The Safety Assessment of Civil Ships Against Capsizing Based on the Probability Analysis of Multiple Threshold-Crossings of Stochastic Sea Waves

Abstract This paper investigates the impact of threshold-crossing events on ship capsizing through a probabilistic model that predicts random wave heights. Utilizing statistical data from wave height observations, this paper proposes that random waves can be approximated and modeled using the Wiener process, employing autocorrelation function identification and probabilistic statistical verification methods. The threshold-crossing duration of a random wave is just the period of the wave exceeds a given threshold, which can reflect the frequency property of the stochastic sea waves. And the probability distribution of the period can theoretically be determined using the derived probability density function for the time interval between any two adjacent crossings of the Wiener process and a threshold. Based on the above theories, the capsizing probabilities of a civil ship sailing in different wave areas are analyzed under different safety thresholds considering certain ratios of the ship's intrinsic period and wave period. The research results can provide a reference for the anti-overturning design of the ships under the action of random waves.

Authors

Institutions

Publication Details

Journal
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering
Published
2024-10-11
DOI
https://doi.org/10.1115/1.4066829
Primary Topic
Maritime Navigation and Safety
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Safety Assessment of Civil Ships Against Capsizing Based on the Probability Analysis of Multiple Threshold-Crossings of Stochastic Sea Waves

Zhenhao Zhang, Guoqing Wei, Zhe Zeng, Fengwei Teng
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering
Maritime Navigation and Safety
article

The Safety Assessment of Civil Ships Against Capsizing Based on the Probability Analysis of Multiple Threshold-Crossings of Stochastic Sea Waves

Zhenhao Zhang, Guoqing Wei, Zhe Zeng, Fengwei Teng
article en

Abstract

Abstract This paper investigates the impact of threshold-crossing events on ship capsizing through a probabilistic model that predicts random wave heights. Utilizing statistical data from wave height observations, this paper proposes that random waves can be approximated and modeled using the Wiener process, employing autocorrelation function identification and probabilistic statistical verification methods. The threshold-crossing duration of a random wave is just the period of the wave exceeds a given threshold, which can reflect the frequency property of the stochastic sea waves. And the probability distribution of the period can theoretically be determined using the derived probability density function for the time interval between any two adjacent crossings of the Wiener process and a threshold. Based on the above theories, the capsizing probabilities of a civil ship sailing in different wave areas are analyzed under different safety thresholds considering certain ratios of the ship's intrinsic period and wave period. The research results can provide a reference for the anti-overturning design of the ships under the action of random waves.

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical EngineeringVol. 11(3)
Beibu Gulf University (CN), Changsha University of Science and Technology (CN)
National Natural Science Foundation of China
Life below water
Openalex Percentile: Top 78%
Maritime Navigation and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.