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Record W2949347941

Mechanistic-empirical failure prediction models for spring weight restricted flexible pavements in Manitoba using Manitoba and MnROAD instrumented test sites

2013· dissertation· en· W2949347941 on OpenAlexaboutno aff
Leonnie Kavanagh

Bibliographic record

VenueMspace (University of Manitoba) · 2013
Typedissertation
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersUniversity of Minnesota
KeywordsSpring (device)EngineeringForensic engineeringTest (biology)Geotechnical engineeringStructural engineeringGeology
DOInot available

Abstract

fetched live from OpenAlex

Pavement damage due to heavy loads on thaw weakened flexible pavements is a major concern for road agencies in Western Canada. To protect weaker, low volume roads, agencies impose spring weight restrictions (SWR) during the spring thaw to reduce pavement damage. While SWR may be cost effective for highway agencies, reducing the spring weight allowances can have a major impact on truck productivity and shipping costs. Therefore an improved process that links SWR loads to pavement damage, and based on limiting failure strain, is required. This thesis developed Local mechanistic-empirical damage models to predict fatigue and rutting failure on two spring weight restricted (SWR) flexible pavements in Manitoba. The Local damage models were used to assess the SWR loads that regulate commercial vehicle weights in Manitoba based on a limiting strain relationship between truck loads and damage. The Local damage models and a calibrated Finite Element Model (FEM) were used to predict the equivalent single axle load (ESAL) repetitions to fatigue and rutting failure at varying B-Train axle loads at the Manitoba sites. The Local model predictions were compared to predictions from the Asphalt Institute (AI) and Mechanistic Empirical Design Guide (MEPDG) damage models. The results of the analysis showed that for each 1% increase in load, there was a corresponding 1% increase in strain, and up to 3% decrease in ESAL repetitions to failure, depending on the Local, AI, or MEPDG damage models. The limiting failure strains, computed from the Local model for design ESALs of 100,000, were 483μm/m and 1,008μm/m for fatigue and rutting failure, respectively. For the Manitoba sites, the predicted FEM strains at B-Train normal and SWR loads were higher than the Local model limiting strains. Therefore the Manitoba ii SWR loads regulating B-Train operations on the two pavements during the spring period appeared to be reasonable. It is recommended that the research findings be verified with further calibration and validation of the Local damage model using a larger data set of low volume flexible pavements. A strain-based concept on how to manage the SWR regime in Manitoba based on the limiting strains was developed and presented.

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)0.001
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 score0.381
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.034
GPT teacher head0.235
Teacher spread0.201 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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