Location

San Diego, California

Session Start Date

5-24-2010

Session End Date

5-29-2010

Abstract

With the increase in population, the evaluation of liquefaction is becoming more important for land use planning and development. In soil deposits under undrained condition, earthquakes induce cyclic shear stresses, may lead to soil liquefaction. Artificial neural network (ANN) is one of the, artificial intelligence (AI) approaches that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built up in conventional liquefaction engineering, an alternative general regression neural network model is proposed in this paper. To meet this objective, a total of 30 boreholes are introduced into the model. The data includes the results of field test from (Babol, Mazandaran, Iran). The results produced by the proposed Artificial Neural Network model compared well with the determined liquefaction decision obtained by simplified methods. It provides a viable liquefaction potential assessment tool that assist geotechnical engineers in making an accurate and realistic predictions. Furthermore, this study integrates knowledge learned from field test and seismic parameters to the ongoing development of liquefaction analysis. The results show that there is liquefaction potential in western part of Babol, and in southern part of Babol no liquefaction potential were seen. In middle part and eastern part low liquefaction potential were predicted by ANNs. This study shows that neural networks are a powerful computational tool which can analyze the complex relationship between soil liquefaction potential and effective parameters in liquefaction.

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

Artificial Neural Network Model for Prediction of Liquefaction Potential in Soil Deposits

San Diego, California

With the increase in population, the evaluation of liquefaction is becoming more important for land use planning and development. In soil deposits under undrained condition, earthquakes induce cyclic shear stresses, may lead to soil liquefaction. Artificial neural network (ANN) is one of the, artificial intelligence (AI) approaches that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built up in conventional liquefaction engineering, an alternative general regression neural network model is proposed in this paper. To meet this objective, a total of 30 boreholes are introduced into the model. The data includes the results of field test from (Babol, Mazandaran, Iran). The results produced by the proposed Artificial Neural Network model compared well with the determined liquefaction decision obtained by simplified methods. It provides a viable liquefaction potential assessment tool that assist geotechnical engineers in making an accurate and realistic predictions. Furthermore, this study integrates knowledge learned from field test and seismic parameters to the ongoing development of liquefaction analysis. The results show that there is liquefaction potential in western part of Babol, and in southern part of Babol no liquefaction potential were seen. In middle part and eastern part low liquefaction potential were predicted by ANNs. This study shows that neural networks are a powerful computational tool which can analyze the complex relationship between soil liquefaction potential and effective parameters in liquefaction.