A significant number of damage events continue to occur to ocean going vessels, many of which remain afloat in need of assistance to evaluate remedial actions to minimise the risk of further damage and conditions for onward transit for a repair facility. Therefore, it is vital that the post damage situation is rapidly understood to reduce damage propagation occurring, reducing the cost of repair and allowing more vessels to be recovered safely, ensuring the safety of personnel onboard. After an incident, it is often difficult to determine and model the precise damage scenario due to the inability to survey the area. Each scenario will have variability in geometrical and material properties, which will affect the residual strength of the structure. Variations in these aspects are not accounted for in methods currently utilised in damage response scenarios. Therefore, to be able to more accurately analyse the damage and provide better guidance to the crew in real time, it is important that this variability can be analysed, allowing an understanding of worst and best case scenarios, the probabilities of these occurring and their affect on the structural strength. Finite element analysis has been used to model damage scenarios due to the high level of accuracy that can be achieved. This paper demonstrates the implications of damage aperture on the limit state of stiffened steel panels, investigating the residual strength of the damaged structures and their sensitivity to variations in damage event, geometric and material properties. The results are then compared with the Smith and Dow progressive collapse method, [1,2], with conclusions being drawn about the use of this method in damage situations. Further to this, the effects of variability in the ship are investigated for damage scenarios, showing that these influence the ultimate strength of the structure to a larger extent in the failure of damaged plates than intact scenarios; however, lack of knowledge relating to the area of the damage could overshadow other potential variability within these scenarios.

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