MétaCan
Menu
Back to cohort
Record W2085636494 · doi:10.3141/2205-29

Direct Measurement of the Impact of Heavy Loads on Thin Membrane Pavements

2011· article· en· W2085636494 on OpenAlexafffundabout
Lynne Cowe Falls, Linda Clary, A'arif Hamad, Ahmed M. H. Abdelfattah

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2011
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsCanadian Pacific Railway (Canada)Suncor Energy (Canada)University of Calgary
FundersSuncor Energy Incorporated
KeywordsSubgradeDeflection (physics)AsphaltAxleGeotechnical engineeringAxle loadWearing courseRoad surfaceEngineeringAsphalt pavementEnvironmental scienceStructural engineeringMaterials scienceCivil engineeringComposite material

Abstract

fetched live from OpenAlex

This paper presents the results of a field study of the loads imposed by heavy oilfield cranes (with hydraulic suspensions and super single tires) on thin membrane asphalt pavements in Alberta, Canada. Three 150-m test road sections (thin asphalt wearing course, bituminous surface treatment, and granular surface) were built and instrumented for strain at the bottom of the asphalt layer, surface deflection, and subgrade pressures. Temperature and moisture profiles were also measured. Field testing involved controlled speed experiments of standard axle configurations and heavy-axle (12,000-kg) vehicles with and without hydraulic suspensions. Focusing on the hot-mix asphalt section, this paper presents a description of the test road design, instrumentation, and testing plan, followed by some results and findings from two seasons (spring and fall 2005) of testing. Vertical stress in the subgrade, longitudinal interfacial strain, and surface deflection are compared for three vehicle types used in the test. Results from tests show that subgrade stress and interfacial strain are very similar for the standard axle configuration during spring compared with those of the cranes without the dolly during the fall season. It could be argued that on the basis of the pavement response, the cranes could operate during the winter season without the dolly (and thereby increase road safety by removing a long combination vehicle from the traffic stream) without causing substantial long-term deterioration.

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)0.008
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.165
GPT teacher head0.377
Teacher spread0.212 · 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 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

Citations1
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207