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پیش بینی مقاومت برشی تیرهای عمیق بتن مسلح با استفاده از روش ماشین بردار پشتیبان حداقل مربعات وزن دار | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 14، دوره 53، شماره 9، آذر 1400، صفحه 3867-3882 اصل مقاله (2.13 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.18122.6775 | ||
| نویسندگان | ||
| محمدرضا محمدی زاده* 1؛ فرناز اسفندنیا2 | ||
| 1استادیار | ||
| 2گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه هرمزگان، بندرعباس، ایران | ||
| چکیده | ||
| مقاومت برشی تیرهای عمیق بتن مسلح (RC) وابسته به پارامترهای مکانیکی و هندسی تیر تغییر مینماید. برآورد دقیق مقاومت برشی در تیرهای عمیق بتن مسلح یکی از اصلیترین موضوعات در طراحی سازههای مهندسی است. با این حال، پیشبینی مقاومت برشی در این نوع تیرها از دقت بالایی برخوردار نیست. یکی از روشهای تخمین نسبتا دقیق مقاومت برشی استفاده از هوش مصنوعی میباشد. هوش مصنوعی دارای روشهای مختلفی است که یکی از این روشها استفاده از تکنیک هوش مصنوعی (AI) مبتنی بر روش ماشین بردار پشتیبان است. در این مطالعه برای پیشبینی ظرفیت برشی تیرهای عمیق بتن مسلح از روش ماشین بردار پشتیبان حداقل مربعات وزندار (WLS-SVM) که روشی نسبتا جدید و کارامد است، استفاده شده است. برای این منظور ابتدا یک بانک اطلاعاتی شامل نتایج آزمایشگاهی مربوط به تیرهای عمیق بتن مسلح جمعآوری شد. سپس پس از تعیین پارامترهای ورودی و خروجی با کمک فرآیند آموزشی در روش WLS-SVM و با استفاده از بخشی از دادههای جمعآوری شده، مدلی برای پیشبینی مقاومت برشی تیرهای عمیق بتن مسلح ایجاد شد. به منظور تعیین دقت روش WLS-SVM، نتایج به دست آمده با نتایج حاصل از سایر روشهای هوش مصنوعی و آییننامههای مختلف مورد ارزیابی و مقایسه قرار گرفت. بررسیهای آماری نشان داد که روش WLS-SVM دارای بهترین عملکرد از نظر پارامترهای ارزیابی آماری (0/9887 = R2 و 107/0=RMSE و 0/478 =MAE و 9/48%=MAPE ) نسبت به سایر روشها هستند. بنابراین این مطالعه نشان میدهد که میتوان از روش WLS-SVM به عنوان ابزاری کارآمد در طراحی تیرهای عمیق استفاده نمود. | ||
| کلیدواژهها | ||
| تیر عمیق بتن مسلح؛ مقاومت برشی؛ هوش مصنوعی؛ ماشین بردار پشتیبان حداقل مربعات وزندار | ||
| موضوعات | ||
| شبکه های عصبی | ||
| عنوان مقاله [English] | ||
| Prediction of shear strength of deep beams of the reinforced concrete using weighted least squares support vector machine method | ||
| نویسندگان [English] | ||
| Mohammad Reza Mohammadizadeh1؛ Farnaz Esfandnia2 | ||
| 1Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan | ||
| 2Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan, Bandar Abbas, Iran | ||
| چکیده [English] | ||
| The shear strength of deep reinforced concrete beams depends on the mechanical and geometrical properties of the beam. Accurate estimation of shear strength in deep reinforced concrete beams is one of the major issues in the design of engineering structures. However, some methods proposed to determine the shear strength in deep reinforced concrete beams do not have high accuracy. One method to accurately estimate shear strength is to use artificial intelligence (AI). Artificial intelligence has many different methods, one of which is the use of artificial intelligence-based on the support vector machine method. In this study, the weighted least squares support vector machine (WLS-SVM), which is a relatively new and efficient method for predicting the shear capacity of reinforced concrete beams, has been used. In this study, a database containing experimental results on deep reinforced concrete beams was first collected. Then, after determining the input and output parameters using a training process in WLS-SVM method and using a part of the collected data, a model was developed to predict the shear strength of deep reinforced concrete beams. In order to determine the accuracy of the WLS-SVM method, the results were compared with those obtained by other AI methods and different regulations. Statistical analysis showed that WLS-SVM has the best performance in terms of statistical evaluation parameters (R2 = 0.9887, RMSE = 0.107, MAE = 0.478 and MAPE = 9.48%) compared to the other method. | ||
| کلیدواژهها [English] | ||
| Deep beam of reinforced concrete, Shear strength, Artificial intelligence, Weighted least squares support vector machine | ||
| مراجع | ||
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