RISK-BASED LEAKAGE DETECTION AND INCIDENT PREVENTION IN LARGE-SCALE SAUDI INDUSTRIAL AND INFRASTRUCTURE PROJECTS

Large-scale industrial and infrastructure projects in Saudi Arabia combine extensive process piping, transmission networks, utility corridors, water systems, storage interfaces, and high-consequence facilities. Leakage in these assets can evolve from a maintenance defect into a safety, environmental, production, or reputational incident when detection is delayed or when integrity decisions are separated from consequence analysis. This review develops an integrative framework for risk-based leakage detection and incident prevention, synthesizing recent peer-reviewed work on pipeline integrity, risk-based inspection, computational leak detection, acoustic and optical sensing, machine learning, digital twins, corrosion prediction, and water-network diagnostics. The evidence indicates that no single detection technology is consistently sufficient across fluids, operating regimes, asset geometries, and leak sizes. More robust practice couples probability-of-failure models with consequence severity, detectability, sensor confidence, and lifecycle condition information. For Saudi applications, the central implication is that leak detection should be designed as a dynamic integrity-management function rather than as an isolated alarm system.