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Record W2147281107 · doi:10.1061/9780784413005.049

Pavement Rehabilitation of Runway 14/32 at Zürich International Airport: Service Life Prediction Based on Updated Incremental Damage Approach

2013· article· en· W2147281107 on OpenAlexfundno aff
Carlo Rabaiotti, M. Amstad, Marco Schnyder

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsRunwayService lifeSubgradeOverlayPavement engineeringPavement managementDeflection (physics)Structural engineeringAsphalt concreteAsphaltAsphalt pavementEngineeringFinite element methodBase courseGeotechnical engineeringCivil engineeringComputer scienceMaterials scienceMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Zurich International Airport operates with three runways: 16/34, 14/32, and 10/28. The pavement of runway 14/32 has reached its end of service life and should be rehabilitated soon. The old pavement structure consists of concrete slabs on a cement treated base. The 30-cm thick concrete pavement will be replaced with a 35-cm thick asphalt layer during rehabilitation. The service life of the proposed rehabilitation design has been analyzed adopting a new mechanistic empirical method based on the AASHTO 2008 design guide, although some improvements have been implemented: 1) three-dimensional finite element modeling (FE) with an elasto-plastic strain hardening material behavior model for the subgrade and 2) an iterative procedure that allows application of damage incrementally to asphalt and cement-treated material in an updated FE model. The method has been calibrated with results from deflection tests carried out in the 1970s on an asphalt test track and validated through deflection measurements carried out on runway 16/34, which was rehabilitated in 2008 with a similar asphalt overlay.

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 categoriesInsufficient payload (model declined to judge)
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 score0.063
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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.

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

Citations5
Published2013
Admission routes1
Has abstractyes

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