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بررسی آزمایشگاهی و تحلیل عددی تأثیر نانوذرات اکسید روی بر نفوذپذیری بتن در کانال‌های هیدرولیکی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 56، شماره 7، 1403، صفحه 803-826 اصل مقاله (1.97 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2024.23012.8090 | ||
| نویسندگان | ||
| کامران رحمتی شادباد* ؛ علی فروغی اصل | ||
| 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 | ||
| نانوذرات اکسید روی؛ نفوذپذیری بتن؛ الگوریتم ژنتیک؛ مقاومت فشاری؛ شبکه عصبی | ||
| موضوعات | ||
| تکنولوژی بتن | ||
| 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 | ||
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آمار تعداد مشاهده مقاله: 388 تعداد دریافت فایل اصل مقاله: 540 |
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