توسعه مدل پیش‌بینی عمق شیارشدگی مخلوط‌های آسفالتی گرم با استفاده از شبکه عصبی

نوع مقاله : مقاله پژوهشی

نویسندگان

• مهسا روحی فریمان ¹
• سید علی حسینی ²
• منصور فخری ³

¹ دانشکده عمران ، دانشگاه صنعتی شاهرود

² دانشکده عمران، دانشگاه صنعتی شاهرود

³ راه و ترابری، دانشکده عمران، دانشگاه صنعتی خواجه نصیر الدین طوسی

10.22067/jfcei.2022.74238.1104

چکیده

محققان و مهندسان دائماً در تلاش هستند تا عملکرد روسازی­های آسفالتی را بهبود بخشند. روسازی­ها، به عنوان سطوحی که اغلب توسط محورهای سنگین بارگیری می­شوند، باید مقاومت کافی در برابر خستگی ، ترک‌خوردگی و شیارشدگی داشته باشند. در این مقاله با استفاده از داده‌های به دست آمده از نتایج آزمایشگاهی مطالعه قبلی که مخلوط­های آسفالتی گرم(WMA) اصلاح شده با الیاف شیشه و 0، 20، 40 و 50 درصد آسفالت تراشیده شده بازیافتی (RAP) برای بررسی مقاومت مخلوط در برابر شیارشدگی ساخته شدند، پیش‌بینی عمق شیارشدگی مخلوط‌ها توسط شبکه­های عصبی مصنوعی چندلایه (MLP) و شعاعی پایه (RBF) انجام شد و نتایج با یکدیگر مقایسه شدند. مدل پیش­بینی عمق شیارشدگی و پیش‌تراکم با نتایج تجربی مطابقت خوبی نشان دادند. برای بررسی قدرت تعمیم شبکه عصبی با استفاده از داده­هایی که در طول مدل‌سازی به کار گرفته نشده بودند، شبکه عصبی چندلایه عملکرد بهتری نسبت به شبکه عصبی شعاعی پایه داشت.

کلیدواژه‌ها

• آسفالت بازیافتی
• پیش تراکم شیارشدگی
• شبکه عصبی مصنوعی چندلایه
• شبکه عصبی مصنوعی شعاعی پایه

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عنوان مقاله [English]

Developing a prediction model for rutting depth of warm mix asphalt mixture using neural network

نویسندگان [English]

• , Mahsa Rouhi fariman ¹
• Sayyed Ali Hosseini ²
• Mansour fakhri ³

¹ Faculty of Civil Engineering -Shahrood university of technology

² Faculty of Civil Engineering , Shahrood university of technology

³ Civil Engineering Faculty of K.N. Toosi University of Technology.

چکیده [English]

Researchers and engineers are constantly working to improve the performance of asphalt pavements. Pavements, as surfaces that are often loaded by heavy axles, must have sufficient resistance to fatigue, cracking and rutting. In this paper, using the data obtained from the laboratory results of the previous study that warm mix asphalt modified with glass fibers and 0, 20, 40 and 50% recycled asphalt pavement (RAP) were made to evaluate the resistance of the mixture against rutting, rutting depth of the mixtures was determined by multilayer perceptron neural network (MLP) and radial basis function neural network (RBF) and the results were compared with each other. The prediction model of post compaction and rutting depth showed good agreement with the experimental results. To evaluate the generalizability of the neural network using data that were not used during modeling, the multilayer perceptron neural network (MLP) performed better than the radial basis function neural network (RBF).

کلیدواژه‌ها [English]

• Recycle asphalt pavement
• Rutting
• Multilayer perceptron neural network
• Radial basis function neural network
• Post compaction

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doi: 10.5772/644.