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Record W2884666117 · doi:10.1016/j.sandf.2018.06.003

Characterizing cyclic and static moduli and strength of compacted pavement subgrade soils considering moisture variation

2018· article· en· W2884666117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSOILS AND FOUNDATIONS · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of Ottawa
FundersMinistère des Transports
KeywordsSubgradeGeotechnical engineeringSoil waterCompactionWater contentCompressive strengthModulusMoistureMaterials scienceEnvironmental scienceGeologySoil scienceComposite material

Abstract

fetched live from OpenAlex

Compacted soils are widely used as the subgrade layer for pavements. Knowledge of the mechanical properties of subgrade soils under cyclic and static loading conditions and their variation under the influence of environmental factors is required for the rational design of pavements based on mechanistic methods. This paper presents an experimental investigation of the cyclic and static moduli and the strength properties of seven different compacted Canadian subgrade soils considering the variation in the post-compaction moisture content. Cyclic triaxial tests were performed to reliably determine the resilient modulus (MR). Unconfined compression tests, which allow an unloading-reloading loop at 1% strain, were performed to determine the deviator stress (Su1%) at 1% strain, the reloading elastic modulus (E1%) at 1% strain and the unconfined compressive strength (qu) at failure. The physical properties, the chemical and mineralogical compositions, and the soil-water characteristics of these soils were also determined. Relationships were developed to predict the MR from the Su1%, E1%, qu and soil physical properties for the investigated subgrade soils because the experimental determination of MR is both expensive and time-consuming. The studies presented in this paper provide useful information and approaches that can be used to promote the implementation of mechanistic pavement design methods using simple techniques.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.458

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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