Evaluation of static creep of FORTA-FI strengthened asphalt mixtures using experimental, statistical and feed-forward back-propagation ANN techniques

Autor: Mohammad Ali Khasawneh, Omar Albatayneh, Madhar Taamneh
Rok vydání: 2019
Předmět:
Zdroj: International Journal of Pavement Research and Technology. 12:43-53
ISSN: 1997-1400
1996-6814
DOI: 10.1007/s42947-019-0006-3
Popis: This paper investigates the effect of using different proportions (0%, 1%, 3%, 5%) of FORTA-FI fiber on asphalt mixtures static creep behavior under different compactive efforts (35 blows, 50 blows, 75 blows) and testing temperatures (25°C, 40°C, 55°C). The accumulated micro-strain was found to increase as temperature increases and stiffness modulus was found to decrease as temperature increases. These results are expected due to the viscoelastic nature of asphalt mixtures. Additionally, feed-forward back-propagation Artificial Neural Networks (ANN) was utilized in order to build a model that describes and predicts the relationship between deformation and stiffness modulus with multiple variables such as; Temperature (Temp), Fiber Content (FC) and Compactive Effort (CE). Evidently, a powerful predictive model was developed with Coefficient of Determination (R2) of 94%. The results using the SPSS statistical software of the two-way and three-way Analysis of Variance (ANOVA) tools showed that temperature and FORTA-FI content have significant interactions effects. Whereas, compactive effort effect on stiffness modulus was insignificant. Further, results obtained using Minitab statistical software confirmed the above findings.
Databáze: OpenAIRE
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