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Record W2972888533

Using AASHTOWare Pavement ME Design Tools to Evaluate Flood Impact on Concrete Pavement Performance

2019· dissertation· en· W2972888533 on OpenAlexaboutno aff
Oluremi Oyediji

Bibliographic record

VenueUWSpace (University of Waterloo) · 2019
Typedissertation
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringCivil engineeringFlood mythPavement engineeringGeotechnical engineeringGeographyCartographyAsphaltArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The resilience of concrete pavement to flood impact has remained positive based on previous experimental investigations and overtime recommended as a pre-flood adaptation strategy in countries such as Australia and the United States. However, no study on concrete pavement flood impact performance has been conducted in Canada until now. Flood impact assessment under Canadian climate conditions was therefore conducted on typical concrete pavement designs common to the provinces of Ontario and Manitoba. \nIn the Ontario study, representative arterial and collector pavement designs were modelled, and cycles of flood hazards simulated on these pavements to evaluate changes in performance under climate change scenarios using the AASHTO Pavement ME Design (PMED) program. Percentage damage was estimated by observing changes in International Roughness Index (IRI) prediction values under flood and no-flood conditions. Results indicate a slight reduction in pavement performance across road classes, and minimal increases in damage as event cycles increased. Estimated flood damage on pavement performance was more pronounced in collector (non-dowelled) pavements than arterial (dowelled) pavements. The major distress indicator which contributed to damage was faulting, being that it increased across event cycles irrespective of return periods. \nIn the Manitoba case study, a total of 27 pavement design classes was developed based on a matrix of representative traffic levels, subgrade conditions and slab thicknesses common to the province. Projected climate-induced flood hazards under climate change scenarios were further modelled on the design classes to evaluate flood impact on concrete pavement performance. Results also indicated diminutive flood damage and loss of life in all of the concrete pavement classes. Increases in flood cycles induced no further damage or loss in pavement performance. In all of the pavement classes considered, there was no positive change or damage to faulting and fatigue cracking under flood conditions. The IRI parameter was the only parameter influenced by inundation, which could further suggest the possible build-up of permanent moisture-induced warping. The observed low flood damage ratios further reiterates the resilience and adaptive capacity of the Jointed Plain Concrete Pavement (JPCP) to withstand extreme precipitation or flood conditions. \nA local calibration of the AASHTOWare Pavement ME Transverse Cracking Transfer Function was successfully completed to fit observed concrete pavement performance in Ontario. As bias existed in cracking predictions using default AASHTOWare Pavement ME cracking calibration coefficients, a need for local calibration was pertinent to provide better predictions of cracking performance under Ontario conditions. This achievement is pivotal to the delivery of reliable and economical pavement design and construction projects across the province. The derived local calibration factors have been accepted and published by the Ministry of Transportation Ontario (MTO) for industry use.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.232
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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