Ocean platform deck damage prediction method considering different falling angles of elongated members

WANG Xiufei, LIU Kun, FEI Baoxiang, WANG Jiaxia, WANG Zili*

School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Abstract: ［Objectives ］ Due to the aging and illegal operation of crane equipment during hoisting operations on offshore platforms, dropped object accidents are the most prevalent type of accident, of which the dropping of slender rod-like objects such as drill collars and casings is the most common. Due to the small contact area and high velocity, the failure of the partial-plate structure is often caused, which requires attention.［Methods ］ This paper studies the specific deformation mechanism of a deck under the impact of dropped slender rods at different impact angles while considering the influence of the impact angle on damage to the deck. A new deformation model is proposed for decks subject to impact loads at different impact angles. Based on this new deformation model, a prediction method which considers the influence of the impact angle is obtained on the basis of the plastic mechanics theory.［Results］The results show that a small impact angle will cause great structural damage and deformation, as well as high structural energy absorption. According to the structural response, the impact angle can be divided into four angle intervals. The energy absorption curves obtained from the analytical method and simulation are similar in value and change trend.［Conclusions］ When a rod-like structure hits a deck at a certain angle, the shape of the plastic deformation area of the deck changes with the depth of the collision. The analytical results of the deformation characteristics of the deck at each stage can provide valuable references for the preliminary structural design and assessment of the crashworthiness of the deck structures of offshore platforms. Key words: falling object accident；deck structure；dropped rod；collision angle；prediction analysis