مقایسه عملکرد تخمینی محاسبات نرم در پیش‌بینی مقاومت فشاری بتن بازیافتی

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسنده

دکتری، گروه مهندسی عمران، دانشگاه سمنان، سمنان، ایران.

چکیده

از زباله‌های ساختمانی به‌عنوان یکی از مهم‌ترین نگرانی‌های زیست‌محیطی در جهان می‌توان نام برد. این نگرانی در ایران نیز مستثنا نبوده و سالانه بیش از 20 میلیون تن مواد زائد ساختمانی در تهران تولید می‌شود. برای برون‌رفت از این اتفاق، محققان بازیافت بتن و استفاده مجدد آن در مصارف ساختمانی و غیرساختمانی را پیشنهاد می‌دهند. این مقاله با هدف پیش‌بینی مقاومت فشاری بتن 28 روزه با سنگ‌دانه بازیافتی با استفاده از روش ماشین‌های برداری پشتیبان (SVM) و رگرسیون خطی چندگانه (MLR) انجام‌شده است. داده‌های آموزش و آزمایش برای توسعه مدل SVM با استفاده از 124 مجموعه داده موجود از 11 مرجع منتشرشده، تهیه ‌شده است. در فرایند مدل‌سازی، شبکه‌ای بهینه محسوب می‌شود که هم‌زمان با داشتن بالاترین ضریب همبستگی، کمترین میانگین مربعات خطا را نیز دارا باشد؛ از این رو ارزیابی کارایی مدل پیشنهادی، روش ماشین‌های برداری پشتیبان را با روش رگرسیون خطی چندگانه با استفاده از روش k-fold cross validation مقایسه شد. نتایج مقایسه دو ابزار پیش‌بینی نشان داد که ماشین‌های برداری پشتیبان از عملکرد مطلوب‌تری به نسبت روش رگرسیون خطی چندگانه برخوردار است. به همین دلیل می‌توان از روش ماشین‌های برداری پشتیبان به‌عنوان یک روش مؤثر برای پیش‌بینی مقاومت فشاری 28 روزه بتن بازیافتی نام برد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Comparison of Estimated Performance of Soft Computing in Predicting Compressive Strength of Recycled Concrete

نویسنده [English]

  • Seyed Reza Salim Bahrami
PhD, Department of Civil Engineering, Semnan University, Semnan, Iran.
چکیده [English]

Construction waste is one of the most important environmental concerns in the world. Iran is not an exception to this concern and more than 20 million tons of construction waste is produced annually in Tehran alone. To overcome this, researchers suggest recycling concrete and reusing it for construction and non-construction purposes. The aim of this paper was to predict the compressive strength of 28-day concrete with recycled aggregate using support vector machines (SVM) and multiple linear regression (MLR). Training and experimental data were developed for the development of the SVM model using 124 existing datasets from 11 published references. In the modeling process, an optimal network is considered to have the lowest mean square error and the highest correlation coefficient. Therefore, to evaluate the efficiency of the proposed model, the method of backup vector machines was compared with the method of multiple linear regression using the k-fold cross validation method. The results of comparing two 28-day compressive strength prediction tools including support vector machines and multiple linear regression using k-fold cross validation technique showed that support vector machines performed better compared to multiple linear regression method. Therefore, the support vector machine method can be mentioned as an effective method for predicting the 28-day compressive strength of recycled concrete.

کلیدواژه‌ها [English]

  • Support machining Recycled aggregate concrete K
  • fold cross validation Compressive strength Multiple linear regression

مراجع

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فصلنامه علمی کارافن
دوره 19، شماره 1 - شماره پیاپی 57
فنی و مهندسی
خرداد 1401
صفحه 13-34

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سابقه مقاله

  • تاریخ دریافت: 16 تیر 1400
  • تاریخ بازنگری: 06 مهر 1400
  • تاریخ پذیرش: 09 آذر 1400

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