پیوندهای مفید
آمار
| تعداد نشریات | 9 |
| تعداد شمارهها | 448 |
| تعداد مقالات | 5,737 |
| تعداد مشاهده مقاله | 8,058,854 |
| تعداد دریافت فایل اصل مقاله | 6,555,832 |
مقایسه آزمایش Break-off و مدول گسیختگی برای تعیین مقاومت بتن الیاف فولادی با استفاده از شبکه عصبی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 3، دوره 52، شماره 3، خرداد 1399، صفحه 565-580 اصل مقاله (1.79 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2019.14724.5727 | ||
| نویسندگان | ||
| سید حسین قاسم زاده موسوی نژاد* 1؛ بنیامین گنجه خسروی2؛ جواد رزاقی لنگرودی3 | ||
| 1استادیار دانشگاه گیلان | ||
| 2دانشجو دکتری سازه-دانشگاه گیلان | ||
| 3عضو هیئت علمی دانشگاه گیلان | ||
| چکیده | ||
| آزمایش مدول گسیختگی بتن جهت تعیین مقاومت در برابر شکست ناشی از خمش و محاسبه لنگر ترک خوردگی نقش مهمی دارد. به منظور تعیین این پارامتر طبق استانداردASTM - C78- نیاز به برش از سازه موجود یا ساخت نمونههای با مقطع مربعی 150 میلی متر و طول 700 میلی متر میباشد. از آنجائی که تهیه نمونه با ابعاد فوق در محل دشوار میباشد و عمال باعث تخریب سازه میشود، استفاده از آزمایش نیمه مخرب با خسارت جزئی بر عضو، مدنظر قرار گرفته است. در این مطالعه آزمایش نیمه مخرب Break- off برای ارزیابی مقاومت بتن حاوی الیاف فولادی در محل به عنوان جایگزین آزمایش مدول گسیختگی مدنظر قرار گرفته است. جهت فراهم کردن یک پایگاه آماری کامل و جامع، 24 طرح اختالط انتخاب گردید. سپس، عوامل تأثیرگذار بر خصوصیات بتن حاوی الیاف فولادی، نتایج آزمایش Break- off و مدول گسیختگی مورد ارزیابی قرار گرفت. نتایج نشان میدهد که مقادیر متوسط مقاومت مدول گسیختگی و Break- off با افزایش مقدار سیمان افزایش مییابد. همچنین این مقادیر برای سنگدانه بزرگتر، بیشتر میباشند. همچنین وجود الیاف فولادی مانع گسترش و توسعه ریزترکهای داخلی بتن شده و باعث افزایش مقاومت خمشی میشود که منجربه افزایش مقاومت Break- off و مدول گسیختگی میشود. همچنین، در این مقاله جهت پیشبینی مقاومت خمشی آزمایش مدول گسیختگی در سنین مختلف از مدلسازی عددی شبکه عصبی استفاده شده است. شاخصهای مختلف آماری برای مقایسه عملکرد مدل در نظر گرفته شده است. نتایج نشان میدهد که مدل شبکه عصبی ابزار قدرتمندی جهت پیش بینی مقاومت فشاری بتن میباشد. | ||
| کلیدواژهها | ||
| آزمایش break-off؛ مدول گسیختگی؛ آزمایش نیمه مخرب؛ الیاف فولادی؛ شبکه عصبی | ||
| موضوعات | ||
| تکنولوژی بتن؛ سازه بتنی | ||
| عنوان مقاله [English] | ||
| Comparison of Break-off and Flexural Strength Test Results for Determining Strength of SFRC Using Neural Network Model | ||
| نویسندگان [English] | ||
| hossein ghasem zadeh1؛ benyamin ganjeh khosravi2؛ Javad Razzaghi Langroudi3 | ||
| 1guilan university | ||
| 2PhD. Student, Civil Engineering, Faculty of Civil , Engineering, Guilan University | ||
| 3Assistant Professor, Faculty of Civil Engineering, Guilan University | ||
| چکیده [English] | ||
| Flexural strength test has a significant effect on the determination of failure strengths and cracking moment. According to ASTM-C78, the size and shape of used specimens were cubes by size (700*150*150 mm). Here, the efficiency of the non-destructive Break-Off (BO) and flexural strength tests was investigated for assessing the in-place compressive strength of steel fiber reinforced concrete (SFRC). In order to provide a thorough and comprehensive database, 24 mixtures were designed with various cement content, maximum aggregate size, steel fibre volume fractions and the constant water/ cement ratio of 0.4 for all mixtures. Then, effective parameters of SFRC and Break-Off and flexural strength test results were evaluated. The studies showed that volumetric percentage and features of steel fibers had a significant influence on concrete properties as well as Break-Off and flexural strength test results. According to the experimental results it