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Record W3003889835

Pavement Flooding Risk Assessment and Management in the Changing Climate

2020· dissertation· en· W3003889835 on OpenAlex
Donghui Lu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsFlooding (psychology)Environmental scienceRisk assessmentClimate changeEnvironmental resource managementEnvironmental planningWater resource managementEngineeringGeographyComputer scienceGeologyPsychologyComputer security
DOInot available

Abstract

fetched live from 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.

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.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.008
GPT teacher head0.217
Teacher spread0.208 · 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