Characterization of Long-period Ship Wave Loading and Vessel Speed for Risk Assessment for Rock Groyne Designs via Extreme Value Analysis

Conference Paper (2021)
Author(s)

S. Memar (TU Delft - Hydraulic Structures and Flood Risk)

Oswaldo Morales-Nápoles (TU Delft - Hydraulic Structures and Flood Risk)

B. Hofland (TU Delft - Hydraulic Structures and Flood Risk)

G. Melling (Federal Waterways Engineering and Research Institute (BAW))

Research Group
Hydraulic Structures and Flood Risk
Copyright
© 2021 S. Memar, O. Morales Napoles, Bas Hofland, Gregor Melling
DOI related publication
https://doi.org/10.3850/978-981-18-2016-8_302-cd
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 S. Memar, O. Morales Napoles, Bas Hofland, Gregor Melling
Research Group
Hydraulic Structures and Flood Risk
Bibliographical Note
Accepted Author Manuscript@en
Pages (from-to)
2525-2532
ISBN (print)
978-981-18-2016-8
Reuse Rights

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Abstract

During the last two decades, increasing vessel size in major German estuaries has led to the significant change of the local loading regime i.e. increased importance of ship-induced waves and currents. As a consequence, the intensity of ship-induced loads has increased considerably, resulting in damage to rock structures such as revetments, training walls, and groynes. Research into the causes of rock structure deterioration by the Federal Waterways Engineering and Research Institute (BAW) has shown that for large ships in relatively narrow waterways, the long-period primary ship wave loading has become the most prescient factor for rock structure damage. Looking into the future, it can be expected that the increase in the vessel dimensions will lead to an increase in the ship-wave loading. For this reason, analysing long-term changing trends of long-period ship waves and vessel speed to understand the wave-structure interaction is of significant importance. In this study, the stochastic characterization of long-period primary wave height, drawdown, and speed of the vessel through the water at Juelssand in the Lower Elbe Estuary was analysed via extreme value analysis and copula modeling, and the bivariate return periods were calculated. The one-parameter bivariate copula was utilized to analyse the data. The dependence pattern between the variables was investigated using five parametric copula families: Gaussian, Gumbel, Clayton, Frank, and student's t.

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