Location
St. Louis, Missouri
Presentation Date
06 Apr 1995, 10:30 am - 12:30 pm
Abstract
Neural networks have emerged as a powerful computational technique for modeling nonlinear multivariate relationships. The neural network is a product of artificial intelligence research. This paper examines the feasibility of using neural networks for assessing liquefaction potential, from actual field records. The paper starts with a brief overview of the basic architecture and concepts of neural networks. The application of the neural network methodology to evaluate seismic liquefaction potential is then presented.
Department(s)
Civil, Architectural and Environmental Engineering
Meeting Name
3rd International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics
Publisher
University of Missouri--Rolla
Document Version
Final Version
Rights
© 1995 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
Goh, A. T. C., "Predicting Seismic Liquefaction Using Neural Networks" (1995). International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics. 15.
https://scholarsmine.mst.edu/icrageesd/03icrageesd/session03/15
Included in
Predicting Seismic Liquefaction Using Neural Networks
St. Louis, Missouri
Neural networks have emerged as a powerful computational technique for modeling nonlinear multivariate relationships. The neural network is a product of artificial intelligence research. This paper examines the feasibility of using neural networks for assessing liquefaction potential, from actual field records. The paper starts with a brief overview of the basic architecture and concepts of neural networks. The application of the neural network methodology to evaluate seismic liquefaction potential is then presented.
