بررسی کاربرد شبکه عصبی در تخمین مقاومت فشاری ستون‌های بتنی با مقاطع دایره‌ای محصورشده با ورقه‌های FRP

نوع مقاله : علمی - پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشکده مهندسی شهید نیکبخت، دانشگاه سیستان و بلوچستان، زاهدان، ایران

2 گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه ولایت، ایرانشهر، ایران

3 گروه مهندسی عمران، دانشگاه سیستان و بلوچستان

4 گروه مهندسی عمران، دانشکده مهندسی، دانشگاه ولایت، ایرانشهر، ایران

5 دانشکده مهندسی عمران، دانشگاه سمنان

چکیده

بسیاری از ستون‌هایی که در گذشته ساخته شده‌اند به دلایل مختلفی نظیر خطا در حین ساخت، خطا در طراحی، تغییر کاربری ساختمان، تغییر در آیین‌نامه، به وجود آمدن شرایط تیر قوی-ستون ضعیف و همچنین آسیب‌دیدگی در برابر حوادث نیاز به ترمیم و تقویت دارند. محصور کردن ستون‌های بتن مسلح، یکی از رایج‌ترین روش‌های مقاوم‌سازی ستون‌ها است. آزمایش‌های متعددی بر روی ستون‌های بتنی محصورشده با FRP انجام شده است که نشان می‌دهد، استفاده از محصورکننده‌ی FRP باعث افزایش مقاومت فشاری و شکل‌پذیری ستون‌های بتنی می‌شود. در تحقیقات گذشته مدل‌هایی برای تعیین مقاومت فشاری این ستون‌ها ارائه شده است. در این مطالعه یک مجموعه‌ی گسترده‌ای‌ از اطلاعات آزمایشگاهی ستون‌های دایره‌ای محصورشده با انواع FRP جمع آوری شده است. سپس از دو مدل پیش‌بینی شبکه عصبی برای تعیین مقاومت فشاری ستون‌های دایره‌ای محصورشده با FRP استفاده شد. این روش‌ها شامل دو شبکه عصبی پس انتشار خطا (PB) و شبکه توابع شعاعی (RBF) می‌باشند. درنهایت، بر اساس چهار معیار سنجش خطا، شبکه‌های عصبی نسبت به مدل‌های تخمین مقاومت موجود ارزیابی شدند. نتایج نشان می‌دهد شبکه‌های عصبی مقاومت فشاری ستون‌های محصورشده با FRP را نسبت به مدل‌های تحلیلی موجود با دقت بیشتری تخمین می‌زند، به طوری که استفاده از شبکه پس انتشار خطا و توابع شعاعی به ترتیب باعث کاهش 33 و 91 درصدی خطای کلی نسبت به مدل‌های تحلیلی می‌شود. همچنین با توجه به نتایج عددی، از بین این دو مدل شبکه عصبی، مدل شبکه توابع شعاعی عملکرد بهتری نسبت به مدل پس انتشار خطا نشان دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Applying neural networks for estimating the compressive strength of confined circular concrete columns with FRP sheets

نویسندگان [English]

  • Yaser Moodi 1
  • Ehsan Eslami 2
  • Seyed Roohollah Mousavi 3
  • Babak Dizangian 4
  • Hamid Mirshekar 5
1 Dept. of Civil Engineering, Univ. of Sistan and Baluchestan, Zahedan, Iran
2 Dept. of Computer Engineering, Velayat University, Iranshahr, Iran
3 Assistant Professor, Dept. of Civil Engineering, Univ. of Sistan and Baluchestan
4 Dept. of Civil Engineering, Velayat University, Iranshahr, Iran
5 Dept. of Civil Engineering, Univ. of semnan
چکیده [English]

Most of existing reinforced concrete columns are in need of retrofitting and strengthening for various reasons, including errors during the construction phase, changing the type of applications in structures, changes in design codes, occurrence of strong beam-weak column mechanism and the damages due to natural disasters. One of the most common ways of strengthening the columns is the confinement of the reinforced concrete columns. So far, several experiments have been conducted on concrete columns confined with fibre-reinforced polymer sheets and the results show that the use of fibre-reinforced polymer sheets increases the compressive strength of the concrete columns effectively. Different models in order to determine the compressive strength of the fibre-reinforced-polymer-confined concrete columns are provided in the previous researches. In this study, a large set of experimental data regarding circular columns confined with different types of FRP has been collected. Two neural network prediction methods were also used for determining the compressive strength of the confined circular columns with FRP sheets. These methods are back propagation (BP) and radial basis functions (RBF). Finally, the defined neural networks were examined with the available estimation models based on four standard error testing criteria. Results show that the neural networks could estimate the compressive strength of the confined columns with FRP with more accuracy rather than the existing analytical models.

کلیدواژه‌ها [English]

  • Concrete Confinement
  • FRP
  • Compressive Strength
  • BP
  • RBF
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