Neuronet-Based Approach for Assessing Liquefaction Potential of Soils

Author:

Eldin Ali Hossam1,Najjar Yacoub M.1

Affiliation:

1. Department of Civil Engineering, Kansas State University, Manhattan, KS 66506

Abstract

A backpropagation artificial neural network (ANN) algorithm with one hidden layer was used as a new numerical approach to characterize the soil liquefaction potential. For this purpose, 61 field data sets representing various earthquake sites from around the world were used. To develop the most accurate prediction model for liquefaction potential, alternating combinations of input parameters were used during the training and testing phases of the developed network. The accuracy of the designed network was validated against an additional 44 records not used previously in either the network training or testing stages. The prediction accuracy of the neural network approach–based model is compared with predictions obtained by using fuzzy logic and statistically based approaches. Overall, the ANN model outperformed all other investigated approaches.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

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