Neural network model for the prediction of wave-induced liquefaction potential

The prediction of wave-induced liquefaction has been recognised by coastal geotechnical engineers as an important factor when considering the design of marine structures. All existing models have been based on conventional approaches of engineering mechanics with limited laboratory work. In this study, we propose an alternative approach for the prediction of the maximum liquefaction depth, based on neural network (NN). Unlike previous engineering mechanics approaches, the proposed NN model is based on data learning knowledge, rather than on knowledge of mechanisms. Numerical examples demonstrate the capacity of the proposed NN model for the prediction of wave-induced liquefaction depth, which provides civil engineers with another effective tool.

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