Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model |
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| Authors: | Daeho Cha Michael Blumenstein |
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| Institution: | a Griffith School of Engineering, Griffith University, Gold Coast Campus, QLD 4222, Australia
b School of Information and Communication Technology, Griffith University Gold Coast Campus, Australia |
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| Abstract: | In the last few decades, considerable efforts have been devoted to the phenomenon of wave-induced liquefactions, because it is one of the most important factors for analysing the seabed and designing marine structures. Although numerous studies of wave-induced liquefaction have been carried out, comparatively little is known about the impact of liquefaction on marine structures. Furthermore, most previous researches have focused on complicated mathematical theories and some laboratory work. In the present study, a data dependent approach for the prediction of the wave-induced liquefaction depth in a porous seabed is proposed, based on a multi-artificial neural network (MANN) method. Numerical results indicate that the MANN model can provide an accurate prediction of the wave-induced maximum liquefaction depth with 10% of the original database. This study demonstrates the capacity of the proposed MANN model and provides coastal engineers with another effective tool to analyse the stability of the marine sediment. |
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| Keywords: | Wave-induced liquefaction Artificial neural networks Multi-artificial neural network |
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