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بررسی خواص مهندسی بتن خود تراکم فوق توانمند الیافی و پیش‌بینی خواص رئولوژی آن با شبکه عصبی هیبریدی و RBF | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 55، شماره 5، مرداد 1402، صفحه 1103-1120 اصل مقاله (2.68 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2023.20292.7394 | ||
| نویسندگان | ||
| علیرضا رشنو1؛ محمدرضا عدل پرور* 1، 2؛ محسن ایزدی نیا1 | ||
| 1گروه مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران | ||
| 2گروه مهندسی عمران، دانشگاه قم، قم، ایران | ||
| چکیده | ||
| امروزه استفاده از بتنهای نوین در حال گسترش است، یکی از انواع این نوع بتن، بتن خود تراکم فوق توانمند الیافی است که شناخت خواص رئولوژی و مکانیکی آن از اهمیت بالایی برخوردار است. ساخت بتن و انجام آزمایش‌های مربوط به آن هزینه‌های مختص به خود را داراست، یکی از راه کارهای کاهش این هزینه‌ها استفاده از روش‌هایی است که بتواند خواص بتن را پیش‌بینی کند. در این تحقیق در قسمت اول از سنگدانه‌های گارنت و بازالت، میکرو سیلیس، خاکستر بادی، نانو سیلیس و الیاف فولادی جهت ساخت بتن خود تراکم فوق توانمند الیافی (UHPSCC) استفاده شده و خواص رئولوژی، مقاومت فشاری، کششی و ریزساختار آن بررسی شده است. جهت صرفه‌جویی در هزینه‌های ساخت و در قسمت دوم این تحقیق، پیش‌بینی و تخمین دو شبکه عصبی مصنوعی ANN-GA (ترکیب شبکه عصبی مصنوعی و الگوریتم ژنتیک) و RBF-NN (شبکه عصبی توابع بنیادی شعاعی) از خواص رئولوژی بتن خود تراکم فوق توانمند الیافی و مقایسه آن با نتایج آزمایشگاهی بررسی شده است. خواص رئولوژی بتن خود تراکم فوق توانمند الیافی که در این تحقیق مورد بررسی قرار گرفته شامل قطر جریان اسلامپ (D)، زمان جریان اسلامپ (T50)، آزمایش قیفV و آزمایش جعبه L است. نتایج آزمایشگاهی نشان‌دهنده مقاومت فشاری و کششی بالا و قرارگرفتن خواص رئولوژی در محدوده مورد پذیرش EFNARC است. تخمین و پیش‌بینی دو شبکه عصبی مورد بررسی از خواص رئولوژی این نوع بتن، نشان‌دهنده دقت قابل‌قبول پیش‌بینی هر دو شبکه عصبی دارد. در میان این دو شبکه عصبی مصنوعی، دقت پیش‌بینی ANN-GA بیشتر است. | ||
| کلیدواژه‌ها | ||
| بتن خود تراکم فوق توانمند الیافی؛ خواص رئولوژی؛ پیش‌بینی؛ ANN-GA؛ .RBF-NN | ||
| موضوعات | ||
| تکنولوژی بتن | ||
| عنوان مقاله [English] | ||
| 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 | ||
| نویسندگان [English] | ||
| Alireza Rashno1؛ Mohamadreza Adlparvar1، 2؛ Mohsen Izadinia1 | ||
| 1Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran | ||
| 2Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran | ||
| چکیده [English] | ||
| This study investigates the rheological and mechanical properties of ultra-high performance fiber reinforced self-compacting concrete (UHPSCC) using garnet and basalt aggregates, microsilica, fly ash, nanosilica, and steel fibers. To reduce construction costs, two artificial neural networks (ANN-GA and RBF-NN) are used to predict UHPSCC properties and compared with laboratory results. The studied rheological properties include slump flow diameter, slump flow time, V-funnel test, and L-box test. The laboratory results show high compressive and tensile strength, and acceptable rheological properties within EFNARC acceptance range. Both neural networks demonstrate acceptable accuracy in predicting rheological properties, with ANN-GA having higher prediction accuracy. Understanding UHPSCC properties is essential for the construction industry, and the use of ANN-GA can save on costs while maintaining accuracy in predicting its properties. | ||
| کلیدواژه‌ها [English] | ||
| Fiber-reinforced ultra-high performance self-compacting concrete, Rheology properties, Prediction, ANN-GA, RBF-NN | ||
| مراجع | ||
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