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Enregistrement W6947766220 · doi:10.4224/40002045

Coastal flood risk assessment guidelines for building and infrastructure design: supporting flood resilience on Canada's coasts

2020· report· en· W6947766220 sur OpenAlexafffundvenueabout

Notice bibliographique

RevueNPARC · 2020
Typereport
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCell Image Analysis Techniques
Établissements canadiensNational Research Council CanadaGovernment of Canada
Organismes subventionnairesNatural Resources Canada
Mots-clésFlood mythResilience (materials science)Coastal floodHazardPopulationRisk assessmentNatural hazardFlood risk assessmentCoastal hazards

Résumé

récupéré en direct d'OpenAlex

More than 15 million people live within 20 km of Canada’s marine and Great Lakes coasts. The buildings and infrastructure that they rely on are vulnerable to coastal flood hazards resulting from extreme water levels, waves, tsunamis and other contributing factors. The risks associated with coastal flood hazards are escalating over time, due to development and population growth in coastal zones, and climate-driven effects, such as global sea-level rise. These growing concerns are prompting a broad re-think of how coastal flood risks can be better managed in Canada, including how building and infrastructure design practice can be enhanced to support resilience objectives. These guidelines apply to coastal flood risk assessments for building and infrastructure design (including retrofit design) applications in Canada. The document is intended to inform, and provide a technical reference for, a wide variety of users interested in building and infrastructure design in areas potentially exposed to coastal flood hazards under present-day and/or future conditions. The guidelines advocate a move toward risk-based approaches to analysis and design for flood resilience, and identify the following: • Key concepts and terminology relevant to understanding and performing coastal flood risk assessments to support building and infrastructure design. • A possible framework and methodology for conducting coastal flood hazard and risk assessments to inform the design and rehabilitation of buildings and infrastructure in areas potentially exposed to coastal flood hazards. • The different levels or tiers of analysis that can be used as the basis for risk assessments, and the circumstances in which they should be applied. • Suggestions for effective stakeholder, partner and public engagement in the coastal flood risk assessment process. • Recommendations for establishing risk-based design criteria for buildings and infrastructure. • Data requirements, and sources of data and information, to support coastal flood risk assessments. • Methodologies and key considerations for assessing coastal flood hazards. • The role of building and infrastructure design practice within the portfolio of tools and strategies available to address coastal flood risks in a changing climate. Though not the focus of the guidelines, some background information on strategic approaches to coastal flood risk management is provided. This information is provided to illustrate how the information derived from a coastal flood risk assessment can be used to support building and infrastructure design within the broader context of managing, mitigating and adapting to flood risks. Efforts have also been made to identify gaps and future needs to support coastal flood risk assessments for design applications, which include: • Expanded hazard datasets (including enhanced spatial and temporal coverage of water level, wave and other parameter measurements). • Improved vulnerability-hazard function datasets, which would enable proper consideration in risk assessments of (i) the benefits and performance of difference structural features, materials, and construction techniques; and (ii) regional differences in hazards and construction types. • New provisions in building codes and infrastructure design standards to address flood resilience objectives, and to enable integration of flood (and eventually multi-hazard) risk management practices with codes and standards.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,561
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,351
Écart entre enseignants0,325 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission4
Résumé présentoui

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