بررسی مقاومت بتن حاوی میکرو سیلیس و خاکستر بادی با استفاده از شبکۀ عصبی مصنوعی

نوع مقاله : یادداشت پژوهشی

نویسندگان

1 گروه عمران، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی، واحد شبستر، شبستر، ایران

2 کارشناسی ارشد مهندسی زلزله، گروه مهندسی عمران، واحد شبستر، دانشگاه آزاد اسلامی، شبستر، ایران،

10.22124/jcr.2021.18016.1465

چکیده

امروزه روش‌های هوشمند و الهام گرفته از طبعیت در حل مسائل پیچیده طرفداران زیادی دارد. یکی از پرطرفدارترین و کاراترین این ساختارها، شبکه‌های عصبی مصنوعی هستند که قادرند یک رابطه کلی بین اطلاعات حجیم و پیچیده ناشی از آزمایشها و مثالهای تجربی به دست آورند. با گسترش روزافزون جمعیت و افزایش میزان ساخت و ساز و همچنین به دلیل محدود بودن منابع و مصالح مصرفی، تقاضا برای استفاده از مصالح جدید و مقاوم در برابر زلزله، در صنعت ساختمان افزایش پیدا کرده است. در این تحقیق، با در نظرگیری پارامترهای طرح اختلاط بتن به عنوان ورودی، از مدل‌سازی شبکۀ عصبی استاتیکی و سری زمانی برای پیش‌بینی مقاومت فشاری بتن استفاده خواهد شد. طرح‌های اختلاط با درصدهای مختلف خاکستر بادی و میکروسیلیس (%1، %5 ، %7 ، %10 ، % 12، %15، %18) و مخلوط میکروسیلیس و خاکستر بادی با درصدهای یکسان (%1 و %1 ، %3 و %3 ، %5 و %5 ، %7 و %7 ، %9 و %9 ، %10و %10 ) به عنوان درصدی از وزن سیمان، جهت بررسی عملکرد مدل‌های مورد استفاده، به کار گرفته شده است. بر اساس نتایج به‌دست آمده مدل‌های شبکۀ عصبی سری زمانی با 5 نورون عملکرد بسیار مناسبی برای پیش‌بینی مقاومت فشاری بتن با دقت و قابلیت اعتماد بیشتر، از خود نشان می‌دهد. همچنین جایگزینی میکروسیلیس به عنوان بخشی از سیمان در درصد‌های مختلف، عملکرد بهتری نسبت به خاکستر بادی و مخلوط این دو (سیلیس و خاکستر بادی) در افزایش مقاومت بتن نسبت به نمونۀ شاهد دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction Containing the Micro Silica and Fly ash on Concrete Strength Using Artificial Neural Network (ANN)

نویسندگان [English]

  • farhad pirmohammadi alishah 1
  • Ahmad Jahandideh Shendu 2
1 Department of Civil Engineering, Faculty of Engineering, Islamic Azad University, Shabestar Branch, Shabestar, Iran
2 Master of Earthquake Engineering,Department of Civil Engineering, Shabestar Branch, Islamic Azad University,Shabesta, Iran.
چکیده [English]

Nowadays, intelligent methods inspired from nature are implemented to resolve complex problems, there are very popular too. The most common one is artificial neural network; they are capable to collect huge amount of complex information through experiments and tests. With increasing population and a rise in construction and also due to limited resources and consumable materials, demand for hot rolled earthquake-resistant materials in the construction industry has increased. The purpose of this research, by considering concrete mix design parameters as input, the Static neural network and Time-series modeling to predict the compressive strength of concrete will be used. Mixing fly ash and silica fume various designs with different percentages (1%, 5%, 7%, 10%, 12%, 15%, 18%) and mixed with silica fume, fly ashes identical percentages (% 1% 1% 3 and 3%, 5% and 5%, 7% and 7%, 9% and 9%, 10% and 10%) as a percentage of the weight of cement, to evaluate the performance of the models in question were applied. It turned out that neural network models for predicting time series with 5 neurons performance concrete compressive strength is accurate and reliable.

کلیدواژه‌ها [English]

  • Compressive strength of concrete
  • Static neural networks
  • Time-series models
  • Micro silica
  • Fly ash

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