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Record W3164111086 · doi:10.82308/6530

An evaluation of selected asphalt pavements in the City of Montreal /

2006· article· en· W3164111086 on OpenAlexaboutno aff
Joseph G. Feghali

Bibliographic record

VenueeScholarship@McGill (McGill) · 2006
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltEnvironmental scienceGeographyCartography

Abstract

fetched live from OpenAlex

Pavements are generally analyzed as elastic layered structures, and designed empirically. Currently, more than 25% of road pavements in Canada fail prematurely (i.e. shortly after they are constructed) whereas only 60% reach their actual design life (C-SHRP, 2002). Pavement failures are generally displayed at the top of the asphalt layer. These deteriorations rarely initiate at the surface as the different layers interact co-dependently in resisting the applied loads. The failure of any one of them will weaken the pavement and reflect at the surface as deteriorations. The City of Montreal is experiencing extensive premature pavement failures mostly in the form of potholes and fatigue cracks which have been treated and repaired using different surface restoration techniques. Unfortunately, these solutions do not address the real problem and are only temporary. This research reviews the pavement repair and maintenance techniques used in the City of Montreal, explores their adequacy, and demonstrates how the current design procedures have yielded weak pavements that are short on thickness and proper drainage. The objective of this thesis is also to determine the main reason why Montreal road pavements fail so prematurely in terms of the current design, repair and maintenance practices. Furthermore, this research project includes a case study of the road pavement condition in the City of Montreal, coupled with a comparative approach to the standards of practice used in both of Quebec and Ontario provinces.

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 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)0.004
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.023
GPT teacher head0.256
Teacher spread0.232 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicAsphalt Pavement Performance EvaluationFrench-language works237,207