could be generally concluded that the influencing factors, namely, SFRC properties due to presence of steel fibers and non-destructive tests significantly affect the results as follows: Generally, for a constant W/C ratio, it can be concluded that raising the cement content increases the mean values of Break-Off strength and Flexural strength. It can be stated that increasing the size of the aggregate causes an increase in strength. Also, the steel fibers restrain the development of internal micro-cracks in the concrete and thus contribute to the increase in bending strength, which causes improving Break-Off and flexural strengths. In addition, the conventional numerical regression model was developed in this study. Statistical indices were used to compare the efficiency and accuracy of the model. The result of this study confirmed the accuracy of the artificial neural network models in the determination of the compressive strength of concrete. | ||
| کلیدواژهها [English] | ||
| Break-off test, Flexural strength test, Steel Fiber, Non-destructive, Neural network model | ||
| مراجع | ||
|
[1] M. Wilson, S. Kosmatka, Design and control of concrete mixtures, High-Performance Concrete, 15 (2011) 299. [2] J.H. Bungey, M.G. Grantham, Testing of concrete in structures, Crc Press, 2014. [3] A. Long, A.M. Murray, The” Pul l-Off” Partially Destructive Test for Concrete, Special Publication, 82 (1984) 327-350. [4] A.M. Neville, Properties of concrete, Longman London, 1995. [5] E. Dahl-Jorgensen, R. Johansen, General and specialized use of the break-off concrete strength testing method, Special Publication, 82 (1984) 293308. [6] M. Carlsson, I. Eeg, P. Jahren, Field experience in the use of the “break-off tester”, Special Publication, 82 (1984) 277-292. [7] M.G. Barker, J.A. Ramirez, Determination of concrete strengths with break-off tester, Materials Journal, 85(4) (1988) 221-228. [8] T. Naik, Z. Salameh, A. Hassaballah, Evaluation of InPlace Strength of Concrete By The Break-Off Method, in: Proceedings of the NDT&E for Manufacturing and Construction Conference, University of Illinois, Urbana-Champaign, IL, 1988. [9] Y. Lin, Y.-F. Lin, C. Hsiao, Evaluation of bond quality at the interface between steel bar and concrete using the small-dimension break-off test, Materials and Structures, 43(5) (2010) 583-595. [10] R. Madandoust, M. Kazemi, Numerical analysis of break-off test method on concrete, Construction and Building Materials, 151 (2017) 487-493. [11] D.A. Abrams, Flexural strength of plain concrete, Structural Materials Research Laboratory, 1922. [12] F. Legeron, P. Paultre, Prediction of modulus of rupture of concrete, Materials Journal, 97(2) (2000) 193-200. [13] Z.P. Bazant, D. Novak, Proposal for standard test of modulus of rupture of concrete with its size dependence, ACI Materials Journal, 98(1) (2001) 7987. [14] V.C. Li, Large volume, high‐performance applications of fibers in civil engineering, Journal of Applied Polymer Science, 83(3) (2002) 660-686. [15] A.C. Aydin, Self compactability of high volume hybrid fiber reinforced concrete, Construction and Building Materials, 21(6) (2007) 1149-1154. [16] Z. Xu, H. Hao, H. Li, Mesoscale modelling of fibre reinforced concrete material under compressive impact loading, Construction and Building Materials, .882-472 )2102( )1(62 [17] G. Khalaj, A. Nazari, Modeling split tensile strength of high strength self compacting concrete incorporating randomly oriented steel fibers and SiO2 nanoparticles, Composites Part B: Engineering, 43(4) (2012) 18871892. [18] B. Luccioni, G. Ruano, F. Isla, R. Zerbino, G. Giaccio, A