مدل‌سازی مبتنی بر تجزیه‌وتحلیل ستون‌های دایره‌ای فولادی پرشده با بتن تحت‌فشار محوری توسط شبکۀ عصبی مصنوعی

نوع مقاله : پژوهشی

نویسندگان

• محمد حسین یعقوبی
• بهروز کشته گر
• حسین علی رهدار

دانشگاه زابل

10.22067/civil.v32i3.73399

چکیده

ستون‌های فولادی پرشده با بتن به‌واسطه عملکرد مناسب بتن و فولاد موجب بهبود مقاومت نهایی محوری ستون‌ها می‌گردد. عمدتاً، مدل‌های تجربی و آیین‌نامه‌ای قادر به محاسبۀ دقیق مقاومت نهایی محوری این مقاطع برای مصالح با مقاومت‌های بالا نیستند. در این تحقیق، بر اساس نتایج آزمایشگاهی، یک مدل تخمین مقاومت نهایی محوری بر اساس شبکه عصبی توسعه داده شده ‌است. نتایج حاصل از تخمین مدل شبکه عصبی بر اساس 1168 نمونۀ آزمایشگاهی، به کمک چندین آماره خطا با هشت مدل‌ تجربی و آیین‌نامه EC4 مقایسه گردیده است. مدل شبکۀ عصبی ارائه‌شده در مقایسه با مدل‌های موجود دقیق‌تر است و محدوده گسترده‌تری از مقاومت‌های مصالح و نسبت قطر به ضخامت و ارتفاع به قطر را پوشش می‌دهد.

کلیدواژه‌ها

• ستون فولادی پرشده با بتن
• مقاومت نهایی محوری
• شبکه‌ی عصبی مصنوعی
• مدل‌سازی

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عنوان مقاله [English]

Model-Based Analysis for Ultimate Axial Load of Circular CFST Columns Using Artificial Neural Network

نویسندگان [English]

• M.H Yaqoubi
• Behrooz Keshtegar
• H.A Rahdar

Univrsity of Zabol

چکیده [English]

Concrete-filled steel tube (CFST) columns are increasingly adopted in many modern structures due to the advantage composite action between steel tube and concrete core. The almost empirical models and design code relations can not provide the accurate predictions for the axial ultimate strength of these composite steel-concrete sections, especially high (ultra-high) strength concrete. In this paper, a novel predicted model of the ultimate axial strength was introduced based on Artificial Neural Network (ANN) using a large experimental data set more 1168 sample data on CFST. The predicted results of the developed ANN model were compared with eight empirical newest models and EC4 code relations using several error statistics. The ANN predictions are more accurate than the existing (code design) models and covers a wider range of material strengths and the ratio of diameter to thickness and height to diameter.
 

کلیدواژه‌ها [English]

• Concrete-filled steel tube
• Axial ultimate strength
• Artificial neural network
• Modeling

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