This study conducts a series of undrained cyclic shear tests on saturated coral sand with different initial relative densities ( D_r) subjected to a 90° jump rotation of the principal stress with various initial orientations. Considering liquefiable coral sand as a fluid, an important finding is that the variation in the apparent viscosity with the number of cycles is significantly affected by D_r, the effective mean principal stress ( [ ] ), cyclic stress ratio, cyclic loading path, and cyclic loading frequency. The average flow coefficient (κ) is introduced to describe the fluidity of saturated coral sand under cyclic loading by considering the variation in the shape of the stress–strain rate curve. A positive exponential correlation is observed between κ and the excess pore water pressure (EPWP) ratio ( r_u) under the given cyclic loading modes. Another significant finding is that the solid–liquid phase change time is the turning point of the apparent viscosity and the average flow coefficient gradients with r_u. The inversion point’s EPWP ratio is approximately 0.9, defined as the EPWP ratio during the solid–liquid phase transformation ( r_uth). The r_uth value is not affected by cyclic loading mode, [ ] , and D_r.

In this study, a series of resonant-column experiments were conducted on marine clays from Bohai Bay and Hangzhou Bay, China. The characteristics of the dynamic shear modulus (G) and damping ratio (D) of these marine clays were examined. It was found that G and D not only vary with shear strain (γ), but they also have a strong connection with soil depth (H) (reflected by the mean effective confining pressure (σ_m) in the laboratory test conditions). With increasing H (σ_m) and fixed γ, the value of G gradually increases; conversely, the value of D gradually decreases, and this is accompanied by the weakening of the decay or growth rate. An intelligent model based on a back-propagation neural network (BPNN) was developed for the calculation of these parameters. Compared with existing function models, the proposed intelligent model avoids the forward propagation of data errors and the need for human intervention regarding the fitting parameters. The model can accurately predict the G and D characteristics of marine clays at different H (σ_m) and the corresponding γ. The prediction accuracy is universal and does not strictly depend on the number of neurons in the hidden layer of the neural network.

A series of undrained cyclic shear tests was carried out on saturated coral sands, with different initial physical states, as they were subjected to rotations of 90° in the paths of cyclic stress with various initial orientations under isotropic conditions. An important finding is that the cumulative dissipated energy required for liquefaction (W_s) was significantly affected by their fines content (FC), relative density (D_r), and effective stress of consolidation ( ${p^{\prime }}_0$), but was independent of the conditions of cyclic loading. W_s increased significantly with the increase in ${p^{\prime }}_0.$ When FC was less than the threshold fines content, W_s increased with FC and decreased with an increase in D_r. The equivalent intergranular void ratio (e*) was introduced to reflect the effects of FC, physical states of the particles, and inter-particle contact on the physical properties of coral sand. The results revealed a unique relationship between e* and W_s at ${p^{\prime }}_0$ = 100 kPa. Furthermore, the unified model of the relation between the normalized cumulative dissipated energy and the ratio of excess pore water pressure (EPWP) followed an arc-tangent function. This model can be used to characterize the generation of EPWP with the cumulative dissipated energy of fine-grained sand under isotropic consolidation conditions.