Effects of binders on resilient modulus of rubberized mixtures containing RAP using artificial neural network approach

Journal of Testing and Evaluation

Volumn 37, Issue 2, 2009, Pages 129-138

  Xiao, Feipeng ^a   Amirkhanian, Serji N ^a  

^a AMRL   (United States)

Author keywords

Artificial neural networks; G* sin , stiffness; Important index; m values; RAP; Resilient modulus; Sensitivity analysis; Viscosity

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; IMPORTANT INDEX; M-VALUES; RAP; RESILIENT MODULUS; ARTIFICIAL NEURAL NETWORK MODELING; ARTIFICIAL NEURAL-NETWORK BASED MODELING; G* SIN Δ; INPUT VARIABLES; M-VALUE; MODULUS VALUES; RECLAIMED ASPHALT PAVEMENTS; RHEOLOGICAL PROPERTY;

ASPHALT; ASPHALT PAVEMENTS; BACKPROPAGATION; BINDERS; CIVIL ENGINEERING; MIXTURES; MODELS; REGRESSION ANALYSIS; RHEOLOGY; RUBBER; SENSITIVITY ANALYSIS; STIFFNESS; VISCOSITY; ASPHALT MIXTURES; COMPLEX NETWORKS; FORECASTING; NEURAL NETWORKS;

NEURAL NETWORKS; SENSITIVITY ANALYSIS;

EID: 63449133230     PISSN: 00903973     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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