Application of Relevance Vector Machine for Prediction of Ultimate Capacity of Driven Piles in Cohesionless Soils

Geotechnical and Geological Engineering

Volumn 30, Issue 5, 2012, Pages 1261-1270

  Samui, Pijush ^a  

^a VIT UNIVERSITY   (India)

Author keywords

Artificial neural network; Cohesionless soil; Pile; Relevance vector machine; Ultimate capacity

Indexed keywords

ARTIFICIAL NEURAL NETWORK MODELS; BAYESIAN FRAMEWORKS; COHESIONLESS SOIL; DRIVEN PILE; PERFORMANCE CRITERION; PREDICTION VARIANCE; RELEVANCE VECTOR MACHINE; ROOT MEAN SQUARE ERRORS; ULTIMATE CAPACITY;

NEURAL NETWORKS; PILES; SOIL STRUCTURE INTERACTIONS; SUPPORT VECTOR MACHINES;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; BEARING CAPACITY; COHESIONLESS SOIL; ERROR ANALYSIS; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; PILE; PREDICTION; REGRESSION ANALYSIS; SOIL-STRUCTURE INTERACTION;

EID: 84866524554     PISSN: 09603182     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10706-012-9539-9     Document Type: Article

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