• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.138-138
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• 2018

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.148-148
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• 2018

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.6
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• pp.646-653
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• 2018

Marine accidents caused by dragging anchors occur constantly due to enlargement of ships' size and unusual weather conditions. Nevertheless, vessel operators rely on their experience because the calculations of actual holding power and external forces are complex and inconvenient. The purpose of this study was to propose a program for the anchor dragging risk assessment in order to provide crew and VTSO with the information to determine easily the danger of dragging and take appropriate action. The input data in this program were composed of the ship's basic particulars, anchoring condition, and external environment etc. on calculating for the wind pressure, frictional force, drift force, and holding power. Three dragging anchor accidents were applied to the program's data input at the time of the day, then the result was assessed by 'warning', which was verified with a high confidence. As a result, the risk of dragging anchors can be predicted in advance through this program. In further studies, it is necessary to simplify the input data and improve user convenience through automatic input from various equipment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.822-831
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• 2021

Although programs have been developed to evaluate the risk of dragging anchors, it is practically difficult for VTS(vessel traffic service) operators to calculate and evaluate these risks by obtaining input factors from anchored ships. Therefore, in this study, the gross tonnage (GT) that could be easily obtained from the ship by the VTS operators was set as an independent variable, and linear and nonlinear regression analyses were performed using the input factors as the dependent variables. From comparing the fit of the polynomial model (linear) and power series model (nonlinear), the power series model was evaluated to be more suitable for all input factors in the case of container ships and bulk carriers. However, in the case of tanker ships, the power supply model was suitable for the LBP(length between perpendiculars), width, and draft, and the polynomial model was evaluated to be more suitable for the front wind pressure area, weight of the anchor, equipment number, and height of the hawse pipe from the bottom of the ship. In addition, all other dependent variables, except for the front wind pressure area factor of the tanker ship, showed high degrees of fit with a coefficient of determination (R-squared value) of 0.7 or more. Therefore, among the input factors of the dragging anchor risk assessment program, all factors except the external force, seabed quality, water depth, and amount of anchor chain let out are automatically applied by the regression analysis model formula when only the GT of the ship is provided.

• Ocean Systems Engineering
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• v.12 no.3
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• pp.267-284
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• 2022

A subsea pipeline designed across active shipping lane prones to failure against external interferences such as anchorage activities, hence risk assessment is essential. It requires quantifying the geometric probability derived from ship traffic distribution based on Automatic Identification System (AIS) data. The actual probability density function from historical vessel traffic data is ideal, as for rapid assessment, conceptual study, when the AIS data is scarce or when the local vessels traffic are not utilised with AIS. Recommended practices suggest the probability distribution is assumed as a single peak Gaussian. This study compares several fitted Gaussian distributions and Monte Carlo simulation based on actual ship traffic data in main ship direction in an active shipping lane across a subsea pipeline. The results shows that a Gaussian distribution with five peaks is required to represent the ship traffic data, providing an error of 0.23%, while a single peak Gaussian distribution and the Monte Carlo simulation with one hundred million realisation provide an error of 1.32% and 0.79% respectively. Thus, it can be concluded that the multi-peak Gaussian distribution can represent the actual ship traffic distribution in the main direction, but it is less representative for ship traffic distribution in other direction. The geometric probability is utilised in a quantitative risk assessment (QRA) for subsea pipeline against vessel anchor dropping and dragging and vessel sinking.