مدل‌سازی رفتار جانبی دیوارهای گهواره‌ای بتنی با استفاده از شبکه عصبی چند هدفه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه مازندران، بابلسر، ایران

2 انستیتوی مشترک دانشگاه میشیگان و دانشگاه شانگهای جیائوتونگ، دانشگاه شانگهای جیائوتونگ، شانگهای، چین

چکیده

دیوارهای گهواره‌ای بتنی به دلیل هزینه‌های خرابی و تعمیر کمتر، یک جایگزین مناسب برای دیوارهای برشی بتنی معمولی هستند. شناخت رفتار دقیق عناصر سازه‌ای به طور کلی با انجام آزمایشات جامع، که پرهزینه و زمان‌بر است، حاصل می‌شود. با توجه به مطالعات پیشین و روند پژوهش در زمینه دیوارهای گهواره‌ای، ضرورت ارائه یک مدل جامع به منظور طراحی دیده می‌شود. ارائه یک مدل تئوری جهت پیش‌بینی رفتار دیوارهای گهواره‌ای بتنی، که پارامترهای متنوعی را شامل شود، امری دشوار می‌باشد و محاسبه همزمان اثر آن پارامترها نیازمند یک مدل جامع است. ارائه چنین مدلی از طریق روش‌های کلاسیک یا عددی، به دلیل پیچیدگی مسئله یا دشواری‌های مدل‌سازی، نیازمند صرف زمان زیاد و محاسبات پیچیده است. اما حل این مسئله به وسیله روش‌های محاسبات نرم امکان ساده‌سازی و تسریع این محاسبات را فراهم می‌سازد. از این رو، این پژوهش با هدف ارائه یک مدل شبکه عصبی چند هدفه برای پیش‌بینی رفتار جانبی دیوارهای گهواره‌ای بتنی انجام شده است. برای ایجاد داده‌های مورد نیاز جهت مدل‌سازی، از نتایج آزمایشگاهی استفاده شد و تمام پارامترهای موثر بر ظرفیت باربری جانبی دیوارهای گهواره‌ای بتنی استخراج شد تا از آنها به عنوان پارامترهای ورودی استفاده شود. سرانجام، منحنی‌های بار جانبی-تغییر مکان و تار خنثی-تغییر مکان به عنوان پارامترهای خروجی پیش‌بینی شدند. علاوه بر این، دقت روابط موجود ارائه شده در آیین‌نامه‌های بتن بین‌المللی و توسعه یافته توسط محققان در پیش‌بینی مقاومت خمشی دیوارهای گهواره‌ای بتنی با استفاده از نتایج آزمایشات انجام شده بر روی این دیوارها بررسی شد. این پژوهش نشان می‌دهد که مدل شبکه عصبی چند هدفه با دقت قابل قبولی رفتار جانبی دیوارهای گهواره‌ای بتنی را پیش‌بینی می‌کند. این مدل قادر است سختی اولیه، سختی ثانویه، مقاومت بیشینه و ظرفیت دورانی را به درستی پیش‌بینی کند. مطابق با نتایج، روابط آیین‌نامه‌های طراحی بتن ایالات متحده (ACI 318-14)، نیوزیلند (NZS 3101) و استرالیا (AS 3600) مقاومت خمشی دیوارهای گهواره‌ای بتنی را در محدوده 0.59 تا 0.99 مقادیر واقعی پیش‌بینی می‌کنند. همچنین، آیین‌نامه طراحی بتن کانادا (CSA-A23.3) پیش‌بینی بسیار محافظه کارانه‌ای از مقاومت خمشی دیوارها ارائه می‌دهد. با وجود این، مدل شبکه عصبی چند هدفه پیش‌بینی‌های بسیار دقیقی را در مقایسه با آیین‌نامه‌های بررسی شده و عبارات موجود نشان داد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling the Lateral Behavior of Concrete Rocking Walls Using Multi-Objective Neural Network

نویسندگان [English]

  • Amir Ali Shahmansouri 1
  • Habib Akbarzadeh Bengar 1
  • Abouzar Jafari 2
1 Department of Civil Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
2 University of Michigan and Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China
چکیده [English]

Concrete rocking walls (CRWs) are an appropriate alternative for common concrete shear walls due to lower repair and downtime costs. Knowing the exact behavior of structural elements is generally achieved by conducting comprehensive experiments, which are costly and time-consuming. According to previous studies and research trends in the field of rocking walls, it is necessary to provide a comprehensive model for design purposes. It is difficult to provide a theoretical model for predicting the behavior of concrete rocking walls, which includes a variety of parameters, and simultaneous calculation of the effect of those parameters requires a comprehensive model. Providing such a model through classical or numerical methods, due to the complexity of the problem or modeling difficulties, requires much time and complex calculations. However, solving this problem by soft computing methods makes it possible to simplify and speed up these calculations. Therefore, this paper aims to develop a multi-objective neural network (MNN) model to predict the lateral behavior of CRWs. To generate the required data for modeling, experimental results were employed, and all the parameters affecting the lateral bearing capacity of CRWs were extracted to use them as input parameters. Finally, the lateral force-displacement and neutral axis-displacement curves were predicted as the output parameters. Besides, the accuracy of the existing equations presented in international concrete codes and developed by researchers in predicting the flexural strength of CRWs was investigated using the results of experiments performed on these walls. This study shows that the MNN model predicts the lateral behavior of CRWs with acceptable accuracy. This model is able to correctly predict initial stiffness, secondary stiffness, maximum strength, and rotational capacity. According to the results, the terms of the concrete design codes of the United States (ACI 318-14), New Zealand (NZS 3101), and Australia (AS 3600) predict the flexural strength of CRWs in the range of 0.59 to 0.99 actual values. Also, Canada's concrete design code (CSA-A23.3-04) provides a highly unconservative prediction of the walls' flexural strength. Nevertheless, the MNN model showed more accurate predictions in comparison with the investigated codes and existing expressions.

کلیدواژه‌ها [English]

  • Concrete Rocking Walls
  • Multi-Objective Neural Network
  • Post-Tensioning
  • Lateral Behavior Estimation
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