پیش‌بینی مقاومت خمشی تیرهای تقویت شده به روش NSM-FRP با استفاده از شبکه‌های عصبی مصنوعی

نوع مقاله: علمی - پژوهشی

نویسندگان

1 استادیار، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران

2 دانشیار، دانشکده مهندسی عمران، دانشگاه سمنان، سمنان، ایران

3 دانشجوی کارشناسی ارشد مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران

چکیده

یکی از متداول‌ترین روش‌های تقویت اعضای بتن مسلح استفاده از الیاف‌های پلیمری می‌باشد. روش کاشت آرماتورهای FRP در پوشش اعضای بتنی (NSM-FRP)، اخیراً توجه محققین بسیاری را به خود جلب کرده‌است. در این روش به­دلیل اتصال بیشتر آرماتورها با بتن، از ظرفیت مصالح الیاف‌های پلیمری استفاده کامل­تری می­گردد. این روش دارای مزایای قابل توجهی نسبت به سایر روش‌های مقاوم‌سازی می‌باشد، این در حالی‌ است که مطالعات عددی کم‌تری در این زمینه نسبت به تکنیک‌های قدیمی‌تر مانند اتصال سطحی ورق‌های FRP انجام شده‌است. شبکه‌های عصبی مصنوعی ابزاری مناسب و کارآمد برای بررسی و پیش‌بینی پاسخ یک سیستم بر پایه تعداد زیادی داده‌های آزمایشگاهی می‌باشد. اساس کار این شبکه‌ها مبتنی بر پروسه یادگیری به عنوان جایگزین مناسب روش رگرسیون‌های معمول در جهت به حداقل رساندن خطای محتمل مطرح می‌باشد. در این تحقیق نتایج آزمایشات انجام شده در زمینه‌ی تقویت خمشی تیرهای بتن مسلح با استفاده از سیستم NSM-FRP جمع‌آوری و پس از شناسایی پارامترهای موثر بر رفتار خمشی تیرهای تقویت شده به عنوان پارامترهای ورودی شبکه‌ عصبی مصنوعی، با انتخاب نسبت افزایش ممان خمشی تیر به عنوان تابع هدف مدل‌ شبکه عصبی ایجاد و به بررسی مقاومت خمشی تیرهای تقویت شده پرداخته شده است. درنهایت با توجه به بررسی‌های پارامتریک، رابطه‌ای به منظور پیش‌بینی مقاومت نهایی تیرهای تقویت شده ارائه شده‌است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

نویسندگان [English]

  • Seyed Rohollah Hosseini Vaez 1
  • Hosein Naderpour 2
  • Mohammad Barati 3
1 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran
2 Associate Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
3 MSc Student, Department of Civil Engineering, University of Qom, Qom, Iran
چکیده [English]

In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP) materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM) FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain) process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.

کلیدواژه‌ها [English]

  • strengthening
  • Fiber Reinforced Polymer
  • NSM-FRP
  • Flexural Strength
  • Artificial Neural Network

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