Evaluation of Wave-induced Liquefaction in a Porous Seabed: Using an Artificial Neural Network and a Genetic Algorithm -based model

The evaluation of wave-induced liquefaction is one of the key factors for analysing seabed characteristics and the design of marine structures. Numerous investigations of wave-induced liquefaction have been proposed. However, most previous research has focused on complicated mathematical theories and laboratory work. In this study, we contribute an alternative approach for the prediction of the waveinduced liquefaction using an Artificial Neural Network (ANN) and a Genetic Algorithm (GA)-based model. Combined ANN and GA-based models are still a newly developed area in coastal engineering. In this study, a Genetic Algorithm-based approach is proposed to find optimal weights for the ANN model. It reduces the training time, and improves the forecasting accuracy for wave-induced maximum liquefaction depth, compared to using the normal ANN training procedure. Simulation results demonstrate the capacity of the proposed ANN model for the prediction of wave induced maximum liquefaction depth in addition to the proposal of GAs for training the ANN model.

[1]  Holger R. Maier,et al.  PREDICTING SETTLEMENT OF SHALLOW FOUNDATIONS USING NEURAL NETWORKS , 2002 .

[2]  Dong-Sheng Jeng,et al.  Application of artificial neural networks in tide-forecasting , 2002 .

[3]  H. Sekiguchi,et al.  Analysis of wave-induced liquefaction of sand beds , 2001 .

[4]  M. S. Rahman.  Wave‐induced instability of seabed: Mechanism and conditions , 1991 .

[5]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[6]  Shinji Sassa,et al.  Analysis of progressive liquefaction as a moving-boundary problem , 2001 .

[7]  善 功企,et al.  Mechanism of wave-induced liquefaction and densification in seabed. , 1990 .

[8]  Dong-Sheng Jeng,et al.  Effects of dynamic soil behavior and wave non-linearity on the wave-induced pore pressure and effective stresses in porous seabed , 2003 .

[9]  C. Hsein Juang,et al.  CPT‐Based Liquefaction Evaluation Using Artificial Neural Networks , 1999 .

[10]  A. Goh.  Seismic liquefaction potential assessed by neural networks , 1994 .

[11]  Tsong Yen,et al.  Modeling Confinement Efficiency of Reinforced Concrete Columns with Rectilinear Transverse Steel using Artificial Neural Networks , 2003 .

[12]  Dong-Sheng Jeng,et al.  Wave-induced seabed instability in front of a breakwater , 1997 .

[13]  Jun Wang,et al.  A neural network model for liquefaction-induced horizontal ground displacement , 1999 .

[14]  M. Biot.  Theory of Propagation of Elastic Waves in a Fluid‐Saturated Porous Solid. I. Low‐Frequency Range , 1956 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Dong-Sheng Jeng,et al.  Assessment of Wave-Induced Liquefaction in a Porous Seabed: Application of an ANN Model , 2004 .

[17]  Michael B. Abbott,et al.  Applications of artificial neural networks to the generation of wave equations from hydraulic data , 1999 .

[18]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[19]  L. Bjerrum,et al.  Geotechnical problems involved in foundations of structures in the North Sea , 1973 .