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Record W1975505422 · doi:10.1061/41167(398)25

Preserving Our Airfield Pavements

2011· article· en· W1975505422 on OpenAlexaff
David Hein, Brian Aho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsPreventive maintenancePrioritizationIdentification (biology)Planned maintenanceTransport engineeringRisk analysis (engineering)EngineeringOperations managementBusinessReliability engineering

Abstract

fetched live from OpenAlex

The need to preserve our airfield pavement infrastructure is paramount to insuring the viability of transportation of people and goods. Preventive maintenance plays an important role in the preservation of airfield pavement infrastructure. A successful preventive maintenance program cannot function without the support of many features associated with pavement management systems (e.g., pavement inventory, condition assessment, and the framework for the identification and prioritization of pavement preservation treatments). The purpose of a preventive maintenance treatment is to prevent premature deterioration of the pavement, retard the progression of pavement defects, and cost-effectively extend the life of the pavement. The objective is to slow down the rate of pavement deterioration and effectively increase the useful life of the pavement. A preventive maintenance treatment is not determined by the type of treatment, but by the reason why the treatment is performed. For cost-effective preventive maintenance it is necessary to apply the right treatment to the right pavement at the right time. The objective is to identify the sections that would benefit most from preventive maintenance (the right pavement), do the identification and apply the treatment in a timely manner (the right time) and to select the most beneficial treatment (the right treatment). The effectiveness of preventive maintenance is largely dependent on the timing of maintenance activities. To ensure that funding for preventive maintenance is available when required, many practitioners advocate the establishment of adequate dedicated funds for preventive maintenance. The development and implementation of a preventive maintenance program should be done in a collaborative manner, and should be supported by training and educational activities. To succeed, a preventive maintenance program requires a long-term commitment, ongoing improvements, and the documentation and reporting of program benefits. This paper outlines 7 basic steps involved in developing and implementing a pavement preservation program for airport pavements and provides examples of decision matrices and life-cycle cost analysis procedures to apply and evaluate the success of pavement preservation treatments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.240

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 head0.022
GPT teacher head0.194
Teacher spread0.172 · 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 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

Citations3
Published2011
Admission routes1
Has abstractyes

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