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Record W2158257514 · doi:10.1520/stp14763s

Variations in Backcalculated Pavement Layer Moduli in LTPP Seasonal Monitoring Sites

2000· book-chapter· en· W2158257514 on OpenAlexaboutno aff
RC Briggs, EO Lukanen

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

Seasonal variations in structural parameters were calculated from Falling Weight Deflectometer data on 25 LTPP flexible seasonal monitoring sections. In all, 23,976 deflection basins were analyzed. The deflection basins were collected monthly between the fall of 1993 and the spring of 1995. The test locations were distributed around North America from Saskatchewan to South Texas and from Idaho to Maine. The sections all consisted of asphalt surfacing and granular bases (and some granular subbases) over subgrades. Asphalt thicknesses ranged from 46 to 277#mm. Seasonal variations observed on selected sections in this database and documented in this paper include variations in asphaltic materials with temperature, variations in moduli of the unbound materials with precipitation, and variations in moduli of unbound materials due to freeze/thaw. For moderately thick to thick asphalt sections (>125 mm) the temperature dependency can clearly be seen in the deflection basins. As expected, this variation in deflections, and AC modulus is sinusoidal over the year. The amplitude of the sinusoidal variations were found to be heavily influenced by the thickness of the AC layer. Thicker AC sections exhibited higher sinusoidal variations. Base and subgrade values tended to be influenced by temperature but remained more consistent throughout the year except in cases where freeze̸thaw effects were seen. During freeze conditions, the moduli of the unbound (base and subgrade) layers drastically increased.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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.

Study designSimulation or modeling
Domainnot available
GenreOther

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

Citations5
Published2000
Admission routes1
Has abstractyes

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