A hybrid deep learning method for the real-time prediction of collision damage consequences in operational conditions

Ship collisions can result in catastrophic outcomes, necessitating effective real-time collision risk assessment methods for proactive risk management. These methods need to rapidly evaluate both the probability of collision and the potential damage dimensions (length, height, and penetration) in real conditions. Existing frameworks often underestimate collision damage consequences during operational risk assessments. This paper presents a hybrid deep learning approach for the real-time prediction of collision damage dimensions under real ship operation conditions. Collision scenarios are identified using Automatic Identification System (AIS) data, with damage extents simulated through the Super Element (SE) method. A comprehensive database of collision scenarios and corresponding damage assessments is developed, sourced from realistic operational data of Ro-Pax ship in the Gulf of Finland. The deep learning model is trained and validated using this dataset, ensuring the model's relevance and practical applicability. Extensive comparative analyses and generalization tests demonstrate the high accuracy of the model in predicting ship collision damages in diverse ship operational conditions. In addition, traditional simulation methods for evaluating damage dimensions require approximately 10 min, whereas the trained deep learning model reduces the time to less than 0.1 s, enabling real-time potential collision consequence assessment in real operational conditions. The proposed model may provide significant insights for ship operators, enhancing ship safety and supporting intelligent decision-making in ship operations.