Engineering Assurance for AI-Enabled Self-Healing Infrastructure: From Damage Signals to Verified Recovery

Artificial intelligence, embedded sensing, digital twins and responsive materials are increasingly being explored as ways to create infrastructure that can detect and respond to damage. A warning message or a crack that closes doesn’t prove that the structure has regained its required performance. This review looks at self-healing infrastructure from an engineering-assurance perspective: what needs to be measured what can be inferred, which actions are permissible what needs to be fixed and what needs to be tested before a system can be called fixed. Using the review source and its references the paper separates sensing confidence, diagnosis confidence repair feasibility and recovery evidence as separate considerations. It looks at embedded -electromechanical sensors, fiber-optic and piezoelectric sensing, edge analytics, digital-twin state estimation, AI-based prediction and material-level healing methods. A workflow is suggested that connects a detected problem to an action and then to checks done after the repair. The study finds challenges including outside interference sensors changing over time models not working well in different places repairs not reaching far enough actions that can’t be undone and the difference between a material fixing itself and the whole system working again. The paper ends by saying that safe self-healing should be seen as a claim that needs proof, clear rules and accountable human oversight, in charge—not as something automated.