Hybrid support vector regression - Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin

Construction and Building Materials

Volumn 34, Issue , 2012, Pages 321-329

  Safarzadegan Gilan, Siamak ^a   Bahrami Jovein, Hamed ^a   Ramezanianpour, Ali Akbar ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Compressive strength; Concrete; Metakaolin; Particle swarm optimization (PSO); RCPT; Support vector regression (SVR); Surface resistivity

Indexed keywords

ADAPTIVE NEURAL FUZZY INFERENCE SYSTEMS; DATA SAMPLE; DOMINANT INPUT; FEATURE SELECTION ALGORITHM; GENERALIZATION CAPABILITY; HIGH DEGREE OF ACCURACY; HYBRID MODEL; HYBRID SUPPORT; LEARNING PARAMETERS; MATERIAL PROPERTY; METAKAOLINS; MIX PROPORTIONS; PARTICLE SWARM OPTIMIZATION MODELS; PREDICTIVE ACCURACY; RAPID CHLORIDE PENETRATION TESTS; RCPT; SUPPORT VECTOR REGRESSION (SVR); SURFACE RESISTIVITY; SYSTEM MODELING;

CHLORINE COMPOUNDS; COMPRESSIVE STRENGTH; CONCRETES; PARTICLE SWARM OPTIMIZATION (PSO); REGRESSION ANALYSIS;

FORECASTING;

EID: 84860608836     PISSN: 09500618     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conbuildmat.2012.02.038     Document Type: Article

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