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Reliability analysis of submarine slope considering the spatial variability of the sediment strength using random fields
Institution:1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Institute of Engineering Risk and Disaster Prevention, Wuhan University, 299 Bayi Road, Wuhan 430072, PR China;2. School of Civil and Environmental Engineering, Nanyang Technological University, Block N1, 50 Nanyang Avenue, 639798, Singapore
Abstract:The properties of marine sediments vary spatially, and the undrained shear strength of marine clay increases linearly with depth because of depositional processes and the effective overburden pressure. To evaluate the stability of submarine slope considering the spatial variability of soil strength, the random field discretized by the Karhunen-Loève expansion is combined with the limit equilibrium method to conduct reliability analysis. For simplicity, our physical model does not include many complexities such as the effects of excess pore water pressure on the stability of submarine slopes. Stability estimates of the infinite slope model, under both static and seismic loading, are made with three types of one-dimensional stationary or non-stationary random fields. The two-dimensional slope model is also analyzed, where the shear strength varies with the positions of the strips because of the discrete random-field function for the soil material. In submarine slope reliability analysis, the non-stationary random field of the linearly increasing soil strength is used, instead of the commonly used stationary one. To obtain the failure probability through Monte Carlo simulations, a novel response surface method based on Gaussian process regression is introduced to build the surrogate model. The computational efficiency is significantly increased, because there is a considerable reduction of calls of the deterministic analysis. Therefore, the proposed method makes the prediction of submarine landslides which are usually rare events with very small probabilities more efficient.
Keywords:Submarine landslide  Reliability analysis  Spatial variability  Karhunen-Loève expansion  Random field  Gaussian process regression
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