Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms

Computers and Geotechnics

Volumn 36, Issue 4, 2009, Pages 616-625

  Ardalan, H ^a   Eslami, A ^b   Nariman Zadeh, N ^a  

^a UNIVERSITY OF GUILAN   (Iran)

^b AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Cone penetration test (CPT); Genetic algorithm; GMDH; Neural networks; Piezocone (CPTu); Pile shaft capacity

Indexed keywords

CHARGE TRAPPING; DATA HANDLING; FRICTION; GENETIC ALGORITHMS; PILES; SENSITIVITY ANALYSIS; TESTING; TUNNELING (EXCAVATION);

CASE HISTORY; CONE PENETRATION TEST (CPT); CONE RESISTANCES; DIRECT METHODS; GMDH; GROUP METHOD OF DATA HANDLINGS; IN-SITU TESTS; INPUT PARAMETERS; MODEL OUTPUTS; PIEZOCONE (CPTU); PILE DESIGNS; PILE SHAFT CAPACITY; POINT RESISTANCES; POLYNOMIAL NEURAL NETWORKS; SHAFT RESISTANCES; SLEEVE FRICTIONS; STATIC LOADING TESTS; TEST DATUM; TOE RESISTANCES;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CONE PENETRATION TEST; ESTIMATION METHOD; GENETIC ALGORITHM; IN SITU TEST; LOADING TEST; PILE; SENSITIVITY ANALYSIS;

EID: 61749083047     PISSN: 0266352X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compgeo.2008.09.003     Document Type: Article

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