Modeling of Resilient Modulus of Asphalt Concrete Containing Reclaimed Asphalt Pavement Using Feed-Forward and Generalized Regression Neural Networks
Publish Year: 1397
نوع سند: مقاله ژورنالی
زبان: English
View: 64
This Paper With 16 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_CIVLJ-6-1_011
تاریخ نمایه سازی: 23 شهریور 1403
Abstract:
Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of different percentages of RAP on resilient modulus of hot-mix asphalt (HMA). In this research, ۲۱۶ resilient modulus tests were conducted for establishing the experimental dataset. Input variables for predicting resilient modulus were temperature, penetration grade of asphalt binder, loading frequency, change of asphalt binder content compared to optimum asphalt binder content and percentage of RAP. Results of modeling using feed-forward neural network (FFNN) and generalized regression neural network (GRNN) model were compared with the measured resilient modulus using two statistical indicators. Results showed that for FFNN model, the coefficient of determination between observed and predicted values of resilient modulus for training and testing sets were ۰.۹۹۳ and ۰.۹۸۱, respectively. These two values were ۰.۹۹۹ and ۰.۹۶۷ in case of GRNN. So, according to comparison of R۲ for testing set, the accuracy of FFNN method was superior to GRNN method. Tests results and artificial neural network analysis showed that the temperature was the most effective parameter on the resilient modulus of HMA containing RAP materials. In addition by increasing RAP content, the resilient modulus of HMA increased.
Keywords:
Authors
Ahmad Mansourian
Bitumen and Asphalt Department, Road, Building and Urban development research center, Tehran, Iran
Ali Ghanizadeh
Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
Babak Golchin
Department of Civil Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :