Autonomous ships, also known as maritime autonomous surface ships (MASS), are vessels that use artificial intelligence and robotics technologies to navigate independently. Due to their advanced technological capabilities, MASS is expected to play a significant role in the future of the shipping industry. The International Maritime Organization (IMO) is currently developing international standards for MASS classification, including accident avoidance technologies. However, the issue of how to mitigate the consequences of accidents involving autonomous ships has not been sufficiently addressed. Therefore, this study focuses on alternative design solutions and emergency response systems for MASS to properly control emergency situations and minimize the impact of accidents, such as flooding and fire on board. The goal is to efficiently address such accidents, especially in situations where the number of people on board is significantly reduced or the ship is being operated automatically or remotely, and to promptly detect and respond to such situations from a remote location. This paper investigates the possibility of modifying the design of the air conditioning system to delay flooding by considering a reduction in the number of crew members in order to prevent flooding. To prevent the spread of fire, the study examines early blockage measures for areas where air can enter. Flood and fire response systems were configured to be executed manually or automatically based on the results of presimulated scenarios defined in thousands of accident scenarios. Each accident propagation and response situation proposes an alternative using a coded shortcut key utilizing graphic symbols of international standard ISO 23120.

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

Considering the nature of marine accidents, even a single accident can result in significant damage to the environment and property, as well as loss of life. Therefore, the initial response should be rapid and accurate, and various decision support systems have been developed to achieve this. Research on simulating progressive flooding on board immediately after an accident is being actively conducted, but this requires high levels of computing power. In this study, a methodology for converting simulated ship motion data into a ship motion database is presented. The model of a training ship from the Korea Institute of Maritime and Fisheries Technology and KRISO in-house code SMTP was used for ship motion computations. The short-time Fourier transform was used to convert time-series motion data into a spectrogram motion database. A methodology for deriving a predicted location of the damage center is presented. The candidate locations of the damage centers were obtained by comparing the root mean square error values of the ship motion database from the simulation and real-time ship motion data. Finally, a probability function was suggested to confirm the predicted location of the damage center. Using 100 randomly selected test cases, our method showed 95% accuracy.

(MIBS), Non ballast water ship (NOBS), ballast water treatment system (BWTS)