Seabed instability is one of the important reasons for offshore structure damage. Unlike most previous studies that treated the oscillatory and residual response separately, a coupled model for wave-induced response in non- homogeneous seabeds is proposed in the present study. Effects of spatial derivative terms in seabed parameters are introduced into the accumulation of pore pressure. Model validations are conducted by comparing the present simulation with the previous analytical solutions, wave flume tests, and numerical simulations. The validated model is applied to investigate the effects of grain size, non-homogeneous distribution of seabed parameters, and non-linear wave conditions on the wave-induced seabed dynamic response and liquefaction. It is found that (1) the oscillatory mechanism in pore pressure variation dominates in the coarser seabed, while the residual mechanism becomes obvious with the decreasing grain size, (2) consideration of the non-uniform permeability and Young's modulus would promote and suppress the pore pressure accumulation and liquefaction, respectively, and (3) the simulation error in pore pressure between homogeneous and non-homogeneous seabeds increases with the increase of the wave nonlinearity.

The soil response under the inherent cyclic loading conditions when dealing with offshore foundations can be considered by using contour plots. These plots are derived from several cyclic laboratory tests and characterize the general cyclic soil behaviour. In the design process with explicit numerical methods, such plots are needed in order to assess the soil behaviour under arbitrary loading conditions and hence estimate the cyclic foundation response. In the paper, excess pore pressure contour plots for a poorly graded medium sand are derived from numerous constant volume (CV) cyclic direct simple shear (DSS) tests and a new approach for parametrization of the plots is presented. Subsequently, the data are assessed regarding scaling for other sand soils, i.e., construction of contour plots with only a small number of test results by using the general trends observed.