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

2025-06-06

AI Summary

Rapid and reliable assessment of residual hull girder strength following ship grounding is challenged by high uncertainty in damage states and the excessive conservatism of traditional deterministic methods (e.g., Smith’s method). Method: This study proposes, for the first time, a Bayesian network–based probabilistic decision framework tailored for grounding emergency response. It integrates heterogeneous multi-source data—including underwater inspection, hydrographic and bathymetric surveys, structural crashworthiness, oil spill modeling, and hydrodynamic simulations—to enable dynamic updating and quantitative characterization of damage-state uncertainty. The framework supports evidence-driven online inference and risk-adaptive decision adjustment, substantially reducing reliance on costly underwater inspections. Contribution/Results: Validated on two real grounding incidents, the framework significantly improves both assessment timeliness and safety margins, overcoming the limitations of conventional 2D deterministic modeling. It provides a generalizable, probabilistic support tool for maritime emergency decision-making.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Assess hull girder residual strength post-grounding for safety decisions
• Address uncertainty in structural damage extent estimation
• Replace costly inspections with model-observation integration

Innovation

• Bayesian networks for probabilistic damage assessment
• Integrates multiple data sources dynamically
• Reduces need for costly underwater inspections