Artificial neural network in CPT base liquefaction prediction
Various simplified procedue have been developed, using case studies that liquefied or not during earthquake, to estimate liquefaction potential of soils. In order to address the collective knowledge built up in conventional liquefaction engineering, this paper proposed to use a artificial neural network, ANN as an alternative tools. ANN has the capability to train itself with available data sets and extrapolate outcome for unknown senerio based on the training. It is particularly helpful for large data sets when human brain is inefficient. Various ANN models have already been in used for liquefaction assessment. However, this paper is more objective in applying ANN in liquefcation prediction. First, the data bases used for training and testing are well verified and well accepted in literature. Second, the inputs for the model are selected on their physical meaning with respect to liquefaction. Third, the source of data sets for training and testing are diffrent. The ANN model achieved a comparable accuracy with other publications where large number of inputs has been used.