved. With the help of neural networks, estimating any amount within the range of the input data is possible. In this paper, in addition to the experimental results, a backpropagation neural network (BNN) was used to simulate the testing on the strength of self-compacting polymeric concrete. The results showed that the use of the normalized mean squared error, resilient backpropagation training, tangent-sigmoid and log sigmoid transfer functions and five neurons in each hidden layers in a two-layer BNN produced good results with a regression value of 0.95 and error of 0.17.


Keywords    Backpropagation Neural Network, Polymeric Concrete, Self-compacting Concrete, Acrylic Polymer


چکیده    پلیمر اکریلیک بهدلیل مقاومت بالا در برابر مواد شیمیایی گزینه مناسبی در ساخت بتنهای مقاوم در برابر حملات شیمیایی است. در این مطالعه به بررسی بتن پلیمری خودمتراکم ساخته شده با پلیمر اکریلیک، نانوسیلیس و میکروسیلیس پرداخته شدهاست. نتایج حاصل از آزمایش نمونهها نشان داده که افزودن میکروسیلیس و افزایش میزان پلیمر، مقاومت کششی، فشاری و خمشی را کاهش میدهد. افزودن نانوسیلیس مقاومت خمشی بتن را افزایش و مقاومت کششی و فشاری با افزایش پلیمر کاهش یافته است. این مقدار کاهش به نسبت سایر طرحها کمتر است. بهعلت آنکه در آزمایشگاه ساخت نمونهها محدود و میزان تغییرات مقادیر اندک است نمیتوان به نتیجه مناسبی دست یافت. به کمک شبکههای عصبی هر مقداری را که در محدوده دادههای ورودی باشد را میتوان تخمین زد. در این مقاله علاوه بر نتایج آزمایشگاهی، عملکرد شبکه عصبی انتشار برگشتی بر تخمین مقاومت بتن خودمتراکم پلیمری پرداخته شدهاست. نتایج شبکه عصبی نشان داده که یک شبکه عصبی انتشار برگشتی دو لایه که در آن از تابع خطای استاندارد میانگین، تابع آموزش انعطافپذیر، تابع تحریک لوگ سیگموئید و تانژانت سیگموئید، و 5 نرون در هر یک از لایههای پنهان استفاده شود، نتایج مناسبی حاصل شده که در آن رگرسیون 95/0 و مقدار خطا 17/0 است.


1.     Tavakoli, D., Hashempour, M. and Heidari, A., "Use of waste materials in concrete: A review", Pertanika Journal of Science & Technology,  Vol. 26, No. 2, (2018).

2.     Heidari, A., Hashempour, M., Javdanian, H. and Karimian, M., "Investigation of mechanical properties of mortar with mixed recycled aggregates", Asian Journal of Civil Engineering,  (2018), 1-11.

3.     Giustozzi, F., "Polymer-modified pervious concrete for durable and sustainable transportation infrastructures", Construction and Building Materials,  Vol. 111, (2016), 502-512.

4.     Hong, S., Kim, H. and Park, S.-K., "Optimal mix and freeze-thaw durability of polysulfide polymer concrete", Construction and Building Materials,  Vol. 127, (2016), 539-545.

5.     Shen, D., Wang, T., Chen, Y., Wang, M. and Jiang, G., "Effect of internal curing with super absorbent polymers on the relative humidity of early-age concrete", Construction and Building Materials,  Vol. 99, (2015), 246-253.

6.     Heidari, A. and Zabihi, M., "Self-compacting concrete incorporating micro-and acrylic polymer", Advances in Civil Engineering,  Vol. 2014, (2014).

7.     Bulut, H.A. and Şahin, R., "A study on mechanical properties of polymer concrete containing electronic plastic waste", Composite Structures,  Vol. 178, (2017), 50-62.

8.     Jafari, K. and Toufigh, V., "Experimental and analytical evaluation of rubberized polymer concrete", Construction and Building Materials,  Vol. 155, (2017), 495-510.

9.     Gill, A.S. and Siddique, R., "Strength and micro-structural properties of self-compacting concrete containing metakaolin and rice husk ash", Construction and Building Materials,  Vol. 157, (2017), 51-64.

10.   Heidari, A., Hashempour, M. and Tavakoli, D., "Using of backpropagation neural network in estimating of compressive strength of waste concrete", Soft Computing in Civil Engineering,  Vol. 1, No. 1, (2017), 54-64.

11.   Kalantari, Z. and Razzaghi, M., "Predicting the buckling capacity of steel cylindrical shells with rectangular stringers under axial loading by using artificial neural networks", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 8, (2015), 1154.

12.   Soleimanzadeh, S. and Mydin, M.O., "Influence of high temperatures on flexural strength of foamed concrete containing fly ash and polypropylene fiber", International Journal of Engineering-Transactions B: Applications,  Vol. 26, No. 2, (2012), 117-126.

13.   Shahba, S., Ghasemi, M. and Marandi, S., "Effects of partial substitution of styrene-butadiene-styrene with granulated blast-furnace slag on the strength properties of porous asphalt", International Journal of Engineering-Transactions A: Basics,  Vol. 30, No. 1, (2017), 40.

14.   Yaman, M.A., Elaty, M.A. and Taman, M., "Predicting the ingredients of self compacting concrete using artificial neural network", Alexandria Engineering Journal,  Vol. 56, No. 4, (2017), 523-532.

15.   Salajegheh, E. and Heidari, A., "Optimum design of structures against earthquake by adaptive genetic algorithm using wavelet networks", Structural and Multidisciplinary Optimization,  Vol. 28, No. 4, (2004), 277-285.

16.   Salajegheh, E. and Heidari, A., "Optimum design of structures against earthquake by wavelet neural network and filter banks", Earthquake Engineering & Structural Dynamics,  Vol. 34, No. 1, (2005), 67-82.

17.   Doğan, E., "Reference evapotranspiration estimation using adaptive neurofuzzy inference systems", Irrigation and Drainage,  Vol. 58, No. 5, (2009), 617-628.

18.   Heidari, A.a.H., M., "Seismic earth pressure on retaining wall by artificial neural network", Mathematics Applied in Science and Technology,  Vol. 4, (2012), 11-19.

19.   Heidari, A., Tavakoli, D. and Fakharian, P., "Approximate eigenvalue of plate by artificial neural networks", Journal of Modeling in Engineering Vol,  Vol. 11, No. 35, (2014).

20.   Kamalloo, A., Ganjkhanlou, Y., Aboutalebi, S.H. and Noranian, H., "Modeling of compressive strength of metakaolin based geopolymers by the use of artificial neural network research note", International Journal of Engineering-Transactions A: Basics,  Vol. 23, No. 2, (2010), 145.

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