Near-real-time ship grounding damage assessment using Bayesian networks

TL;DR

This paper introduces a Bayesian network-based probabilistic framework for rapid ship grounding damage assessment, integrating multiple data sources and uncertainties to improve decision-making during incidents.

Contribution

It presents a novel Bayesian network model that combines diverse information sources for real-time damage assessment, surpassing traditional deterministic methods.

Findings

Bayesian networks effectively incorporate multiple data sources and uncertainties.

The model can update damage assessments dynamically with new evidence.

Case studies show potential to replace costly underwater inspections.

Abstract

In a post-grounding event, the rapid assessment of hull girder residual strength is crucial for making informed decisions, such as determining whether the vessel can safely reach the closest yard. One of the primary challenges in this assessment is the uncertainty in the estimation of the extent of structural damage. Although classification societies have developed rapid response damage assessment tools, primarily relying on 2D Smith-based models, these tools are based on deterministic methods and conservative estimates of damage extent. To enhance this assessment, we propose a probabilistic framework for rapid grounding damage assessment of ship structures using Bayesian networks (BNs). The proposed BN model integrates multiple information sources, including underwater inspection results, hydrostatic and bathymetric data, crashworthiness models, and hydraulic models for flooding and oil spill monitoring. By systematically incorporating these parameters and their associated uncertainties within a causal framework, the BN allows for dynamic updates as new evidence emerges during an incident. Two case studies demonstrate the effectiveness of this methodology, highlighting its potential as a practical decision support tool to improve operational safety during grounding events. The results indicate that combining models with on-site observations can even replace costly underwater inspections.