Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Ultimate Bearing Capacity of Prestressed Pipe Pile Based on BP Neural Network
Based on BP neural network, this paper had a prediction on ultimate bearing capacity of prestressed pipe pile. Taking pile diameter, effective pile length, ultimate average value of friction standard value, ultimate average value of end resistance standard value as influences factors, the prediction model of pile bearing capacity based on BP neural network was obtained. It was found that, the average value of absolute value for the relative error of fitting value of pile bearing capacity compared with the observed value for 70 groups of independent variables training BP neural network model was 3.1498%; And the average value of absolute value for the relative error of prediction value of pile bearing capacity compared with the observed value for 10 groups of independent variables validating BP neural network model was 3.50126% whose precision was better than ANFIS’5.32293%. The following conclusion can be drawn that, the prediction model of ultimate bearing capacity of prestressed pipe pile based on BP neural network is feasible.
Prediction of Ultimate Bearing Capacity of Prestressed Pipe Pile Based on BP Neural Network
Based on BP neural network, this paper had a prediction on ultimate bearing capacity of prestressed pipe pile. Taking pile diameter, effective pile length, ultimate average value of friction standard value, ultimate average value of end resistance standard value as influences factors, the prediction model of pile bearing capacity based on BP neural network was obtained. It was found that, the average value of absolute value for the relative error of fitting value of pile bearing capacity compared with the observed value for 70 groups of independent variables training BP neural network model was 3.1498%; And the average value of absolute value for the relative error of prediction value of pile bearing capacity compared with the observed value for 10 groups of independent variables validating BP neural network model was 3.50126% whose precision was better than ANFIS’5.32293%. The following conclusion can be drawn that, the prediction model of ultimate bearing capacity of prestressed pipe pile based on BP neural network is feasible.
Prediction of Ultimate Bearing Capacity of Prestressed Pipe Pile Based on BP Neural Network
Applied Mechanics and Materials ; 101-102 ; 228-231
27.09.2011
4 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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