پژوهشنامه حمل و نقل

پژوهشنامه حمل و نقل

ساخت مدل تحلیلی به منظور پیش‌بینی زاویه فاز (δ) در آزمایش رئومتر برشی دینامیکی (DSR)

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانش آموخته کارشناسی ارشد، موسسه آموزش عالی اقبال لاهوری، مشهد، ایران
2 دانش آموخته کارشناسی ارشد، دانشگاه آزاد اسلامی، مشهد، ایران
چکیده
مشخصات سوپرپیو قیر به‌منظور بهبود عملکرد روسازی با کنترل مشکلات روسازی تحت طیف وسیعی از دماها و شرایط پیری طراحی شده است. رئومتر برشی دینامیکی (DSR) یکی از آزمایش‌های سوپرپیو است که برای تعیین خواص رئولوژیکی قیر استفاده می‌شود. هدف این مطالعه ایجاد مدل پیش‌بینی باقابلیت پیش‌بینی زاویه فاز (δ) به‌عنوان یک نتیجه اصلی از روش آزمایش DSR است. این به‌نوبه خود می‌تواند زمان به دست آوردن نتایج آزمایشگاهی و درنتیجه هزینه را کاهش دهد. برای این هدف یک روش یادگیری ماشین گروهی با رویکرد جنگل تصادفی استفاده شده است. بر این اساس، هفت متغیر مؤثر بر زاویه فاز قیر از نتایج 1225 نمونه از وب‌سایت LTPP جمع‌آوری شد. این عوامل عبارت‌اند از: دمای آزمایش، نوع پیری، درجه عملکرد پایین (PG-low)، درجه عملکرد بالا (PG-high)، نفوذ، ویسکوزیته سینماتیک و ویسکوزیته مطلق (دینامیک). روش پیشنهادی از طریق یک روش اعتبارسنجی متقاطع 10 برابری تأییدشده و بر اساس تجزیه‌وتحلیل انجام‌شده به‌دقت بیش از 90 درصد ازنظر ضریب تعیین می‌رسد. درنهایت، تأثیر برخی از عوامل کلیدی در رویکرد جنگل تصادفی نیز بررسی شد، به‌عنوان‌مثال، میزان تأثیر حساسیت پارامترهای ورودی زاویه فاز. همچنین بر اساس نتایج تحلیل حساسیت، اهمیت متغیرهای ورودی مختلف به دست آمد. بر اساس بررسی‌ انجام‌شده، دمای آزمایش و نوع پیرشدگی بیشترین تأثیر را بر زاویه فازی قیر دارند. می‌توان با افزایش تعداد و تنوع داده‎‌های آموزشی مدل‌ را جهت رسیدن به نتایج بهتر و پیش‌بینی سایر خواص عملکردی قیر استفاده کرد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Creating an Analytical Model to Predict the Phase Angle (Δ) in The Dynamic Shear Rheometer (DSR) Test

نویسندگان English

Hassan Hosseinzadeh 1
Sina Arman 2
Behnam Khayat 2
1 M.Sc., Grad., Eqbal Lahori Institute of Higher Education, Mashhad, Iran.
2 M.Sc., Grad.,, Islamic Azad University, Mashhad, Iran.
چکیده English

Superpave bitumen specifications are designed to improve pavement performance by controlling pavement problems under a wide range of temperatures and aging conditions. Dynamic shear rheometer (DSR) is one of the superpave tests used to determine the rheological properties of bitumen. The aim of this study is to create a prediction model with the ability to predict the phase angle (δ) as a main result of the DSR test method. This, in turn, can reduce the time to obtain laboratory results and, consequently, the cost. For this purpose, a ensemble machine learning method with a random forest approach has been used. Based on this, seven effective variables on bitumen phase angle were collected from the results of 1225 samples from the LTPP website. These factors are: test temperature, type of aging, low performance degree (PG-low), high performance degree (PG-high), penetration, kinematic viscosity and absolute viscosity (dynamic). The proposed method is confirmed through a 10-fold cross-validation method and based on the analysis, it reaches more than 90% accuracy in terms of coefficient of determination. Finally, the effect of some key factors in the random forest approach was also investigated, for example, the effect of the sensitivity of the phase angle input parameters. Also, based on the results of sensitivity analysis, the importance of different input variables was obtained. Based on the research, the test temperature and the type of aging have the greatest effect on the bitumen phase angle. By increasing the number and variety of training data, the model can be used to achieve better results and predict other performance properties of bitumen.

کلیدواژه‌ها English

LTPP
Dynamic Shear Rheometer
Phase Angle
Machine Learning
Random Forest
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