Location
San Diego, California
Presentation Date
27 May 2010, 4:30 pm - 6:20 pm
Abstract
Liquefaction is considered among the most important hazards associated with earthquakes. The damage resulting from seismic liquefaction may be huge; thus, there always exists needs to mitigate the damage associated with such risks. One of the main problems challenging geotechnical engineers is how to assess the seismic liquefaction hazard. Statistical and probabilistic approaches for seismic liquefaction are currently available. In this paper, a general regression neural networks approach (GRNNs) has been used to assess the liquefaction hazard in Egypt. Thus, data from new locations can be analyzed using GRNNs to obtain the liquefaction risk associated with this new site. The computer package “Neuroshell 2®” has been extensively used to build up the GRNNs models. Highly encouraging results have been obtained in the field of seismic liquefaction mitigation.
Department(s)
Civil, Architectural and Environmental Engineering
Meeting Name
5th International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics
Publisher
Missouri University of Science and Technology
Document Version
Final Version
Rights
© 2010 Missouri University of Science and Technology, 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
Salem, Suzan S. and El-Zahaby, Khalid, "Application of General Regression Neural Networks (GRNNs) in Assessing Liquefaction Susceptibility" (2010). International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics. 3.
https://scholarsmine.mst.edu/icrageesd/05icrageesd/session04/3
Included in
Application of General Regression Neural Networks (GRNNs) in Assessing Liquefaction Susceptibility
San Diego, California
Liquefaction is considered among the most important hazards associated with earthquakes. The damage resulting from seismic liquefaction may be huge; thus, there always exists needs to mitigate the damage associated with such risks. One of the main problems challenging geotechnical engineers is how to assess the seismic liquefaction hazard. Statistical and probabilistic approaches for seismic liquefaction are currently available. In this paper, a general regression neural networks approach (GRNNs) has been used to assess the liquefaction hazard in Egypt. Thus, data from new locations can be analyzed using GRNNs to obtain the liquefaction risk associated with this new site. The computer package “Neuroshell 2®” has been extensively used to build up the GRNNs models. Highly encouraging results have been obtained in the field of seismic liquefaction mitigation.
