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Record W6966946410 · doi:10.4224/40002986

Flood damage to critical infrastructure

2022· report· en· W6966946410 on OpenAlexafffundvenueabout

Bibliographic record

VenueNPARC · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council CanadaGovernment of Canada
FundersNational Research Council CanadaNatural Resources CanadaEnvironment and Climate Change CanadaPublic Safety Canada
KeywordsCritical infrastructureDamagesFlood mythResilience (materials science)Vulnerability (computing)HazardWork (physics)Natural hazardClimate change

Abstract

fetched live from OpenAlex

This report provides a review of the tools and metrics available for flood hazard mappers, planners, engineers and scientists for describing the vulnerability of critical infrastructure in Canada when exposed to coastal and riverine flooding. This research was undertaken for Public Safety Canada and in collaboration with Natural Resources Canada (NRCan) and Environment and Climate Change Canada (ECCC). Consultation with these research partners identified a need for more science-based tools and methods to estimate the resistance and resilience of critical infrastructure to flood hazards, beyond post flood-damage inspection surveys. The critical infrastructure that has been identified for this work includes roads, rail, pipelines, telecommunication lines, water supplies, sewage treatment facilities, fire stations, hospitals, police stations, emergency medical services (EMS), electricity production and distribution, other transportation infrastructure, hotels, schools and community centres. The greatest amount of available information on methods for assessing flood damage pertains to buildings. Canadian stage-damage functions exist for the structure and contents of Canadian buildings identified as critical infrastructure. Further information is also available from sources within the United States that could allow for the incorporation of waves and currents into the building stage-damage functions. No North American-based stage-damage functions were found for predicting flood damages to road and rail in the available literature. Stage-damage functions have been provided based on data from Asia, which has been found to be similar to data from Europe. However, these available empirical stage-damage functions are based on post-flood surveys and therefore are inherently specific to local conditions and infrastructure design/construction methods, which may not reflect Canadian settings. The functions are broad, they do not account for the different types of infrastructure which make up these systems such as tunnels and bridges where most flood damage occurs, they are simply based on length of road or railway. For other types of critical infrastructure, stage-damage functions were found in studies from the United States. Those functions were predominantly based on identifying the height of critical components susceptible to damage from flooding. As such, these functions incorporated an element of subjectivity and lack validation in either controlled or uncontrolled environments. No stage-damage functions were found for communications systems. The elements that comprise communications systems have been identified and most elements could be assessed in a manner similar to the other infrastructure systems provided in this report. One exception is utility poles. Utility poles were not identified as critical elements in either electrical or communications systems of existing stage-damage functions and these features were highlighted by our project partners as one of the gaps in current flood hazard mapping and planning. Recently published work should provide the theoretical basis for the development of analytical stage-damage functions for utility poles exposed to flooding. Recommendations are made for potential future research directions to add data-driven methods for estimating damages to critical infrastructure which are exposed to flood hazards in an effort to Flood Damage to Critical Infrastructure improve and add resolution to existing stage-damage functions, as well as fill any gaps by developing new stage-damage functions for critical infrastructure in Canada.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.027
GPT teacher head0.335
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes4
Has abstractyes

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