A multi-hazard framework for resilient service and functional recovery in sustainable multi-building facilities
Bibliographic record
Abstract
: Enhancing the resilience of multi-building facilities under natural hazards necessitates a detailed understanding of the interdependencies between building systems and critical operational functions. This study proposes a systematic and adaptable framework to model these service-function interdependencies, aiming to support continuity planning and optimize post-disruption recovery. The framework combines a hierarchical dependency structure, stakeholder-informed mapping, and a simulation-based recovery model, tailored for facilities exposed to seismic and flood risks. The methodology was applied to a three-building facility in Montreal, Canada. Simulation results demonstrated that flood-induced disruptions produced recovery durations approximately 300% longer than those caused by earthquakes. Access systems showed the most extensive delays, requiring up to 2,545 days for restoration after floods, in contrast to 500 days following seismic events. Similar delays were observed across power, telecommunications, and other critical services under flood conditions. Function-specific recovery profiles showed a notable divergence between hazard types. After earthquakes, essential operations such as Maintenance, Shipping and Receiving, and Marine Services retained 91–92% functionality, while areas like Manufacturing and Environmental Compliance retained 82%. Conversely, floods caused sharper initial impairments, with Quality Control Tests retaining 50% of operational capacity and Marine Operations, Shipping, and Maintenance functions falling to approximately 5%. Despite the greater initial severity, flood-related recoveries followed a more uniform timeline, with most functions restored within 1,000 days. These findings underscore the need for hazard-specific mitigation strategies that reflect both the nature and duration of disruption. The framework provides a scalable decision-support tool to inform service prioritization, retrofitting investments, and long-term facility management in multi-hazard contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".