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Record W2951236813 · doi:10.1139/cjce-2019-0152

Accuracy evaluation of statically backcalculated layer properties of asphalt pavements from falling weight deflectometer data

2019· article· en· W2951236813 on OpenAlexvenueno aff
Guozhi Fu, Cheng Xue, Yanqing Zhao, Dandan Cao, Mohsen Alae

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsFalling weight deflectometerDeflection (physics)Structural engineeringGeotechnical engineeringAsphaltAsphalt concreteDynamic loadingModulusEngineeringMaterials scienceSubgradeComposite materialPhysics

Abstract

fetched live from OpenAlex

This study is to evaluate the dynamic effects of falling weight deflectometer (FWD) loading on the surface deflection of asphalt pavement and the accuracy of statically backcalculated layer moduli from FWD data. The dynamic and static deflections were computed using the spectral element method and the layer elastic theory, respectively, for various pavement structures. The static deflection is considerably larger than the dynamic deflection for typical FWD loading and the normalized difference between static and dynamic deflections increases with increasing distance from the load center and decreases with increasing loading duration. The dynamic deflections were utilized to backcalculate the layer moduli using two static backcalculation procedures, MODULUS and EVERCALC. The backcalculated moduli can be significantly different from the actual moduli. The results indicate that the static backcalculation procedure can lead to significant errors in the backcalculated layer moduli by ignoring the dynamic effects of FWD loading.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.265
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations16
Published2019
Admission routes1
Has abstractyes

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