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Modeling of Resilient Modulus of Asphalt Concrete Containing Reclaimed Asphalt Pavement Using Feed-Forward and Generalized Regression Neural Networks | ||
| Journal of Rehabilitation in Civil Engineering | ||
| مقاله 11، دوره 6، شماره 1 - شماره پیاپی 11، اردیبهشت 2018، صفحه 132-147 اصل مقاله (1.44 M) | ||
| نوع مقاله: Regular Paper | ||
| شناسه دیجیتال (DOI): 10.22075/jrce.2017.10876.1177 | ||
| نویسندگان | ||
| Ahmad Mansourian* 1؛ Ali Reza Ghanizadeh2؛ Babak Golchin3 | ||
| 1Bitumen and Asphalt Department, Road, Building and Urban development research center, Tehran, Iran | ||
| 2Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran | ||
| 3Department of Civil Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran | ||
| تاریخ دریافت: 26 اسفند 1395، تاریخ بازنگری: 25 مرداد 1396، تاریخ پذیرش: 14 شهریور 1396 | ||
| چکیده | ||
| 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, 216 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 0.993 and 0.981, respectively. These two values were 0.999 and 0.967 in case of GRNN. So, according to comparison of R2 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. | ||
| کلیدواژهها | ||
| Asphalt Pavement؛ Reclaimed Asphalt؛ Resilient Modulus؛ Neural Networks | ||
| مراجع | ||
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