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Empirical model for liquefaction resistance of soils based on artificial neural network learning of case histories
Chien Hsun Chen, C. Hsein Juang, Matt J. Schuster
土木工程學系
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引文 斯高帕斯(Scopus)
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Keyphrases
Liquefaction Resistance
100%
Soil Resistance
100%
Artificial Neural Network Learning
100%
Artificial Neural Network
66%
Neural Network Training
33%
Neural Network
33%
High Complexity
33%
Piezocone Penetration Test
33%
Liquefaction
33%
Liquefaction Potential
33%
Cyclic Stress Ratio
33%
Geotechnical Problems
33%
Pore Pressure
33%
Limit State
33%
CPTu
33%
Feedforward Neural Network
33%
Multi-parameter
33%
Output Variable
33%
Soil Reaction
33%
Input Vector
33%
Liquefaction Triggering
33%
Behaviour Score
33%
Three-input
33%
Cone Resistance
33%
Feed Forward Back Propagation Neural Network (FFBPNN)
33%
Intended Model
33%
Soil Behavior
33%
Excess Pore Pressure Ratio
33%
Pressure Parameters
33%
Binary Field
33%
Engineering
Empirical Model
100%
Artificial Neural Network
100%
Feedforward
40%
Pressure Ratio
20%
Cyclic Stress
20%
Field Observation
20%
Limit State
20%
Input Vector
20%
Excess Pore Pressure
20%
Stress Ratio
20%
Binary Variable
20%