ترکیب مدل‌های شبکه عصبی برای پیش‌بینی مقاومت چسبندگی میلگردهای پلیمری با الیاف شیشه به بتن

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار، دانشکده مهندسی علوم آب، گروه سازه های آبی، دانشگاه شهید چمران، اهواز، ایران.

2 دانشجوی دکتری، گروه عمران- مدیریت ساخت، دانشکده عمران، معماری و هنر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

چکیده

استفاده از مصالح FRP و سایر مصالح کامپوزیتی به عنوان میلگرد یا ورق، یکی از گزینه‌های مناسب فنی و اقتصادی در ساخت، بهسازی و مقاوم‌سازی سازه‌هایی نظیر سازه‌های بتنی است. یکی از مهمترین مسائلی که باید در مورد استفاده از چنین مصالحی مدنظر قرار گیرد، مقاومت چسبندگی آنها به بتن سازه‌ای است. در این مقاله، تأثیر ترکیب مدل‌های پیش‌بینی گروهی با مد‌‌ل‌های تخمین منفرد بر روی بهتر شدن نتایج مدل‌های منفرد برآورد مقاومت چسبندگی میلگردهای FRP با الیاف شیشه به بتن مورد بررسی قرار می‌گیرد. برای رسیدن به این هدف ابتدا از شبکه‌های عصبی با ورودی‌های نتایج پیش‌بینی دو مدل منفرد قبلأ ارائه شده برای برآورد مقاومت چسبندگی GFRP به منظور بهبود نتیجه بهترین مدل از میان دو مدل مذکور استفاده می‌شود. سپس با درنظرگیری خروجی-های پیش‌بینی مدل شبکه عصبی اول و بهترین مدل منفرد از بین دو مدل فوق‌الذکر به عنوان ورودی، دوباره از شبکه‌های عصبی برای ارائه یک مدل بهتر از مدل ANN اول استفاده می‌شود. نتایج انتهایی نشان از کاهش خطای پیش‌بینی مدل ANN ترکیب شده از روش-های منفرد و گروهی نسبت به مدل‌های منفرد قبلأ ارائه شده، مدل میانگین وزن‌دار نتایج خروجی پیش‌بینی شده‌ دو مدل منفرد مذکور و مدل ANN ترکیبی آن دو مدل منفرد می‌دهند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Combining Neural Network Models to Prediction the Bond Strength of Glass FRP to Concrete

نویسندگان [English]

  • Ahmad Fathi 1
  • Farshad Peyman 2
1 Assistant Professor, Water Sciences Engineering Faculty, Department of Hydraulic Structures, Shahid Chamran University, Ahvaz, Iran.
2 Ph.D. Student, Department of Civil Engineering – Construction Management and Engineering , Faculty of Civil , Architecture and Art, Science and Research Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

The use of FRP and other composite materials as bar or sheets is one of the most technically and economically viable options in the construction, refurbishment, and reinforcement of structures such as concrete structures. One of the most important issues to consider when using such materials is their bond strength to structural concrete. In this paper, the effect of combining ensemble prediction models with single estimation models on improving the results of single models is estimated to estimate the bond strength of GFRP bars to concrete. To this end, neural networks with predictive results inputs are first used to estimate the bond strength of GFRP to improve the best model result from the two previous models- Be. Then, by considering the prediction outputs of the first neural network model and the best single model above mentioned as input, the neural networks are again used to present a better model than the first ANN model. The final results show the reduction of the prediction error of the ANN model combined with single and ensemble methods compared to the single models previously presented, the weighted average output model of the two single models above and the ANN model. The combination of the two models usefulness a single.

کلیدواژه‌ها [English]

  • Bond strength of GFRP bar
  • Structural concrete
  • Artificial Neural Networks
  • Combination of ensemble and single models
  • MATLAB software
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