Resilient infrastructure management systems using real-time analytics and AI-driven disaster preparedness protocols
Bibliographic record
Abstract
This review explores the convergence of real-time analytics and artificial intelligence (AI) in strengthening resilient infrastructure management systems, particularly for disaster preparedness and response. As climate change and urbanization amplify infrastructure vulnerability, cities and critical systems require intelligent frameworks capable of anticipating, adapting to, and recovering from disruptions. The paper outlines how AI-powered data streams from sensors, digital twins, and geospatial platforms are transforming static infrastructure into self-monitoring, self-correcting networks. It discusses predictive models for hazard forecasting, risk detection, and automated decision-making protocols during emergencies. Emphasis is placed on early warning systems, dynamic resource allocation, and post-event impact analysis, all supported by AI and real-time dashboards. Use cases across transportation, energy, water, and healthcare systems are examined to illustrate the role of integrated AI in building infrastructure resilience. The paper concludes with a call for ethical AI governance, interoperable systems, and cross-sector collaboration to enable sustainable, intelligent infrastructure preparedness. Keywords: Resilient Infrastructure, Real-Time Analytics, AI-Driven Disaster Preparedness, Risk Forecasting, Critical Infrastructure Management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".