تخمین مقاومت برشی تیرهای بتنی مسلح به آرماتور FRP با استفاده از مدل GMDH-GA

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار، دانشکده فنی مهندسی، دانشگاه آیت ا... بروجردی (ره)، بروجرد، ایران

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

3 دانشکده مهندسی عمران، دانشگاه سمنان، سمنان، ایران

10.22065/jsce.2021.284971.2445

چکیده

در سال‌های اخیر استفاده از میلگردهای کامپوزیتی در سازه های بتن آرمه به دلیل مقاومت بالا در برابر خوردگی، مقاومت کششی قابل توجه و خاصیت ضد مغناطیسی بسیار مورد توجه قرار گرفته است. با توجه به مدول الاستیسیته کمتر میلگردهای کامپوزیتی نسبت به میلگردهای فولادی، تیرهای بتنی مسلح به میلگردهای کامپوزیتی دارای مقاومت برشی نسبتا کمتری در مقایسه با میلگردهای فولادی هستند. از سوی دیگر، شکست برشی در تیرهای بتنی مسلح به میلگردهای کامپوزیتی عموما ترد بوده و نیاز به پیش بینی دقیقی از عملکرد این اعضا نیاز می باشد. از اینرو، در این مطالعه به پیش بینی مقاومت برشی تیرهای بتنی مسلح به میلگردهای کامپوزیتی با استفاده از ترکیب شبکه عصبی GMDH و الگوریتم ژنتیک بر اساس طیف گسترده ای از مطالعات آزمایشگاهی پرداخته شده است. پارامترهای درنظر گرفته شده در این مطالعه شامل: پهنای تیر، عمق موثر تیر، نسبت دهانه برش به عمق، مقاومت فشاری نمونه استوانه ای استاندارد بتن، درصد آرماتورهای طولی و مدول الاستیسته میلگردهای کامپوزیتی می باشد. برای کنترل دقت و درجه اطمینان مدل پیشنهادی، نتایج آن با نتایج آزمایشگاهی و روابط موجود مقایسه گردیده است. نتایج این مقایسه نشان داده است که مدل پیشنهادی ترکیب شبکه عصبی GMDH و الگوریتم ژنتیک نتایج دقیقتری در محاسبه مقاومت برشی تیرهای بتنی نسبت به سایر روابط موجود دارد. همچنین بر اساس رابطه پیشنهادی به آنالیز حساسیت پارامترهای ورودی نیز پرداخته شده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Shear Capacity Prediction of FRP Reinforced Concrete Beams Using Hybrid GMDH–GA

نویسندگان [English]

  • Masoud Ahmadi 1
  • Hosein Naderpour 2
  • Pouyan Fakharian 3
  • Danial Rezazadeh Eidgahee 3
1 Assistant Professor, Department of Civil Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran
2 Associate Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
3 Faculty of Civil Engineering, Semnan University, Semnan, Iran
چکیده [English]

In recent years, the use of composite rebars in reinforced concrete structures has received much attention due to its high corrosion resistance, significant tensile strength, and appropriate non-magnetization characteristics.  Due to the lower modulus of elasticity of composite rebars than steel rebars, concrete beams reinforced with composite rebars have relatively lower shear strength compared to beams reinforced with steel rebars. On the other hand, shear failure in concrete beams reinforced with composite rebars is generally brittle and requires accurate prediction of the behavior of these members. Therefore, in this study, the shear strength of concrete beams reinforced with composite rebars is predicted using a combination of GMDH type neural networks and genetic algorithms based on a wide range of experimental results. The key effective parameters that consider in this study are the width of the web, effective depth of the beam, shear span to depth ratio, concrete compressive strength, modulus of elasticity of FRP longitudinal bars, and longitudinal reinforcement ratio. The accuracy of the proposed method has been verified by comparing the model predictions with the collected experimental results and existing shear design equations. The results show that the proposed model has more accurate results in calculating the shear strength of concrete beams than other existing relationships. A sensitivity analysis is also performed to assess the effect of the input parameters on the shear strength of FRP-reinforced concrete beams.

کلیدواژه‌ها [English]

  • FRP bar
  • shear capacity
  • GMDH
  • Genetic Algorithm
  • empirical model
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