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Record W627114345

Case Studies to Determine the Relationship between Rehabilitation Process, Hot-Mix Asphalt Lift Thickness, and Pavement Smoothness

2011· article· en· W627114345 on OpenAlexaboutno aff
Salman A. Bhutta, R Essex

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsSmoothnessAsphaltLift (data mining)Asphalt pavementEngineeringForensic engineeringComputer scienceMathematicsCartographyGeography
DOInot available

Abstract

fetched live from OpenAlex

With the introduction of High Speed Inertial Profilograph (HSP), it has become possible to ascertain pavement smoothness accurately and instantaneously. HSP used for Highway smoothness testing in Ontario are ASTM Class 1 compliant, with capability to determining the smoothness of the pavement layer accurately and in a repeatable manner. The HSP can be operated at speeds of up to 80km/hr for prompt results with minimum interruption to traffic during smoothness testing. Users of the Profilograph devices have started utilizing the HSP as a tool to isolate operational issues from pre-existing conditions with the objective of achieving the desired contractual smoothness as a function of the rehabilitation process and lift(s) thickness of hot-mix asphalt. Case studies were undertaken on various highways in Ontario, that were undergoing Full Depth Reclamation (FDR), Cold-in-place Recycling (CIR), Expanded Asphalt Stabilization (cold in-place recycling with expanded asphalt, CIREAM) processes and conventional grind-and-pave projects. This paper outlines key lessons learned from the results of these studies, with special attention given to achievable smoothness end results as a function of the rehabilitation process. The paper also summarizes guidelines established as a result of this study to undertake profile-quality-control of various types of rehabilitation techniques outlined above. // Avec l'introduction du profilographe inertiel a haute vitesse inertielle (HSP), il est devenu possible de verifier l'uni de la chaussee avec precision et instantanement. Les HSP utilises pour tester l'uni des autoroutes en Ontario sont conformes a la classe 1 de l'ASTM, avec la capacite de mesurer l'uni de la couche de chaussee avec precision et d'une maniere reproductible. Le HSP peut fonctionner a des vitesses pouvant atteindre 80 km/h pour obtenir des resultats rapides avec une interruption minimale a la circulation pendant les essais de mesure de l'uni. Les utilisateurs des profilographes ont commence en utilisant le HSP comme un outil pour isoler les problemes operationnels de conditions preexistantes avec l'objectif d'atteindre l'uni contractuel desire en fonction procede de rehabilitation et lift(s) l'epaisseur des couches d'enrobe bitumineux a chaud. Des etudes de cas ont ete menees sur diverses routes en Ontario, qui etaient en cours de remise en etat complete a pleine profondeur (FDR), de recyclage a froid en place (CIR), de stabilisation au bitume expanse (recyclage a froid en place avec bitume expanse, CIREAM) procedes et projets de planage et pavage conventionnels. Ce document decrit les principales lecons apprises a partir des resultats de ces etudes, avec une attention particuliere aux resultats finaux d'uni realisables en fonction du procede de rehabilitation. L'article resume aussi les lignes directrices etablies comme resultat de cette etude pour entreprendre un controle de qualite du profil de divers types de techniques de rehabilitation decrites ci-dessus.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.287
Teacher spread0.231 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

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