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Record W4405462869 · doi:10.1061/9780784485804.ch3

Impact of Climate Change on Infrastructure Performance

2024· book-chapter· en· W4405462869 on OpenAlexaboutno aff
Bruce R. Ellingwood, Paolo Bocchini, Zoubir Lounis, Michel Ghosn, Ming Liu, David Y. Yang, Luca Capacci, Sofia Maria Carrato Diniz, Ning Lin, George C. Tsiatas, Fabio Biondini, John van de Lindt, Dan M. Frangopol, Mitsuyoshi Akiyama, Yue Li, Michele Barbato, Hanping Hong, Therese P. McAllister, Γεώργιος Τσάμπρας, Farshid Vahedifard

Bibliographic record

VenueAmerican Society of Civil Engineers eBooks · 2024
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeEnvironmental scienceBusinessGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

This chapter addresses the challenges posed by climate change in structural engineering practice to advance the performance of infrastructure systems through guidelines for recommended code and standard provisions and for best engineering practices for design, rehabilitation and risk management of future climate extremes. Many climate effects in infrastructure systems, such as wildland-urban interface fires, and pluvial flooding are better addressed through zoning or land use regulations than through structural engineering practice. The chapter draws upon recent activities in the United States as well as in Canada under the Climate-Resilient Buildings and Core Public Infrastructure Initiative to develop future climate-based design criteria for the National Building Code of Canada and the Canadian Highway Bridge Design Code . It deals with climate change impacts on community resilience, a topic of urgency in light of increasing losses to infrastructure systems as a result of extreme climate hazards with large geographic footprints and the issuance of PPD-21.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.236
Teacher spread0.222 · 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.

Study designOther design
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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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