AFSs-RBF neural network for predicting earthquake-induced liquefaction of light loam

Author

Hao, Dongxue ; Chen, Rong

Author_Institution

Sch. of Civil & Archit. Eng., Northeast Dianli Univ., Jilin, China

Volume

3

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

1518

Lastpage

1522

Abstract

In this study, AFSs-RBF neural network, based on the adaptive fuzzy systems (AFSs) combined with the radial basic function (RBF) is developed to evaluate liquefaction potentials of light loam induced by Tangshan earthquake in Tianjin area. The proposed system has strong self-adaptation and can dynamically adjust the number of hidden units through sample training under supervision. Six parameters related to earthquake and site conditions are selected as inputs and four outputs are designed to evaluate the extent of liquefaction according to code for seismic design of buildings. With the help of measurement data, it is shown that the AFSs-RBF network approach is able to predict liquefaction potentials and has a high success in training and testing for evaluating liquefaction classification of light loam.

Keywords

building standards; earthquakes; fuzzy set theory; geophysics computing; geotechnical engineering; liquefaction; radial basis function networks; seismology; structural engineering computing; AFS-RBF neural network aprroach; Tangshan earthquake; Tianjin area; adaptive fuzzy systems; earthquake-induced liquefaction prediction; light loam; liquefaction classification evaluation testing; measurement data; radial basic function neural network; seismic design; Adaptation model; Artificial neural networks; Data models; Earthquakes; Indexes; Testing; Training; AFSs-based RBF; classification identification; liquefaction of light loam;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583880

Filename

5583880