بررسی رفتار دیوارهای برشی بتنی L-شکل با در نظر گرفتن اثرات تأخیر برشی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشکده مهندسی عمران، دانشگاه صنعتی شریف، تهران، ایران

2 استاد، دانشکده مهندسی عمران، دانشگاه صنعتی شریف، تهران، ایران

چکیده

در این مطالعه، اثرات ناشی از پدیده تأخیر برشی در عملکرد دیوارهای برشی بتنی L-شکل تحت بارگذاری چرخه ای ارزیابی میشود. ابتدا با استفاده از برنامه FlashLab، 42 دیوار برشی با مقطع کوتاه، متوسط و لاغر با مقطع L-شکل و با بالهای نابرابر ساخته شد و سپس تحلیل عددی آن در چارچوب نرم افزار اجزای محدود آباکوس صورت گرفت.بر اساس نتایج مدلسازی عددی و با در نظر گرفتن اثرات ناشی از تأخیر برشی، توزیع کرنش و تغییرشکل محوری، به عنوان پارامترهای رفتاری دیوارهای برشی بتنی با مقطع L- شکل،ترسیم شدند و سپس با اتکا به روشهای رگرسیون تکاملی (EPR) و الگوریتم ژنتیک، روابطی کاربردی جهت تخمین کرنش و تغییرشکل محوری این دیوارها ارائه شد. سنجش دقت روابط پیشنهادی بر اساس ضریب تعیین مشخص و به‌طور میانگین برای کرنش و تغییرشکل محوری به ترتیب مقادیر 80 و 72 درصد حاصل شد که نشانگر عملکرد مطلوب روابط ارائه شده در تخمین پارامترهای پاسخ است.همچنین، مقادیر حاصل از روابط پیشنهادی با روابط مطرح شده در مطالعات پیشین برای دیوارهای غیرمستطیلی مقایسه و سپس توصیه هایی جهت برآورد توزیع کرنش و تغییرشکل محوری در دیوارهای برشی بتنی L-شکل ارائه شده است.همچنین، به منظور ارزیابی دقت روابط پیشنهادی، از شاخص میانگین خطای نسبی (MARE) استفاده شده است. به این ترتیب، با محاسبه نسبت MARE روابط پیشنهاد شده در این مطالعه به MARE توزیعهای کرنش و تغییرشکل مطالعات پیشین، مشاهده شد که در ارزیابی مقدار کرنش محوری، توزیع یکنواخت به همراه توزیع ارائه شده در این مطالعه بهترین دقت را داشته و به طور میانگین خطای آن‌ها از 65 درصد خطای سایر روابط کمتر است.همچنین، در پیش بینی مقدار تغییرشکل محوری ملاحظه شد که توزیع پیشنهادی این پژوهش به طور میانگین با خطایی کمتر از 54 درصد سایر روابط، بهترین دقت و عملکرد را در محاسبۀ این پارامتر پاسخ از دیوارهای بتنی با مقطع L-شکل دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigation of the shear-lag effects on the response of L-shaped RC shear walls

نویسندگان [English]

  • Mohammad Tabiee 1
  • Hatef Abdoos 1
  • Alireza Khaloo 2
1 Ph.D. Student, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
2 Professor, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
چکیده [English]

In this study, the shear-lag effects are evaluated on the performance of L-shaped reinforced concrete (RC) shear walls. At first, 42 L-shaped RC shear walls are simulated employing the FlashLab program, and then, the numerical analyses are performed due to the cyclic loading applied within the framework of ABAQUS software. Based on the results of the numerical studies and taking into account the shear-lag effects, axial strain as well as axial deformation distribution are obtained for L-shaped RC shear walls. Thereafter, on the strength of the evolutionary polynomial regression (EPR) and genetic algorithm, new expressions have been developed and introduced for L-shaped sections which allow quick estimation of the axial strain and deformation distributions. The reliability of the proposed expressions has then been examined based on the coefficient of determination, of which the average values for the axial strain and deformation are 80 and 72 percent, respectively. Also, the results of the proposed expression are compared with those of the previous studies for non-rectangular RC shear walls. Finally, on account of the Mean Absolute Relative Error (MARE) index attained for the proposed expressions to that of the previous studies available in the literature, the uniform distribution exhibits the best performance in the response prediction of L-shaped RC walls. Furthermore, calculation of the MARE values indicates that the established formulations can respectively predict the axial strain and deformation of L-shaped RC shear walls with average error of 65 and 54 percent of the MARE values of the available expressions addressed in the literature.

کلیدواژه‌ها [English]

  • L-shaped RC shear wall
  • Shear-Lag
  • Cyclic loading
  • Axial strain &
  • deformation
  • EPR Method
  • Genetic Algorithm
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