Probabilistic damage stability assessment is increasing the complexity of ship safety verification, particularly for vessels with irregular compartment layouts. This research introduces an automated scenario-generation algorithm that maps complex compartment relationships and flooding pathways, enabling more accurate risk analysis, reducing modelling errors and improving compliance with modern damage stability requirements.

Designing electric river ferries demands balancing battery capacity, hull efficiency and route-specific constraints. This integrated methodology combines Python-based automation with advanced naval architecture and simulation tools to optimise vessel design, accelerate trade-off analysis and reduce development time, enabling cost-effective, energy-efficient ferry solutions tailored to challenging inland operating environments.

Natural language processing is transforming how maritime professionals access complex regulatory requirements. By converting classification rules into searchable semantic datasets and applying AI-powered similarity matching, this approach delivers more relevant results than traditional keyword searches, reducing compliance effort, improving efficiency and helping organisations navigate increasingly complex regulatory environments

Predictive maintenance is becoming essential as ship systems grow more complex and maintenance demands increase. By combining vessel monitoring data, artificial intelligence and physics-based modelling, this approach detects hull and propeller degradation in real time, enabling proactive maintenance planning, reducing operational burden and improving fleet efficiency, availability and long-term performance.

Digital twins are evolving beyond real-time vessel monitoring into powerful enablers of maritime AI development. By embedding operational ship data within realistic environmental simulations, organisations can generate large-scale training datasets, test scenarios and support predictive maintenance. This approach offers a cost-effective pathway to smarter, more capable autonomous systems while reducing reliance on expensive sea trials.

Artificial intelligence is poised to transform every stage of a ship’s lifecycle, from concept design and construction to operations, maintenance and disposal. This perspective explores practical applications, ethical considerations and engineering challenges, highlighting how human-AI collaboration, explainability and responsible governance can unlock innovation while managing risk effectively at scale responsibly

Reinforcement learning is opening new possibilities for intelligent maritime route planning. By combining Deep Q-Network algorithms with ship dynamics and wind effects, this approach generates smoother, more navigable routes than conventional methods. The findings highlight how AI can enhance operational efficiency, manoeuvrability and decision-making in complex, obstacle-constrained marine environments.

Digital transformation is redefining naval capability through data-centric architectures, digital twins and advanced decision-support tools. By integrating interoperable services, scalable platforms and virtualised systems, the Digital Warship concept enables faster decision cycles, enhanced operational awareness and greater adaptability, providing a foundation for more connected, predictive and resilient naval operations.

Advanced finite element modelling remains essential to the safety of large ships, but increasingly detailed meshing requirements are creating significant engineering workloads. This research introduces innovative mesh copy and restoration capabilities that streamline repetitive analysis tasks, accelerate structural assessment and improve efficiency, helping designers manage growing complexity while maintaining compliance with evolving classification standards.

Real-time posture monitoring is advancing safety and productivity in shipbuilding’s demanding work environments. By combining LiDAR-enabled smart devices, computer vision and 3D skeletal modelling, this research delivers accurate, non-contact assessment of worker movements, overcoming visibility challenges and creating valuable data to support safer operations, ergonomic improvements and workforce performance.

Model-based approval is reshaping ship design and classification by replacing document-heavy workflows with data-driven digital processes. Leveraging the OCX standard, automated FEA validation and lightweight model exchange, this approach streamlines analysis, improves traceability and enhances efficiency, demonstrating a compelling transition from traditional PDF reporting to integrated

A data-driven modular design methodology is helping shipbuilders address workforce shortages and knowledge loss. By combining machine learning, Design Structure Matrices and Multi-Domain Matrices, the approach identifies component relationships, streamlines design workflows and captures expert knowledge, enabling faster development cycles, improved efficiency and more effective transfer of critical engineering expertise.