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

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