Support vector machines approach to HMA stiffness prediction

Journal of Engineering Mechanics

Volumn 137, Issue 2, 2010, Pages 138-146

  Gopalakrishnan, Kasthurirangan ^a   Kim, Sunghwan ^a  

^a IOWA STATE UNIVERSITY   (United States)

Author keywords

Artificial intelligence (AI); Asphalt; Construction materials; Dynamic modulus; Support vector machines (SVM)

Indexed keywords

AI TECHNIQUES; ARTIFICIAL NEURAL NETWORK; CONSTRUCTION MATERIALS; DATA POINTS; DEGREE OF COMPLEXITY; DYNAMIC MODULI; DYNAMIC MODULUS; HMA MIXTURES; HOT MIX ASPHALT; MECHANICAL BEHAVIOR; MULTIVARIATE REGRESSION; MULTIVARIATE REGRESSION ANALYSIS; PAVEMENT DESIGN; PAVEMENT PERFORMANCE; PAVEMENT RESPONSE; PREDICTION MODEL; PREDICTION PERFORMANCE; STATISTICAL LEARNING THEORY; STIFFNESS PREDICTION; SUPPORT VECTOR MACHINES (SVM); SVM MODEL;

ASPHALT; DYNAMICS; FORECASTING; GEARS; MATHEMATICAL MODELS; MECHANICAL PROPERTIES; MULTIVARIANT ANALYSIS; NEURAL NETWORKS; PAVEMENTS; PIPE; REGRESSION ANALYSIS; VECTORS;

SUPPORT VECTOR MACHINES;

ARTIFICIAL INTELLIGENCE; ASPHALT; CONSTRUCTION MATERIAL; ELASTIC MODULUS; MULTIVARIATE ANALYSIS; NUMERICAL MODEL; PAVEMENT; REGRESSION ANALYSIS; STIFFNESS; UNCERTAINTY ANALYSIS;

EID: 78651512466     PISSN: 07339399     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)EM.1943-7889.0000214     Document Type: Article

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