Location

San Diego, California

Session Start Date

5-24-2010

Session End Date

5-29-2010

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

Appears In

International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics

Meeting Name

Fifth Conference

Publisher

Missouri University of Science and Technology

Publication Date

5-24-2010

Document Version

Final Version

Rights

© 2010 Missouri University of Science and Technology, All rights reserved.

Document Type

Article - Conference proceedings

File Type

text

Language

English

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May 24th, 12:00 AM May 29th, 12:00 AM

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.