Keywords: liquefaction, earthquake, artificial neural network.

The occurrence of a large earthquake near a major city may be a rare event, but its societal and economic impact can be so devastating that it is a matter of national interest. The earthquakes do not only destroy the residents' properties, but also cause the instability of the whole societies. For example, the earthquake with Richter magnitude of 7.3 occurred at Chi-Chi City on September 21, 1999 has been recognised as the most serious disaster by public concerns in Taiwan. During the earthquake, numerous civil structures, such as buildings, highway embankments and retaining structures etc., have been damaged or completely destroyed. The resident regions affected by the earthquake have not been re-established until now.

In general, damage of civil structures during earthquakes occurs with two general failure modes evident. The first mode is that of structural failure, caused by strong acceleration of the earthquake, results in the damage of the structure itself. The second mode is that of foundation failure, caused by liquefaction, resulting in collapse of the structure as a whole. Therefore, estimation of the earthquake-induced liquefaction potential is essential for the civil engineers in the design procedure.

Since the 1960s, numerous research has been devoted to the evaluation of earthquake-induced liquefaction. The penetration resistance of the standard penetration test (SPT) is commonly used as an index of liquefaction potential. The reason why SPT test has been commonly used in the prediction of the liquefaction potential is because the in-site SPT-N value is easily obtained with reasonable accuracy. In the SPT-N value method, the earthquake-induced cyclic stress ratio (CSR) must be determined first, and then the cyclic resistance ratio can be calculated for the estimation of earthquake-induced liquefaction potential [1,2]. Also, Japanese Road Association [3] proposed an empirical procedure for liquefaction assessment.

The artificial neural network (ANN) has been widely applied in various areas such as water resources and coastal engineering [4,5]. Recently, ANN model has also been applied to the predication of earthquake-induced liquefaction [6,7,8]. Among these, Goh [6] used the back-propagation neural network (BPN) to predict seismic liquefaction potential. Based on the earthquake in Turkey, Ural and Saka [8] also applied the BPN for assessing liquefaction potential. Juang and Chen [7] forecasted seismic liquefaction potential from the cone penetration test (CPT) field data. The general application of artificial intelligence applications in geotechnical engineering was reviewed in Toll [9].

The aim of this paper is to apply an artificial neural network in the prediction of the occurrence of the earthquake-induced liquefaction. The raw field data at Wufeng city, Taiwan during Chi-Chi earthquake will be used to as an example to demonstrate the proposed model.

1

H.B. Seed, H. Tokimatsu, L.F. Harder, R.M. Chung, "Influence of SPT procedure in seismic liquefaction resistance evaluation", Journal of Geotechnical Engineering, ASCE. 111(12), 1425-1445, 1985. doi:10.1061/(ASCE)0733-9410(1985)111:12(1425)

2

K. Tokimatsu, Y. Yoshimi, "Empirical correction of soil liquefaction based on SPT N-Value and fine content", Soils and Foundation, 23(4), 56-74, 1983.

3

Japanese Road Association, Handbook of Earthquake Engineering Design, 1996.

4

M.N. French, W.F. Krajewski, R.R. Cuykendall, "Rainfall forecasting in space and time using a neural network", Journal of Hydrology, 137, 1-31, 1992. doi:10.1016/0022-1694(92)90046-X

5

T.L. Lee, C.P. Tsai, C.P., D.-S. Jeng, R.J. Shieh, "Neural network for the prediction and supplement of tidal record in Taichung Habor, Taiwan", Advances in Engineering Software, 33(6), 329-338, 2002. doi:10.1016/S0965-9978(02)00043-1

6

A.C.T. Goh, "Seismic liquefaction potential assessed by neural networks", Journal of Geotechnical Engineering, ASCE, 120(9), 1467-1480, 1995. doi:10.1061/(ASCE)0733-9410(1994)120:9(1467)

7

C.H. Juang, C.J. Chen, "CPT-based liquefaction evaluation using neural network". Journal of Computer-Aided Civil Infrastructure Engineering, 14, 221-229, 1999. doi:10.1111/0885-9507.00143

8

D.N. Ural, H. Saka, H. "Liquefaction assessment by artificial neural networks", The Electronic Journal of Geotechnical Engineering, 3 (available at http://www.ejge.com/1998/), 1998

9

D.G. Toll, "Artificial intelligence applications in geotechnical engineering", Electronic Journal of Geotechnical Engineering, 1 (available at http://www.ejge.com/1996/), 1996.

10

D.E. Rumelhart, G.E. Hinton, R.J. Williams, "Learning representations by back-propagating errors", Nature, 323: 533-536, 1996. doi:10.1038/323533a0

11

R.A. Jacobs, "Increased rates of convergence through learning rate adaptation", Neural Network, 1, 295-307, 1988. doi:10.1016/0893-6080(88)90003-2

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