View in EDS

Quantitative comparison of environmental contour approaches.

Saved in:
Bibliographic Details
Title: Quantitative comparison of environmental contour approaches.
Authors: Hauteclocque, Guillaume de1 (AUTHOR) guillaume.de-hauteclocque@bureauveritas.com, Mackay, Ed2 (AUTHOR) E.Mackay@exeter.ac.uk, Vanem, Erik3 (AUTHOR) Erik.Vanem@dnvgl.com
Source: Ocean Engineering. Feb2022, Vol. 245, pN.PAG-N.PAG. 1p.
Subjects: Offshore structures, Statistical models, Pragmatics
Abstract: Environmental contours are a pragmatic and widespread method to estimate the long-term extreme response of marine structures. Over the years, a range of approaches have been proposed. A benchmarking study was recently conducted to compare the various methods using a common set of data. The current work extends this benchmark study by providing a quantitative assessment of the contours submitted to the exercise. The estimates of long-term responses from the contours were compared against a response-based analysis (RBA) for a wide range of responses. While some contour methods agreed well with estimates from the RBA (relative errors less than 10%), most methods were found to give large errors relative to the RBA. For the 1-year responses most methods showed a large positive bias, whilst both positive and negative biases were found for the 20-year responses. The reasons for the differences between the contours and RBA were explored. It was shown that the fitted statistical models accounted for a large portion of the error in some approaches, with both positive and negative biases of the order of 50% for some contributions, depending on the response type. Whilst for other methods, the statistical model gave accurate predictions for most responses, no models were able to capture all response behaviours for all locations. Secondly, most contour methods do not account for serial correlation in the data. It is shown that this introduces a significant positive bias into long-term response estimates, especially for lower return periods. The level of error introduced by the type of contour method is dependent on the assumption made about the shape of the failure region in the contour definition. For the predominantly unimodal response types considered, contours which approximate the failure region as having a linear boundary (IFORM and direct sampling contours), introduce relatively little error for most responses. However, for some responses, the direct sampling contours were found to introduce errors in the range 20%–40%, depending on the variable space in which they are constructed. The ISORM and highest density contours were found to have a significant over-conservatism bias, which would be expected for the response types considered. • Extreme responses from environmental contours compared to response-based analysis. • A large variation in the performance of the various contours was found. • Fitted models for the joint distributions contribute significantly to the errors. • Neglecting the serial correlation leads to significant overestimation. • Errors from the contour approximation itself are low for most response types. [ABSTRACT FROM AUTHOR]
Copyright of Ocean Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Environmental contours are a pragmatic and widespread method to estimate the long-term extreme response of marine structures. Over the years, a range of approaches have been proposed. A benchmarking study was recently conducted to compare the various methods using a common set of data. The current work extends this benchmark study by providing a quantitative assessment of the contours submitted to the exercise. The estimates of long-term responses from the contours were compared against a response-based analysis (RBA) for a wide range of responses. While some contour methods agreed well with estimates from the RBA (relative errors less than 10%), most methods were found to give large errors relative to the RBA. For the 1-year responses most methods showed a large positive bias, whilst both positive and negative biases were found for the 20-year responses. The reasons for the differences between the contours and RBA were explored. It was shown that the fitted statistical models accounted for a large portion of the error in some approaches, with both positive and negative biases of the order of 50% for some contributions, depending on the response type. Whilst for other methods, the statistical model gave accurate predictions for most responses, no models were able to capture all response behaviours for all locations. Secondly, most contour methods do not account for serial correlation in the data. It is shown that this introduces a significant positive bias into long-term response estimates, especially for lower return periods. The level of error introduced by the type of contour method is dependent on the assumption made about the shape of the failure region in the contour definition. For the predominantly unimodal response types considered, contours which approximate the failure region as having a linear boundary (IFORM and direct sampling contours), introduce relatively little error for most responses. However, for some responses, the direct sampling contours were found to introduce errors in the range 20%–40%, depending on the variable space in which they are constructed. The ISORM and highest density contours were found to have a significant over-conservatism bias, which would be expected for the response types considered. • Extreme responses from environmental contours compared to response-based analysis. • A large variation in the performance of the various contours was found. • Fitted models for the joint distributions contribute significantly to the errors. • Neglecting the serial correlation leads to significant overestimation. • Errors from the contour approximation itself are low for most response types. [ABSTRACT FROM AUTHOR]
ISSN:00298018
DOI:10.1016/j.oceaneng.2021.110374