Location
San Diego, California
Presentation Date
29 Mar 2001, 7:30 pm - 9:30 pm
Abstract
The increasing popularity of the cone penetration test (CPT) for site investigations has led to several methods for predicting liquefaction potential from CPT data. This paper describes a feed-forward neural network model trained by back-propagation for predicting liquefaction potential. The model requires the following seven input variables: cone resistance, total vertical stress, effective vertical stress, earthquake magnitude, maximum horizontal acceleration at ground surface, the mean grain size D50, and the seismic shear-stress ratio. A total of ninety-six data sets from different sites around the world were used for training, and eighty-two data sets were used for testing and validating the neural network model. The model gave an overall success rate of 96% for correctly predicting the liquefaction potential.
Department(s)
Civil, Architectural and Environmental Engineering
Meeting Name
4th International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics
Publisher
University of Missouri--Rolla
Document Version
Final Version
Rights
© 2001 University of Missouri--Rolla, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Document Type
Article - Conference proceedings
File Type
text
Language
English
Recommended Citation
Kurup, Pradeep U. and Dudani, Nitin K., "CPT Evaluation of Liquefaction Potential Using Neural Networks" (2001). International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics. 28.
https://scholarsmine.mst.edu/icrageesd/04icrageesd/session04/28
Included in
CPT Evaluation of Liquefaction Potential Using Neural Networks
San Diego, California
The increasing popularity of the cone penetration test (CPT) for site investigations has led to several methods for predicting liquefaction potential from CPT data. This paper describes a feed-forward neural network model trained by back-propagation for predicting liquefaction potential. The model requires the following seven input variables: cone resistance, total vertical stress, effective vertical stress, earthquake magnitude, maximum horizontal acceleration at ground surface, the mean grain size D50, and the seismic shear-stress ratio. A total of ninety-six data sets from different sites around the world were used for training, and eighty-two data sets were used for testing and validating the neural network model. The model gave an overall success rate of 96% for correctly predicting the liquefaction potential.
