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A platform for research: civil engineering, architecture and urbanism
Artificial Neural Network Model for Time-Dependent Vertical Bearing Capacity of Preformed Concrete Pile
Mechanism of time effect on vertical ultimate bearing capacity (VUBC) of preformed concrete pile is analyzed. The effect strongly depends on seven parameters of pile engineering. Pile length, area of pile section, soil friction angle, soil consolidation coefficient, soil elastic module and time after pile installation and pile type are them. Considering time effect and soil consolidation, artificial neural network model to predict this time-dependent VUBC is established. Input layer includes seven parameters discussed above. Conjugate gradient method is adopted to train the net. Based on calculation of practical piles, results of the model are found to be in good agreement with field tests, and the efficiency of the present model is signalized.
Artificial Neural Network Model for Time-Dependent Vertical Bearing Capacity of Preformed Concrete Pile
Mechanism of time effect on vertical ultimate bearing capacity (VUBC) of preformed concrete pile is analyzed. The effect strongly depends on seven parameters of pile engineering. Pile length, area of pile section, soil friction angle, soil consolidation coefficient, soil elastic module and time after pile installation and pile type are them. Considering time effect and soil consolidation, artificial neural network model to predict this time-dependent VUBC is established. Input layer includes seven parameters discussed above. Conjugate gradient method is adopted to train the net. Based on calculation of practical piles, results of the model are found to be in good agreement with field tests, and the efficiency of the present model is signalized.
Artificial Neural Network Model for Time-Dependent Vertical Bearing Capacity of Preformed Concrete Pile
Applied Mechanics and Materials ; 29-32 ; 226-230
2010-08-13
5 pages
Article (Journal)
Electronic Resource
English
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