A Data-Driven Intelligent Prediction Approach for Collision Responses of Honeycomb Reinforced Pipe Pile of the Offshore Platform

Abstract

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.

Modal Analysis of the Hydrodynamic Force of a Capsule in a Hydraulic Capsule Pipeline

“…To accurately resolve the hydrodynamic force, we propose to resolve the dynamic characteristics of capsule motion via modal analysis instead of using the time-averaged results. Modal analysis is broadly used to resolve the dynamic characteristics of dynamical systems, especially the system vibration responses for solid structures [39].…”

Ship Collision Risk Assessment

“…The Special Issue, 'Ship Collision Risk Assessment', introduces 12 papers which include newly developed approaches to reduce the risk to a ship in the fields of the material, structural capacity/layout, avoidance, navigation/algorithms, etc. [1][2][3][4][5][6][7][8][9][10][11][12].…”

“…Finally, Yang et al [12] proposed a data-driven intelligent prediction approach for obtaining a structural response of honeycomb-reinforced pipe piles of offshore platforms by collisions. They combined the Artificial Neural Network (ANN) and Dynamic Particle Swarm Optimization (DPSO) for developing the proposed method.…”