Scholarlink

<p>Environmental contours (ECs) quantify concurrent marine environmental extremes for designated return periods, serving as critical inputs for the structural dynamic analysis and reliability design. In this study, we proposed a novel framework for 3D ECs of wind, wave, and current, which integrates measured data analysis, marginal distribution modeling based on the adaptive bandwidth Kernel Density Estimation-Pareto (ABKP) model, joint probability distribution (JPD) construction using R-vine copulas, and 3D EC generation via direct sampling method. The developed semi-parametric ABKP model effectively combines flexibility with reliable extrapolation capability compared with traditional parametric and non-parametric methods, and the R-vine copula accurately characterizes the multivariate dependence structure. Using measured data from American National Data Buoy Center Station 41,052, we demonstrate the implementation workflow and validate the rationality of this framework. Results show that the synergistic integration of ABKP model and R-vine copula ensures robust and accurate wind-wave-current JPD modeling. The generated 3D ECs reproduce consistent distribution patterns with the observations and maintain smooth morphological characteristics. The pronounced heavy-tailed behavior of wave height and current speed leads to evident amplification of extreme values in higher return periods. The proposed framework provides a systematic and data-driven approach for generating realistic and correlated extreme wind-wave-current conditions, offering valuable support for marine structural safety and multi-hazard design.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A framework for 3D direct sampling-based environmental contours of wind, wave, and current using ABKP model and R-vine copula

  • 期刊论文

  • 发表日期：

    2026年3月16日

查看全文
  • Guangsong Song,
  • Hui Jiang,
  • Xiaoyu Bai,
  • Dongxu Wu

摘要

Environmental contours (ECs) quantify concurrent marine environmental extremes for designated return periods, serving as critical inputs for the structural dynamic analysis and reliability design. In this study, we proposed a novel framework for 3D ECs of wind, wave, and current, which integrates measured data analysis, marginal distribution modeling based on the adaptive bandwidth Kernel Density Estimation-Pareto (ABKP) model, joint probability distribution (JPD) construction using R-vine copulas, and 3D EC generation via direct sampling method. The developed semi-parametric ABKP model effectively combines flexibility with reliable extrapolation capability compared with traditional parametric and non-parametric methods, and the R-vine copula accurately characterizes the multivariate dependence structure. Using measured data from American National Data Buoy Center Station 41,052, we demonstrate the implementation workflow and validate the rationality of this framework. Results show that the synergistic integration of ABKP model and R-vine copula ensures robust and accurate wind-wave-current JPD modeling. The generated 3D ECs reproduce consistent distribution patterns with the observations and maintain smooth morphological characteristics. The pronounced heavy-tailed behavior of wave height and current speed leads to evident amplification of extreme values in higher return periods. The proposed framework provides a systematic and data-driven approach for generating realistic and correlated extreme wind-wave-current conditions, offering valuable support for marine structural safety and multi-hazard design.