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Laboratory Study and Investigation on Significance Level of Fatigue Phenomenon in Warm Mix Asphalt Modified with Nano-Silica | ||
| Journal of Rehabilitation in Civil Engineering | ||
| مقاله 6، دوره 8، شماره 2 - شماره پیاپی 18، مرداد 2020، صفحه 92-113 اصل مقاله (1.55 M) | ||
| نوع مقاله: Regular Paper | ||
| شناسه دیجیتال (DOI): 10.22075/jrce.2019.17478.1331 | ||
| نویسندگان | ||
| Saber Kie Badroodi1؛ Mahmood Reza Keymanesh* 2؛ Gholamali Shafabakhsh3 | ||
| 1Ph.D. candidate of Tehran PNU University, Tehran, Iran | ||
| 2Associate Professor, North Tehran Branch, Payam Noor University | ||
| 3Faculty of Civil Engineering, Semnan University | ||
| تاریخ دریافت: 28 اسفند 1397، تاریخ بازنگری: 24 تیر 1398، تاریخ پذیرش: 14 مهر 1398 | ||
| چکیده | ||
| The present research aims to conduct laboratory assessment on fatigue phenomenon in warm mix asphalt modified with nano-silica and including reclaimed asphalt pavement materials by the aid of review on self-healing behavior and measurement of validity of laboratory results by modeling via neural artificial network in neutral network of SPSS software. For this purpose, 2% weight of sasobit and 3, 5 and 7 % weights of base bitumen-to-bitumen (85-100) were added and they were stirred up by high-cut mixer. Then, the specimens of four-point flexural test were made by the reclaimed bitumen samples. The quantities of 0, 70 and 100% of reclaimed asphalt materials were utilized for aging simulation process in warm mix asphalt to build four-point flexural tested slabs. The findings indicate that adding nano-silica may essentially affect rising self-healing level in warm mix asphalts. The current study intends to present a model based on neural artificial network technique to predict behavior of warm asphalt specimens including different nano-material contents and to compare them with the laboratory results for measurement of validity of the given model. The given results show high precision of the model at level of 0.951. | ||
| کلیدواژهها | ||
| Warm Mix Asphalt؛ Fatigue؛ Self-Healing؛ Reclaimed Asphalt Materials؛ Nano-Silica؛ Neural Network | ||
| مراجع | ||
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