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Seismic Hazard Estimation in Canada and its Contribution to the Canadian Building Code Implications for Code Development in Countries such as Australia

2010· article· en· W2275344050 on OpenAlexafffundabout
J Adams

Bibliographic record

VenueAustralian Journal of Structural Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsGeological Survey of Canada
FundersNational Research Council Canada
KeywordsBuilding codeSeismic hazardSeismic riskInduced seismicityCode (set theory)HazardEngineeringEstimationProcess (computing)Risk analysis (engineering)Civil engineeringComputer scienceBusinessSystems engineering

Abstract

fetched live from OpenAlex

Seismic design provisions of national building codes aim to save lives and reduce losses from future earthquakes. The provisions need to be based on reliable seismic hazard maps, the generation of which is a challenge in low-seismicity regions such as eastern Canada and Australia, and which contain inherently-large uncertainties. A process is needed to incorporate the hazard values into design provisions, and this is best done through continual code improvements occurring within an on-going national code committee. Building codes need to balance the benefits against the costs, and so the improvements are aided by crude risk assessments (to focus the effort where the risk is greatest) together with crude cost-benefit analyses. Most codes become more stringent to match evolving societal goals, and while the cost of increased code requirements may be strongly resisted by some groups, they may also be economically justified (present cost versus future loss). The seismic provisions of national building codes tend to focus on new, engineered “large” buildings but may not provide comparable benefits to new “small” buildings and are unlikely to reduce risk in existing buildings, even though damage to these may represent the major loss in moderate-magnitude urban earthquakes like the 1989 Newcastle earthquake. Additional and different strategies are therefore needed to complement existing code activities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.250
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2010
Admission routes3
Has abstractyes

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