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Record W6991983832

Investigation into Asphalt Concrete Material and Volumetric Properties that Promote Moisture Damage

2013· dissertation· en· W6991983832 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2013
Typedissertation
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltMoistureCohesion (chemistry)DurabilityAsphalt concreteAggregate (composite)Volume (thermodynamics)Asphalt pavement
DOInot available

Abstract

fetched live from OpenAlex

The research presented in this thesis: (1) quantifies and qualifies the Surface Free Energy (SFE) of neat and Liquid Anti-Strip (LAS) modified asphalt binders (binder); and (2) identifies volumetric mix properties that inhibit or assist in the susceptibility of Hot Mix Asphalt (HMA) to moisture damage based on time dependent phenomenological mechanical responses. These two research elements provide insight into the physical, chemical, mechanical and volumetric mix properties that inhibit or facilitate moisture damage in HMA. Moisture damage is a mechanism that causes distress and failure in asphalt concrete (AC) pavements due to a loss of durability resulting from the presence of moisture, in the form of a vapour or liquid, originating internally or externally. This reduces the pavements performance by promoting distresses such as: longitudinal cracking, spalling, rutting, shoving, stripping and ravelling. When moisture originates or is introduced in the AC a weakening of adhesion and cohesion of the material occurs, due in part to: binder properties, aggregate properties, volumetric mix properties, environmental conditions, traffic volume and loads, pavement design and construction practices. The research performed was split in two parts. The first part consisted of conducting SFE measurements on two PG 58-34 binders with different sources. One binder was modified with a LAS agent at concentrations of 0.5%, 2.0% and 5.0% by mass of binder and the other binder was kept neat. The neat and LAS modified binders were subjected to short-term aging by oxidation and then tested with a goniometer to determine their SFE and wettability. The SFE measurements revealed that an LAS concentration of 0.5% maximizes: (1) the work of adhesion of an unaged and aged binder, and (2) the ability of the binder to repel water. Furthermore, the process of aging increases the hydrophobicity or tendency of the binder to repel water regardless of the LAS concentration. Hence, an LAS concentration of 0.5% minimizes the potential for moisture damage in HMA. The second part of the research consisted of investigating the potential for moisture damage of seven bituminous type B (Bit B) and eight bituminous type C (Bit C) mix specified by Manitoba Infrastructure and Transportation. Laboratory testing of the resilient modulus and creep compliance was conducted to determine the fundamental mechanical response of the material. The resilient modulus and creep compliance test program were conducted on samples before and after moisture conditioning. As a result of the testing program, it was observed that the susceptibility of AC to moisture damage based on volumetric mix properties can be dependent on the air voids ratio, aggregate gradation and binder content of the mix.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.187
Teacher spread0.169 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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