تخمین مقاومت فشاری بتن ‏های با عملکرد بالا بر پایه خاکستر بادی با استفاده از شبکه ‏های عصبی مصنوعی

نوع مقاله : علمی - پژوهشی

نویسنده

استادیار، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه پیام نور، تهران، ایران

چکیده

خاکستر بادی از استخراج گازهای خروجی کوره‌های آتش با سوخت زغال و سیلت، غیر پلاستیک و ریز می‌باشد که ترکیبی متفاوت بر اساس سوخت زغال طبیعی است. خاکستر بادی جزو مصالح زائد در نیروگاه‌های حرارتی می‌باشد. استفاده از مواد زائد مانند خاکستر بادی در صنعت بتن راه حل جایگزین و ارزشمندی برای ایجاد یک محیط دوستدار محیط زیست ارائه می‏دهد. با این حال، کار آزمایشگاهی زمان بر و پرهزینه می باشد و استفاده از تکنیک های محاسبات نرم می تواند روند پیش بینی خواص مقاومتی بتن را تسریع بخشد. در این مطالعه با استفاده از شبکه عصبی مصنوعی (ANN) به پیش بینی مقاومت فشاری بتن های با عملکرد بالا بر پایه خاکستر بادی پرداخته شده است. تعداد 471 داده آزمایشگاهی از منابع مطالعاتی معتبر استخراج شد و پارامترهایی مانند مقدار سیمان، مقدار خاکستر بادی، مقدار آب، میزان فوق روان کننده، مقادیر ریز دانه و درشت دانه و سن نمونه آزمایشگاهی به عنوان پارامترهای ورودی و مقاومت فشاری نمونه بتنی به عنوان پارامتر خروجی در نظر گرفته شدند. از میان شبکه ها با تعداد نرون های مختلف، شبکه ای به عنوان شبکه بهینه انتخاب گردید که دارای بهترین مقادیر ضریب همبستگی حاصل از داده های آموزش، ارزیابی و آزمایش بوده و همچنین کمترین مقدار میانگین مربعات خطاها (MSE) را داشته باشد که در این مطالعه، شبکه با تعداد 6 عدد نورون بهترین نتایج پیش بینی را ارائه کرد . همچنین میزان تاثیر هر یک از پارامترها در بازه های عددی مختلف بر مقاومت فشاری بتن مورد بررسی قرار گرفته و ارائه گردید و مشخص شد که سن نمونه، مقدار سیمان، خاکستر بادی و آّّب دارای بیشترین اهمیت نسبی می باشند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

A New Estimation Approach for Fly Ash Incorporated High Strength Concrete Using Artificial Neural Network

نویسنده [English]

  • Ali Ghorbani
Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Payame Noor University, Tehran, Iran
چکیده [English]

The fly ash obtained from the extraction of exhaust gases from coal and silt furnaces, is non-plastic and fine, which is a different combination based on natural coal fuel. Fly ash is one of the waste materials in thermal power plants. The use of waste materials such as fly ash in the concrete industry offers an alternative and valuable solution to create an environmentally friendly environment. However, experimental work is time-consuming and expensive, and the use of soft computing techniques can speed up the process of predicting concrete's resistance properties. In this study, artificial neural network (ANN) was used to predict the compressive strength of fly ash-based high-performance concrete. A number of 471 experimental data were extracted from valid sources and parameters such as cement, fly ash, water, superplasticizer, fine and coarse aggregate and age of the samples were considered as input parameters and compressive strength of samples was considered as output parameters. Among the networks with different number of neurons, a network which has the best correlation coefficient values obtained from training, evaluation and testing data and also has the lowest mean square error (MSE) value was selected as the optimal network. In this study, the network with the number of 6 neurons provided the best results. Also, the effect of each parameter in different numerical ranges on the compressive strength of concrete was investigated and presented, and it was found that the age of the samples and the amount of cement, fly ash and water have the greatest relative importance.

کلیدواژه‌ها [English]

  • Compressive strength of concrete
  • Fly ash
  • Artificial neural network
  • Concrete
  • Matlab
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