بررسی آزمایشگاهی و تحلیل عددی تأثیر نانوذرات اکسید روی بر نفوذپذیری بتن در کانال‌های هیدرولیکی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده فنی و مهندسی عمران، دانشگاه تبریز، تبریز، ایران

چکیده

بتن به عنوان یکی از مواد ساختمانی پرکاربرد در صنعت ساخت و ساز، در تأمین امنیت سازه‌ها به خصوص کانال‌های هیدرولیکی از نظر مقاومت در برابر نفوذ آب و مواد شیمیایی بسیار حائز اهمیت است. بر این اساس، در تحقیق حاضر برای اولین تأثیر نانوذرات اکسید روی بر روی نفوذپذیری بتن و همچنین مشخصه‌های مکانیکی آن به صورت تجربی و آزمایشگاهی مورد بررسی قرار گرفته است. با انجام تست‌های فشاری تک‌جهته و آزمایش دو نیم شدن استوانه، مقاومت فشاری و کششی بتن حاوی مقادیر 0%، 0.1%، 0.5%، 1.0% و 1.5% در سنین 7 و 28 روزه تعیین شده است. همچنین، نفوذپذیری و میزان جذب آب این نمونه‌ها نیز بررسی شده است. با توجه به نتایج تست‌های تجربی، مقاومت‌‌های مکانیکی بتن با افزایش نانوذرات تا 1.0% افزایش و به ازای مقادیر بیشتر کاهش می‌یابد. همچنین، در بهترین حالت به ازای 1/0 درصد میزان نانوذرات اکسید روی، نفوذپذیری نسبت به نمونه شاهد 97 درصد کاهش یافته است. علت این امر آن است که نانومواد با ایجاد ساختاری متراکم‌‌تر و کم تخلخل در مخلوط ملات و بتن باعث بهبود مقاومت‌‌های مکانیکی می‌‌شود. در نهایت، مدل‌های رفتاری با استفاده از برنامه‌نویسی الگوریتم ژنتیک برای توصیف ویژگی‌های رفتاری وابسته به زمان نمونه‌های بتن مخلوط شده با نانوذرات در حالت‌های مختلف فشاری و کششی در سنین مختلف توسعه یافت. لذا در این تحقیق سعی شده است با استفاده از شبکه‌‌های عصبی همراه با روش الگوریتم ژنتیک به پیش‌بینی طرح اختلاط بتن حاوی نانو ذرات پرداخته شود. هدف از این مدل‌سازی ضمن نشان دادن دقت شبکه‌های عصبی در پیش‌بینی مقاومت فشاری، کششی و نفوذپذیری بتن با درصدهای مختلف نانوذرات اکسید روی است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Experimental investigation and numerical analysis of the effect of zinc oxide nanoparticles on the permeability of concrete in hydraulic channels

نویسندگان [English]

  • Kamran Rahmati Shadabad
  • Ali Foroughi-Asl
Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
چکیده [English]

Concrete is a fundamental building material widely employed in various construction projects, particularly in ensuring the structural integrity of hydraulic channels against water and chemical infiltration. In this study, we investigate, for the first time, the impact of zinc oxide nanoparticles on the permeability and mechanical properties of concrete through experimental and laboratory analyses. Uniaxial compressive tests were conducted to determine the compressive and tensile strength of concrete specimens containing zinc oxide nanoparticles at concentrations of 0%, 0.1%, 0.5%, 1.0%, and 1.5% at 7 and 28 days of age. Additionally, permeability and water absorption rates were assessed. The findings reveal that the mechanical strength of concrete increases with the addition of nanoparticles up to a certain threshold. Remarkably, at a nanoparticle concentration of 0.1%, the permeability of concrete decreased by 97% compared to the control sample. This enhancement can be attributed to the ability of nanomaterials to enhance mechanical strength by fostering a denser and less porous microstructure in the mortar-concrete matrix. Furthermore, behavioural models were developed utilizing genetic algorithm programming to depict the time-dependent properties of concrete specimens incorporating nanoparticles under various compressive and tensile conditions at different ages. Consequently, this study endeavours to predict the concrete mix design incorporating nanoparticles using neural networks in conjunction with the genetic algorithm approach. The aim of this modelling is to demonstrate the accuracy of neural networks in forecasting the compressive, tensile, and permeability properties of concrete with varying proportions of zinc oxide nanoparticles.

