واسنجی رفتار فشاری نمونه ستون‌های محصورشده با کامپوزیت‌های پایه‌معدنی به کمک رابطه‌های تجربی و روش‌های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشگاه بیرجند، بیرجند، ایران

2 گروه مهندسی عمران، دانشکده مهندسی، دانشگاه بیرجند، بیرجند، ایران

3 گروه مهندسی عمران، دانشکده مهندسی، گروه پژوهشی فناوری‌های نوین در مهندسی عمران، دانشگاه بیرجند، بیرجند، ایران

چکیده

کامپوزیت‌های پایه‌معدنی به‌علت وزن کم، مقاومت زیاد و سازگاری با محیط زیست، یکی از روش‌های نوین مقاوم‌سازی به‌شمار می‌رود. در این پژوهش، به ارزیابی رفتار محصورکنندگی این کامپوزیت‌ها پرداخته شده است. برای این منظور، با در نظر گرفتن عامل‌های ورودی اثرگذار شامل قطر نمونه ستون‌ها، تعداد لایه‌های محصورکننده، ضریب کشسانی، ضخامت و کرنش نهایی الیاف در کامپوزیت استفاده‌شده و مقاومت فشاری نمونه‌ستون در حالت محصورنشده، پایگاه داده‌ای دارای 92 نمونه‌ستون بتنی با مقطع دایره‌ای محصورشده با کامپوزیت‌های پایه‌معدنی از پژوهش‌های آزمایشگاهی پیشین گردآوری شد و دقت رابطه‌های موجود برای تخمین ‌مقاومت فشاری ستون‌های محصورشده ارزیابی شد. نتیجه‌ها نشان دادند که رابطه‌های موجود توانایی مناسبی برای تخمین مقاومت فشاری در ستون‌های محصورشده را ندارند. از این رو، با در نظر گرفتن 80% داده‌ها برای مرحله آموزش و 20% باقی‌مانده برای مرحله آزمون، شبکه‌ عصبی مصنوعی با تعداد بهینه 9 نورون در لایه‌ پنهان ایجاد شد که توانست با مقدارهای R به‌ترتیب برابر با 0.9952 و 0.999 و مقدارهای اندک خطای MSE به‌ترتیب برابر 0.000187 و 0.0000069 و خطای MAPE به‌ترتیب برابر1.6871% و 0.6795% در مرحله‌ آموزش و آزمون، رابطه‌ای دقیق برای برآورد ‌مقاومت فشاری ستون‌های محصورشده ارایه دهد. نتیجه‌ها نشان داد که از میان رابطه‌های موجود، رابطه‌ ارایه‌شده در پژوهشTriantafillou و همکاران کارایی بهتری دارد. نتیجه‌های انجام تحلیل حساسیت نیز بیان‌گر این بودند که مقاومت فشاری ستون در حالت محصورنشده با نسبت اهمیت 20.92% بیشترین اثر و تعداد لایه‌های محصورکننده با نسبت اهمیت 5.81% کمترین اثر را بر افزایش مقاومت فشاری ستون‌های محصورشده داشته‌اند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Predicting Compressive Behavior of Inorganic Matrix Composite Confined Columns Using Empirical and Machine Learning Methods

نویسندگان [English]

  • Atefeh Soleymani 1
  • Ali Mohammad Mohammadi 2
  • Hashem Jahangir 3
1 Department of Civil Engineering, University of Birjand, Birjand, Iran
2 Civil Engineering Department, University of Birjand, Birjand, Iran.
3 Department of Civil Engineering, Research Group of Novel Technologies in Civil Engineering, University of Bir-jand, Birjand, Iran
چکیده [English]

Inorganic matrix composites are considered an advanced method for structural retrofitting due to their low weight, high strength, rapid installation, and environmental compatibility. This study evaluates the confinement behavior of circular concrete columns wrapped with inorganic matrix composites. A database of 92 experimental specimens was compiled, considering key input parameters: column diameter, number of confinement layers, fiber elastic modulus, fiber thickness, ultimate strain, and unconfined compressive strength. Existing empirical models were found inadequate for accurately predicting strength enhancement. Consequently, an artificial neural network (ANN) with nine hidden neurons was developed, achieving high prediction accuracy (R=0.9952 and 0.9990; MSE=0.000187 and 0.0000069; MAPE=1.6871% and 0.6795% for training and testing, respectively). Among previous models, Triantafillou et al.’s formula showed the best performance. Sensitivity analysis quantitatively indicated that unconfined compressive strength (fco) has the greatest influence on strength enhancement (92.20%), while the number of confinement layers has the least influence (5.81%). The proposed ANN model provides a reliable tool for accurate prediction and practical design of inorganic matrix composites -confined concrete columns.

کلیدواژه‌ها [English]

  • Confined columns
  • Compressive behavior
  • Inorganic matrix compo-site
  • empirical models
  • Machine learning
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