Effect of Database Generation on Damage Consequences' Assessment Based on Random Forests (CROSBI ID 301044)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Braidotti, Luca ; Prpić-Oršić, Jasna ; Valčić, Marko
engleski
Effect of Database Generation on Damage Consequences' Assessment Based on Random Forests
Recently, the application of machine learning has been explored to assess the main damage consequences without employing flooding sensors. This method can be the base of a new generation of onboard decision support systems to help the master during the progressive flooding of the ship. In particular, the application of random forests has been found suitable to assess the final fate of the ship and the damaged compartments' set and estimate the time-to-flood. Random forests have to be trained using a database of precalculated progressive flooding simulations. In the present work, multiple options for database generation were tested and compared: three based on Monte Carlo (MC) sampling based on different probability distributions of the damage parameters and a parametric one. The methods were tested on a barge geometry to highlight the main effects on the damage consequences' assessment in order to ease the further development of flooding-sensor-agnostic decision support systems for flooding emergencies.
damaged ship ; progressive flooding ; random forests ; database generation ; decision support system
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
9 (11)
2021.
1303
17
objavljeno
2077-1312
10.3390/jmse9111303
Trošak objave rada u otvorenom pristupu
Povezanost rada
Brodogradnja, Informacijske i komunikacijske znanosti, Tehnologija prometa i transport