Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data
In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on the results of cone penetration test (CPT) data. Approximately, 300 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.
Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data
In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on the results of cone penetration test (CPT) data. Approximately, 300 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.
Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data
Nejad, F. Pooya (Autor:in) / Jaksa, Mark B. (Autor:in)
GeoFlorida 2010 ; 2010 ; Orlando, Florida, United States
GeoFlorida 2010 ; 1432-1441
15.02.2010
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data
| British Library Conference Proceedings | 2010
Prediction of pile settlement based on cone penetration test results: An ANN approach
| Online Contents | 2015
Prediction of pile settlement based on cone penetration test results: An ANN approach
| Springer Verlag | 2014
Prediction of liquefaction induced settlement based on cone penetration test data
| British Library Conference Proceedings | 2001