کلیدواژه‌ها [English]

  • Zinc Oxide Nanoparticles
  • Concrete Permeability
  • Genetic Algorithm
  • Compressive Strength
  • Neural Network

مراجع

[1] M. Parhizkari, A. Saberi Vaezaneh, M. Naderi, The effect of penetration-reducing materials on concrete permeability and strength with "cylindrical chamber" and "Twist-off" tests, Amirkabir Journal of Civil Engineering, 55(1) (2023) 19-40.
[2] J. Esmaeili, K. Andalibi, Investigation of the effects of nano-silica on the properties of concrete in comparison with micro-silica, International Journal of Nano Dimension, 3(4) (2013) 321-328.
[3] J. Esmaeili, V. Romouzi, J. Kasaei, K. Andalibi, An investigation of durability and the mechanical properties of ultra-high performance concrete (UHPC) modified with economical graphene oxide nano-sheets, Journal of Building Engineering, 80 (2023) 107908.
[4] M. Jafari Nadoushan, A. Ramezanianpor, Performance of alkali-activated slag and pumice mortars against chloride ions penetration in the Persian Gulf, Amirkabir Journal of Civil Engineering, 55(3) (2023) 531-554.
[5] S.H. Ghasemzadeh Mosavinejad, A. Darvishalinezhad, Durability of geopolymeric mortars based on recommended slag and kaolin percentages containing polymer, Amirkabir Journal of Civil Engineering, 55(11) (2024) 2243-2262.
[6] F.K. Maleki, M.K. Nasution, M.S. Gok, V.A. Maleki, An experimental investigation on mechanical properties of Fe2O3 microparticles reinforced polypropylene, journal of materials research and technology, 16 (2022) 229-237.
[6] M. Hoseinzadeh, R. Pilafkan, V.A. Maleki, Size-dependent linear and nonlinear vibration of functionally graded CNT reinforced imperfect microplates submerged in fluid medium, Ocean Engineering, 268 (2023) 113257.
[8] P. Vahidi Pashaki, M. Pouya, V.A. Maleki, High-speed cryogenic machining of the carbon nanotube reinforced nanocomposites: Finite element analysis and simulation, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 232(11) (2018) 1927-1936.
[9] M. Tabish, M.M. Zaheer, A. Baqi, Effect of nano-silica on mechanical, microstructural and durability properties of cement-based materials: A review, Journal of Building Engineering, 65 (2023) 10565767.
[10] K. Abu el‐Hassan, I.Y. Hakeem, M. Amin, B.A. Tayeh, A.M. Zeyad, I.S. Agwa, Y. Elsakhawy, Effects of nano titanium and nano silica on high‐strength concrete properties incorporating heavyweight aggregate, Structural Concrete 36 (2023) 14-26.
[11] F. Althoey, O. Zaid, R. Martínez-García, F. Alsharari, M. Ahmed, M.M. Arbili, Impact of Nano-silica on the hydration, strength, durability, and microstructural properties of concrete: A state-of-the-art review, Case Studies in Construction Materials,  (2023) e01997.
[12] A.S. Dahlan, Impact of nanotechnology on high performance cement and concrete, Journal of molecular structure, 1223 (2021) 128896.
[13] A. Jagadisha, K.B. Rao, G. Nayak, M. Kamath, Influence of nano-silica on the microstructural and mechanical properties of high-performance concrete of containing EAF aggregate and processed quarry dust, Construction and Building Materials, 304 (2021) 124392.
[14] P. Zhang, D. Sha, Q. Li, S. Zhao, Y. Ling, Effect of nano silica particles on impact resistance and durability of concrete containing coal fly ash, Nanomaterials, 11(5) (2021) 1296.
[15] P. Zhang, H. Zhang, G. Cui, X. Yue, J. Guo, D. Hui, Effect of steel fiber on impact resistance and durability of concrete containing nano-SiO2, Nanotechnology Reviews, 10(1) (2021) 504-517.
[16] C. Li, G. Li, D. Chen, K. Gao, Y. Mao, S. Fan, L. Tang, H. Jia, Influencing mechanism of nano-Al2O3 on concrete performance based on multi-scale experiments, Construction and Building Materials, 384 (2023) 131402.
[17] M. Naderi, A. Saberi Vaezaneh, m. parhizkari, The effect of different temperature cycles on permeability and surface resistance of concretes containing permeability-reducing materials, Amirkabir Journal of Civil Engineering, 55(9) (2023) 1845-1862.
[18] A. Tangtakabi, M.H. Ramesht, A. Golsoorat Pahlaviani, T. Pourrostam, Optimum Use of Microsilica in Reducing Corrosion Reinforcing Steel of Marine Concrete Structures, Amirkabir Journal of Civil Engineering, 54(8) (2022) 2953-2968.
[19] S. Khosravi, M.A. Goudarzi, Seismic risk assessment of on-ground concrete cylindrical water tanks, Innovative Infrastructure Solutions, 8(1) (2023) 68.
