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Enregistrement W3003889835

Pavement Flooding Risk Assessment and Management in the Changing Climate

2020· dissertation· en· W3003889835 sur OpenAlexfundaboutno aff
Donghui Lu

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2020
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueFlood Risk Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
Mots-clésFlooding (psychology)Environmental scienceRisk assessmentClimate changeEnvironmental resource managementEnvironmental planningWater resource managementEngineeringGeographyComputer scienceGeologyPsychologyComputer security
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Flooding, which can cause substantial infrastructure damages resulting in adverse social, environmental, and economical consequences, is a rising concern in the changing climate. Road networks consisting of interconnected links designed to accommodate transportation needs of the public and can be affected by flood hazards. In road pavement design and management, historical climate design data are becoming less representative of the future climate resulting in unexpected risks. Road pavement damage caused by expected intensification of flood events under climate change can lead to safety, mobility, comfort, functionality, and accessibility concerns. In order to mitigate the risk of flooding on pavements, this research develops risk quantification methodology and implementation guidelines, which enable informed pavement management and adaptation leading to increased resilience of pavement networks in the changing climate. 
\nThe risk assessment methodology includes project level risk assessment and network level risk assessment. The key components of project level risk assessment include flood hazard assessment, flooded pavement performance analysis, quantification of pavement fragility, and consequence analysis. The network level risk assessment is an extension of the project level risk assessment. It involves an eight-step approach including mapping the flood hazards, mapping the road exposure and characteristics, matching fragility models, calculating risk for a range of events, and summing up the risks. The risk estimation can be used to inform and initiate the adaptation planning and programming at the prioritized sections of pavement networks. Case studies have been conducted to illustrate the implementation of the risk assessment methodology. Based on the research findings, pavement flooding risk assessment and management implementation guidelines and procedures are developed. The outcome of the research helps the advancement of pavement design and management practices for addressing flood hazards.
\nThe results in the flood hazard analysis indicate that the probabilistic flood hazard analysis method provides a quantitative estimation of flood hazard for various climate change scenarios. Road pavement infrastructure can be subjected to more frequent and intense extreme precipitation events causing more pavement flooding in the case study area. The new extremes should be incorporated in pavement design and management practices. Regarding pavement damage, a comprehensive analysis summarizes the pavement damage processes, causes, components, damage patterns, impact factors, and temporal and spatial characteristics. Probabilistic pavement flooding damage analysis is illustrated by fragility models, which provide estimations of conditional probability of exceeding certain pavement damage given a flood event. Pavement mechanistic-empirical (ME) design method is utilized to simulate the impact of extreme precipitations on pavement performance of typical arterial and collector flexible pavements in Toronto, Canada. Fragility models and curves are generated based on the performance simulation results. In the case study, the pavement roughness degradation is accelerated post-flooding during the life cycle, which is assessed as the jump & delayed effect damage pattern. The extreme events can lead to the loss of pavement life up to 303 days, approximately more than 4% of a pavement’s life. More flood cycles lead to shorter pavement life, which is caused by the accelerated deterioration after the flood cycles. The increase of precipitation levels under climate change increases the probability of pavement damage in each damage state for different designs. The incorporation of ME performance simulation and experimental testing allows obtaining the damage data from aged pavements for fragility analysis. 
\nThe quantitative pavement flooding risk assessment at the project level integrates the findings of the flood hazard analysis, fragility, and vulnerability. Considering the climate from 2017 to 2100, the extreme precipitations from representative concentration pathway (RCP) 8.5 climate scenario results in asset value losses as high as CAD$112,471 and CAD$46,487 per kilometer for arterial and collector pavements, respectively for moderate damage. The risk of major damage is not the highest when compared to the risks of minor and moderate damage, which is because the major damage has a lower occurrence resulting in lower asset value losses in the case study. The network spatial risks are analyzed and visualized through risk mapping. The results indicate the length of flooded pavements for each functional class increases as the magnitude of flooding increases. As the damage state threshold value increases, the percentage of road sections with high risk decreases and that with low risk increases. The risk of climate-change-induced flooding is sensitive to the range of flood events included in the risk assessment. When include the climate change scenario in a full range of flood hazard, the percentage of road network with low risk is increased from 12.1% to 45.7%, and the percentage of high-risk sections is increased from 46.0% to 79.9% for pavement damage over 1.5%.
\n Adaptation strategies that have been established are reducing hazard exposure, reducing fragility of pavement structures, and reducing the cost of certain damage. The implementation guidelines are introduced according to the time horizon: pre-event, during-event, and post-event. Pre-event, probabilistic risk assessment and risk matrix approach are both included in the risk assessment guide. The general principles, key activities, and procedures introduced in the guide enable researchers, practitioners, and stakeholders to apply the research findings.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,176
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
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 Opus0,008
Tête enseignante GPT0,217
Écart entre enseignants0,208 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

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

En bref

Citations13
Publié2020
Routes d'admission2
Résumé présentoui

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