Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning

Abstract

Recently, progressive flooding simulations have been applied onboard to support decisions during emergencies based on the outcomes of flooding sensors. However, only a small part of the existing fleet of passenger ships is equipped with flooding sensors. In order to ease the installation of emergency decision support systems on older vessels, a flooding-sensor-agnostic solution is advisable to reduce retrofit cost. In this work, the machine learning algorithms trained with databases of progressive flooding simulations are employed to assess the main consequences of a damage scenario (final fate, flooded compartments, time-to-flood). Among the others, several classification techniques are here tested using as predictors only the time evolution of the ship floating position (heel, trim and sinkage). The proposed method has been applied to a box-shaped barge showing promising results. The promising results obtained applying the bagged decision trees and weighted k-nearest neighbours suggests that this new approach can be the base for a new generation of onboard decision support systems.

Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests

“…When the hull integrity is compromised leading to the progressive flooding of the ship, a few pieces of information are essential to support the master's decision [11]. The most important one is the final fate of the ship, namely whether the ship will survive the damage scenario, reaching a new safe equilibrium position, or will sink, capsize, or shift towards an unsafe condition, e.g., excessive equilibrium heeling angle.…”

“…The process is sketched in Figure 1. More details about the applied methodology can be found in [11].…”

“…The floodwater pouring among connected internal rooms is governed by the hydraulic laws, and thus, it can be predicted by applying progressive flooding simulation codes. Using RF, a link can be searched between the time records of sinkage s, heel φ and trim θ angles, i.e., the predictors, and the above-mentioned damage consequences, i.e., the responses [11]. Considering a time instant t * during the progressive flooding, the past time evolution of the floating position is known.…”

“…The following cumulative probability function was applied to draw the damage areas: (11) where A min and A max are the minimum and maximum damage areas that can be defined for each different ship. In a real application, floodwater inflow due to very small damages can be controlled by the bilge system.…”

“…The required set of instruments is limited to inclinometers (usually fit on all the vessels) and one or more level radar(s) fit in still-pipes or below bridge wings to measure ship draught. Then, Machine Learning (ML) can be used to correlate the recorded floating position with the main flooding consequences [11].…”