[20] Komasi, M., khosravi, S., chobkar, H., Laboratory study for optimal mixing scheme of pervious concrete containing additive of microsilica fume based on maximum compressive strength and permeability, Journal of Structural and Construction Engineering, 7(4) (2021) 42-61.
[21] M.R. Mohammadizadeh, F. Esfandnia, Prediction of shear strength of deep beams of the reinforced concrete using weighted least squares support vector machine method, Amirkabir Journal of Civil Engineering, 53(9) (2021) 3867-3882.
[22] A. Hakimi Khansar, J. Parsa, A. Hoseinzadeh dalir, J. Shiri, Simulation of soil stress in earth dams using artificial intelligence models and determination of effective features, Amirkabir Journal of Civil Engineering, 54(1) (2022) 247-262.
[23] F. Salmasi, F. Nahrain, A. Taheri aghdam, Prediction of discharge coefficients for broad-crested weirs using expert systems, Amirkabir Journal of Civil Engineering, 54(12) (2023) 4435-4458.
[24] M. Adamu, A.B. Çolak, Y.E. Ibrahim, S.I. Haruna, M.F. Hamza, Prediction of mechanical properties of rubberized concrete incorporating fly ash and nano silica by artificial neural network technique, Axioms, 12(1) (2023) 81.
[25] J. Abellán García, J. Fernandez Gomez, N. Torres Castellanos, Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks, European Journal of Environmental and Civil Engineering, 26(6) (2022) 2319-2343.
[26] Y. Zhang, Z. Gao, X. Wang, Q. Liu, Predicting the pore-pressure and temperature of fire-loaded concrete by a hybrid neural network, International Journal of Computational Methods, 19(08) (2022) 2142011.
[27] A.N. Beskopylny, S.A. Stel’makh, E.M. Shcherban’, L.R. Mailyan, B. Meskhi, I. Razveeva, A. Chernil’nik, N. Beskopylny, Concrete strength prediction using machine learning methods CatBoost, k-Nearest Neighbors, Support Vector Regression, Applied Sciences, 12(21) (2022) 10864.
[28] M. miri, H. Beheshti nezhad, M. Jafari, Experimental Investigation on Mechanical Properties of Concrete containing Nano Wollastonite and Modeling with GMDH-type Neural Networks, Amirkabir Journal of Civil Engineering, 46(2) (2015) 143-156.
[29] N. Ukrainczyk, V. Ukrainczyk, A neural network method for analysing concrete durability, Magazine of Concrete Research, 60(7) (2008) 475-486.
[30] A. Rashno, M. Adlparvar, M. Izadinia, Investigating the engineering properties of fiber-reinforced ultra-high performance self-compacting concrete and predicting its rheological properties using a hybrid neural network and RBF, Amirkabir Journal of Civil Engineering, 55(5) (2023) 1103-1120.
[31] M. Nataraja, L. Das, Concrete mix proportioning as per IS 10262: 2009–Comparison with IS 10262: 1982 and ACI 211.1-91, The Indian Concrete Journal, 35 (2010) 64-70.
[32] B. En, 12390-3, Testing hardened concrete-Part 3: Compressive strength of test specimens, British Standards Institution, (2003).
[33] C. Astm, 496/C 496M, Standard Test Method for Splitting Tensile Strength of Cylindrical Concrete Specimens,” ASTM International, West Conshohocken, PA (2004).
[34] B. Standard, Testing concrete: Method for determination of water absorption, BSI 12, 1998.
[35] V. Rathi, C. Modhera, An overview on the influence of nano materials on properties of concrete, International Journal of Innovative Research in Science Engineering and Technology, 3(2) (2014) 36-59.
[36] J.A. Abdalla, B.S. Thomas, R.A. Hawileh, K.S.A. Kabeer, Influence of nanomaterials on the water absorption and chloride penetration of cement-based concrete, Materials Today: Proceedings, 65 (2022) 2066-2069.
[37] M. Ashok, A. Parande, P. Jayabalan, Strength and durability study on cement mortar containing nano materials, Advances in nano research, 5(2) (2017) 99.
[38] J.R. Koza, Genetic programming: on the programming of computers by means of natural selection, MIT press, 1992.
[39] P. Chopra, R.K. Sharma, M. Kumar, Prediction of compressive strength of concrete using artificial neural network and genetic programming, Advances in Materials Science and Engineering, 2016 (2016) 36-56.
[40] W.B. Langdon, R. Poli, Foundations of genetic programming, Springer Science & Business Media, 2013.
[41] H.A. Shah, M.L. Nehdi, M.I. Khan, U. Akmal, H. Alabduljabbar, A. Mohamed, M. Sheraz, Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm, Materials, 15(15) (2022) 5436.
[42] A.A. Shahmansouri, H.A. Bengar, S. Ghanbari, Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method, Journal of Building Engineering, 31 (2020) 101326.
 
نشریه مهندسی عمران امیرکبیر
دوره 56، شماره 7
1403
صفحه 803-826

فایل ها

هم رسانی

ارجاع به این مقاله

آمار