نشریه مهندسی عمران امیرکبیر

نشریه مهندسی عمران امیرکبیر

پیش‌بینی مقاومت فشاری بتن حاوی خاکستر بادی با استفاده از مدل‌های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران.
2 دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران
چکیده
خاکستر بادی در نتیجه فرایند احتراق ذغال‌سنگ در نیروگاه ‌های حرارتی به عنوان یکی از محصولات جانبی به دست می‌آید. خاکستربادی ذرات بسیار ریز و میکروسکوپی دارد که معمولاً از ترکیب مواد معدنی مانند سیلیسیم دی ‌اکسید، آلومینیوم اکسید و آهن اکسید تشکیل شده است. این ترکیبات باعث می‌شوند که از خاکستربادی در صنایع مختلف، به ویژه در صنعت بتن، استفاده شود. ازکاربرد های خاکستربادی میتوان به افزودنی به بتن، پرکننده در آسفالت، ساخت آجر و بلوک‌های بتنی و جذب آلاینده ‌ها اشاره نمود. خاکستربادی به عنوان یک ماده پوزولانی، به کاهش تولید کربن دی‌اکسید در فرآیند تولید سیمان کمک می‌کند. در این مطالعه ابتدا یک پایگاه داده ای جامع از مطالعات گذشته در خصوص بتن حاوی خاکستر بادی جمع آوری شد. این داده ها شامل ۵۹۹ نمونه از مطالعات آزمایشگاهی معتبر بود. مجموعه داده‌های جمع‌آوری ‌شده شامل متغیرهای ورودی مختلفی از جمله نسبت آب به سیمان، مقدار خاکستر بادی، میزان سیمان، مقدار درشت‌دانه‌ ها، مقدار ریزدانه‌ ها، میزان روان‌کننده و سن عمل‌آوری بتن می‌باشد. برای پیش‌بینی مقاومت فشاری بتن ‌ها، از الگوریتم‌ های مختلف یادگیری ماشین از جمله برنامه‌ریزی ژنتیک)1(GP، سیستم استنتاج منطق فازی- عصبی)2(ANFIS، پرسپترون چندلایه)3(MLP، شبکه عصبی شعاعی پایه)4(RBF، کریجینگ(Kriging) و شبکه‌های عصبی تک لایه)5(ELM استفاده شده است. همچنین، دقت هر مدل با استفاده از شاخص‌های آماری ارزیابی شده و بهترین مدل معرفی گردید. نتایج نشان می‌دهد که مدل‌های مختلف یادگیری ماشین عملکردهای متفاوتی در پیش‌بینی مقاومت فشاری دارند. به ویژه روش کریجینگ (Kriging)با ضریب همبستگی 96/0 به عنوان بهترین مدل انتخاب گردید.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Prediction of Compressive Strength of Fly Ash Concrete Using Machine Learning Models

نویسندگان English

Elahe Moazeni 1
Yaser Moodi 2
1 Department of Civil Engineering, Sirjan University Of Technology, Sirjan, Iran
2 Department of Civil Engineering, Sirjan University Of Technology, Sirjan, Iran
چکیده English

Fly ash is produced as a byproduct of the coal combustion process in thermal power plants. Fly ash consists of very fine and microscopic particles, typically composed of mineral compounds such as silicon dioxide, aluminum oxide, and iron oxide. These compounds make fly ash suitable for use in various industries, particularly in the construction industry. Applications of fly ash include additives in concrete, fillers in asphalt, production of bricks and concrete blocks, and pollutant absorption. As a pozzolanic material, fly ash helps reduce carbon dioxide emissions in the cement production process. In this study, a comprehensive database of previous studies on fly ash concrete was initially collected. This data included 599 samples from credible laboratory studies. The gathered dataset consisted of various input variables, including the water-to-cement ratio, amount of fly ash, cement content, coarse aggregate amount, fine aggregate amount, superplasticizer content, and curing age of the concrete. To predict the compressive strength of the concrete, various machine learning algorithms were utilized, including Genetic Programming (GP), Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), Radial Basis Function Neural Network (RBF), Kriging, and Extreme Learning Machine (ELM). Furthermore, the accuracy of each model was evaluated using statistical indices, and the best model was identified. The results show that different machine learning models exhibit varying performances in predicting compressive strength. In particular, the Kriging method, with a correlation coefficient of 0.96, was selected as the best model.

کلیدواژه‌ها English

Concrete
Fly Ash
Compressive Strength
Prediction Model
Machine Learning Techniques
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