Resilient infrastructure management systems using real-time analytics and AI-driven disaster preparedness protocols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it