Analysis of the upper tail of the short-term extreme tension distribution of mooring line by the peaks-over-threshold method

• Published in: Ocean engineering Vol. 281; p. 114994
• Main Authors: Hou, Hui-Min, Liu, Yong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Mooring system; Peaks-over-threshold; Short-term extreme response distribution; Mooring system; Peaks-over-threshold; Short-term extreme response distribution
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2023.114994

The consistency of the upper tail of the extreme response distribution plays an important role in the accuracy of the extreme response prediction. Although the peaks-over-threshold method has a high data utilization of the response time series, the consistency of the upper tail of the extreme response distribution was not thoroughly investigated, especially for the inherent randomness of the short-term sea state. This study aims to estimate the upper tail of the short-term extreme tension distribution of the mooring line considering the short-term variability by the peaks-over-threshold method. The consistency of the extreme tension distribution tail is quantified by a relative error compared with the global maximum method, and the theoretical optimal threshold is defined by minimizing the largest relative error among the tail quantiles for different thresholds. Results from a case study for the mooring line of the fish cage indicate that the effect of the random seed is significant for consistency in the prediction of the distribution tail of the short-term extreme tension. And the generalized Pareto distribution model combined with the L-moments method is recommended for the theoretical optimal threshold. These findings provide some useful suggestions for the preliminary design of the mooring line for offshore structures. •Consistency of the upper tail of the extreme response distribution by the peaks-over-threshold method is investigated.•Effect of the random seed on the consistency of the short-term extreme response distribution tail is analyzed.•Performance of different peaks extraction methods and peak distribution parameter estimation methods is discussed.