MétaCan
Menu
Back to cohort
Record W2285571355 · doi:10.1520/stp104432

Proactive Pavement Smoothness from the Base Upward

2012· book-chapter· en· W2285571355 on OpenAlexaboutno aff
Stephen N. Goodman, Rhiannon Arsenault, Tom Dziedziejko

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsSmoothnessBase (topology)Computer scienceGeologyMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

The importance of high initial smoothness on newly constructed or rehabilitated pavement facilities has been identified by many researchers and recognized by many road agencies. Pavements with high initial smoothness display retarded roughness progression and, therefore, longer service lives. In Ontario, Canada, the Ministry of Transportation (MTO) implemented a bonus/penalty system for smoothness in the mid-1990s. Initially based on the profile index and scallops (i.e., bumps) measured with a California Profilograph, the specification is now based on the International Roughness Index analyzed locally (for bumps) and over regular intervals. The potential bonus for high initial smoothness in Ontario is considerable, as is the potential penalty for excessive roughness. Furthermore, the ability to correct areas of excessive roughness after placement is greatly limited by the MTO because the public does not wish to observe a new pavement surface being “damaged” by a milling machine. Given such, contractors are proactively requesting smoothness testing on base pavement layers so that areas of excessive roughness can be addressed prior to the placement of the surface layer. However, once the smoothness of the base layer is determined, the contractor must decide whether areas of excessive roughness will be sufficiently smoothed by the surface hot mix asphalt (HMA) layer or whether milling is first required (at additional cost). In addition to a brief overview of the Ontario smoothness specification, this paper presents the change in pavement smoothness with increasing HMA layers measured at a select project location in order to identify how much additional smoothing a contractor should anticipate. Of particular interest is the change in smoothness from an in-place recycled layer to a base HMA layer and, finally, a surface HMA layer. The results will allow contractors to optimize pavement smoothness (and their associated bonus) while minimizing costly corrective milling.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.224
Teacher spread0.189 · 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
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicAsphalt Pavement Performance EvaluationFrench-language works237,207