Page 195 - Demo

How is AI being used to predict structural, machinery, or safety risks before they turn into incidents, and how reliable are these models today?AI can enhance maritime safety by analysing sensor outputs, historical failure patterns, weather data, and operational behaviour to detect early indicators of structural fatigue, machinery degradation, or emerging safety hazards. Predictive maintenance models will increasingly help prevent breakdowns, extend asset life, and reduce downtime by identifying anomalies well before conventional methods would capture them. However, AI remains most effective when paired with marine engineering and human expertise, particularly because explainability and governance are essential for trust. Today%u2019s models are robust enough to support everyday operational decisions, but expert validation remains central to safe adoption across the fleet.To what extent will real-time ship data affect physical inspections in terms of ensuring compliance with safety and environmental regulations?Real-time data will not eliminate physical inspections, but it will redefine their purpose. Continuous monitoring allows surveyors to verify compliance and technical performance remotely, reducing reliance on rigid survey windows and shifting attention toward targeted, risk-based interventions. This mirrors the industry%u2019s broader shift from siloed processes to a more integrated, system-wide approach to safety and regulatory obligations. With high-quality data streams feeding digital platforms, classification societies can confirm the health of machinery, structural integrity, or environmental performance without always requiring physical attendance. In-person inspections will remain essential for critical components and damage assessment, yet overall frequency and duration can decrease, improving efficiency and reducing operational disruption. RINA supports this transition by enabling continuous environmental compliance and helping shipping companies meet current regulatory obligaMay 2026 191