The growing scale, complexity, and interdependency of modern civil engineering megaprojects have significantly increased the importance of uncertainty management throughout project delivery. Conventional risk management approaches often rely on static documentation practices that separate risk analysis from operational decision-making processes. While such methods provide formal risk visibility, they frequently fail to influence real-time engineering, planning, and execution decisions under dynamic project conditions. As a result, megaprojects continue to experience delays, cost overruns, coordination failures, and operational disruptions despite extensive risk management procedures. This paper examines risk-informed project delivery as an integrated decision-oriented framework in which uncertainty actively shapes engineering and management processes across the full project lifecycle. The study argues that successful megaproject delivery depends not only on identifying risks, but also on embedding risk considerations directly into planning, design, sequencing, coordination, and execution decisions. Particular attention is given to adaptive decision-making, prioritization of critical uncertainties, operational trade-off analysis, and cross-disciplinary communication within large-scale infrastructure environments. Drawing from practical engineering perspectives and systems-level project management principles, the paper explores how structured decision frameworks improve project reliability by linking risk evaluation with actionable operational choices. The study further investigates the role of continuous feedback mechanisms, adaptability, and stakeholder alignment in reducing uncertainty across complex project environments involving multiple technical, logistical, and institutional dependencies. The paper ultimately proposes that risk-informed delivery represents a broader transformation in megaproject management, shifting risk from a passive reporting function toward an active operational driver guiding decision-making under uncertainty.
Oguz Kahraman (Tue,) studied this question.