Abstract
Collision accidents may lead to significant asset damage and human casualties. This paper introduces a direct analysis methodology that makes use of Automatic Identification System (AIS) data to estimate collision probability and generate scenarios for use in ship damage stability assessment. Potential collision scenarios are detected from AIS data by an avoidance behaviour-based collision detection model (ABCD-M) and the probability of collision is estimated in various routes pertaining to a specific area of operation. Damage extents are idealised by the Super – Element (SE) method accounting for the influence of surrounding water in way of contact. Results are presented for a Ro - Pax ship operating from 2018 to 2019 in the Gulf of Finland. It is confirmed that collision probability is extremely diverse among voyages and the damages obtained correlate well with those adopted by the UN IMO Regulatory Instrument SOLAS (2020). It is concluded that the method is by nature sensitive to traffic features in the selected case study area. Yet, it is useful for the evaluation of flooding risk for ships operating in real hydro-meteorological conditions.
Original language | English |
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Article number | 109605 |
Number of pages | 20 |
Journal | Ocean Engineering |
Volume | 237 |
Early online date | 7 Aug 2021 |
DOIs | |
Publication status | Published - 1 Oct 2021 |
Keywords
- big-data analytics
- collisions
- damage stability
- flooding risk
- passenger ships
- super-element method