Hybrid Parabolic Interpolation - Artificial Neural Network Method (HPI-ANNM) for long-term extreme response estimation of steel risers

被引：9

作者：

Monsalve-Giraldo, J. S. ^{[1
]}

Cortina, Joao P. R. ^{[1
]}

de Sousa, Fernando J. M. ^{[1
]}

Videiro, Paulo M. ^{[1
]}

Sagrilo, Luis V. S. ^{[1
]}

机构：

[1] Univ Fed Rio de Janeiro, COPPE, Civil Engn Program, Lab Anal & Reliabil Offshore Struct, Rio De Janeiro, Brazil

来源：

APPLIED OCEAN RESEARCH | 2018年 / 76卷

关键词：

Long-term analysis; Extreme response; Parabolic interpolation method; Artificial neural networks; Steel risers; Nonlinear stochastic dynamic analysis; MARINE STRUCTURES; COUPLED ANALYSIS; WAVE HEIGHT; PREDICTION;

D O I：

10.1016/j.apor.2018.05.008

中图分类号：

P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

This paper presents a computer efficient approach to evaluate the multi-dimensional integral found in the evaluation of the long-term extreme response of marine structures. The proposed method is a hybrid combination of two numerical procedures. The first one consists of a parabolic interpolation scheme used to obtain the statistical parameters describing the short-term peaks probability distribution of the time-series responses, designated as PIM (Parabolic Interpolation Method), which reduces the total number of short-term structural analyses. The second one is an Artificial Neural Network-based surrogate model which is used to obtain long response time histories based on short finite element-based simulations. The approach is named as HPI-ANNM (Hybrid Parabolic Interpolation - Artificial Neural Network Method). The efficiency and accuracy of the proposed hybrid method is compared with the complete long-term integration method in the analysis of 100-yr characteristic values of cross-section utilization ratios of a Steel Catenary Riser (SCR) connected to a semisubmersible platform in deep water.

引用

页码：221 / 234

页数：14