simple approach to model SFRC, Construction and Building Materials, 37 (2012) 111-124. [19] Z. Xu, H. Hao, H. Li, Mesoscale modelling of dynamic tensile behaviour of fibre reinforced concrete with spiral fibres, Cement and Concrete Research, 42(11) (2012) 1475-1493. [20] B.S. Institution, Specification for aggregates from natural sources for concrete, British Standards Institution London, 1992. [21] C. ASTM, 1150: 1990. Standard Test Method for the Break-Off Number of Concrete, Annual Book of ASTM Standards, 4. [22] ASTM, C78 / C78M-16 : Standard Test Method for Flexural Strength of Concrete (Using Simple Beam with Third-Point Loading), in, ASTM International, 2016. [23] B.B. Adhikary, H. Mutsuyoshi, Prediction of shear strength of steel fiber RC beams using neural networks, Construction and Building Materials, 20(9) (2006) 801-811. [24] A. Mukherjee, S.N. Biswas, Artificial neural networks in prediction of mechanical behavior of concrete at high temperature, Nuclear engineering and design, 178(1) (1997) 1-11. [25] R. Ince, Prediction of fracture parameters of concrete by artificial neural networks, Engineering Fracture Mechanics, 71(15) (2004) 2143-2159. [26] I. Nikbin, M. Beygi, M. Kazemi, J.V. Amiri, E. Rahmani, S. Rabbanifar, M. Eslami, A comprehensive investigation into the effect of aging and coarse aggregate size and volume on mechanical properties of self-compacting concrete, Materials & Design, 59 (2014) 199-210. [27] X.H. Vu, L. Daudeville, Y. Malecot, Effect of coarse aggregate size and cement paste volume on concrete behavior under high triaxial compression loading, Construction and Building Materials, 25(10) (2011) 3941-3949. [28] A. El-Dieb, M.R. Taha, Flow characteristics and acceptance criteria of fiber-reinforced self-compacted concrete (FR-SCC), Construction and Building Materials, 27(1) (2012) 585-596. [29] R. Madandoust, M.M. Ranjbar, R. Ghavidel, S. Fatemeh Shahabi, Assessment of factors influencing mechanical properties of steel fiber reinforced selfcompacting concrete, Materials & Design, 83 (2015) 284-294. [30] F. Aslani, S. Nejadi, Self-compacting concrete incorporating steel and polypropylene fibers: Compressive and tensile strengths, moduli of elasticity and rupture, compressive stress–strain curve, and energy dissipated under compression, Composites Part B: Engineering, 53 (2013) 121-133. [31] E. Güneyisi, M. Gesoğlu, A.O.M. Akoi, K. Mermerdaş, Combined effect of steel fiber and metakaolin incorporation on mechanical properties of concrete, Composites Part B: Engineering, 56 (2014) 83-91. [32] A. Al-Ameeri, The Effect of Steel Fiber on Some Mechanical Properties of Self Compacting Concrete, 2013. [33] R. Ghavidel, R. Madandoust, M.M. Ranjbar, Reliability of pull-off test for steel fiber reinforced selfcompacting concrete, Measurement, 73 (2015) 628.936 [34] L. Martinie, N. Roussel, Simple tools for fiber orientation prediction in industrial practice, Cement and Concrete Research, 41(10) (2011) 993-1000. [35] R. Zerbino, J.M. Tobes, M.E. Bossio, G. Giaccio, On the orientation of fibres in structural members fabricated with self compacting fibre reinforced concrete, Cement and Concrete Composites, 34(2) (2012) 191-200. [36] R. Madandoust, R. Ghavidel, N. Nariman-zadeh, Evolutionary design of generalized GMDH-type neural network for prediction of concrete compressive strength using UPV, Computational Materials Science, 49(3) (2010) 556-567. [37] R. Madandoust, 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, 51(1) (2012) 261-272.
| ||
|
آمار تعداد مشاهده مقاله: 1,157 تعداد دریافت فایل اصل مقاله: 1,402 |
||