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ABSTRACT
This paper concentrates on the simulating methodology for the reliability analyses of the groyne erosion stability with the randomness, including the probabilistic turbulences of the mean value, the variation, and the correlation of the pertinent parameters. On the basis of the interpretation for the mechanism of the groyne erosion, the backslope gradient of the local eroded pit is adopted as the major factor for the establishment of the reliability function. Created for the reliability analyses in this paper is the systematic algorithm that can solve the complicated reliability function of the groyne erosion stability and compute the corresponding failure probability. The reliability function here incorporates the random parameters that are non-Gaussian and statistically correlated. These complicated parameters exist in most coastal engineering cases. The comprehensive sensitivity of the groyne erosion stability is investigated based on the measured data of the random parameters and their designed randomness that includes the designed variation and the designed correlative coefficients. Results show that the probabilistic turbulences of the velocity of the natural main stream flow and the mouth radius of the crateriform shape of the local eroded pit affect significantly the groyne erosion stability; the optimal conformation interval of the assembly average diameter of the particles in the local river bed sediments is 0.62, 0.68 mm, where the value of the failure probability is the minimal one; the statistic correlation between the behaviors of the natural main stream and other random parameters deteriorates certainly the performance of the coastal engineering structures. According to the sensitivity analyses, the suggested values of the random parameters are offered in this paper. The methodology developed here is a generalized approach to the reliability simulation for the pertinent structures of coastal engineering and can be applied directly in the n-dimensional independently standardized Gaussian space. On the basis of the comparison with the Monte Carlo simulation, the methodology developed here shows the super efficiency.