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The collision between the pipe legs of jacket platforms and bypassing ships is of great concern for the safety assessment of platforms. Honeycomb structures have been widely used owing to their unique deformation and mechanical properties under dynamic impact loads. In this paper, two typical honeycomb structures, namely hexagonal honeycomb and arrow honeycomb, were constructed for the impact protection of inclined pipe legs in jacket platforms, and the present study aimed to assess the dynamical performance and crushing resistance of the designed honeycomb reinforced structure under ship collision by using the numerical simulation software ANSYS/LS-DYNA. The dynamical performance of the honeycomb reinforced pipe leg was investigated considering various influential parameters, including the impact velocity and impact direction. The crashworthiness of the two types of honeycomb was evaluated and compared by different criteria, namely the maximum impact depth (δmax), specific energy absorption (SEA) and the proposed index offset sliding (OS). The results demonstrated that both the hexagonal honeycomb structure and the arrow honeycomb structure can reduce the damage of inclined pipe legs caused by ship collision, while the hexagonal honeycomb can provide the better anti-collision capacity, which can well reduce the offset sliding and better protect the pipe leg from ship collision.

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The potential collision between the ship and the pipe piles of the jacket structure brings huge risks to the safety of an offshore platform. Due to their high energy-absorbing capacity, honeycomb structures have been widely used as impact protectors in various engineering applications. This paper proposes a data-driven intelligent approach for the prediction of the collision response of honeycomb-reinforced structures under ship collision. In the proposed model, the artificial neural network (ANN) is combined with the dynamic particle swarm optimization (DPSO) algorithm to predict the collision responses of honeycomb reinforced pipe piles, including the maximum collision depth (δmax) and maximum absorption energy (Emax). Furthermore, a data-driven evaluation method, known as grey relational analysis (GRA), is proposed to evaluate the collision responses of the honeycomb-reinforced pipe piles of offshore platforms. Results of the case study demonstrate the accuracy of the DPSO-BP-ANN model, with measured mean-square-error (MSE) of 5.06 × 10−4 and 4.35 × 10−3 and R2 of 0.9906 and 0.9963 for δmax and Emax, respectively. It is shown that the GRA method can provide a comprehensive evaluation of the performance of a honeycomb structure under impact loads. The proposed model provides a robust and efficient assessment tool for the safe design of offshore platforms under ship collisions.

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