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Environmental effects on the application of spring load restrictions on low volume roads in Northern Ontario / by Jeffrey Chapin.

2017· dissertation· en· W7009528452 sur OpenAlexaboutno aff

Notice bibliographique

RevueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Langueen
DomaineEarth and Planetary Sciences
ThématiqueClimate change and permafrost
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCalibrationWork (physics)Spring (device)Volume (thermodynamics)Air temperature
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Many jurisdictions throughout Canada and the United States utilize Spring Load Restriction (SLRs) on low volume roads to minimize the damage during spring thaw. The main objective of this research is to develop a SLR method for use in northern Ontario (the Lakehead University (LU) method). In order to achieve this objective a detailed review and assessment was conducted on three potential SLR methods for their use on low volume roads in northern Ontario. The three methods assessed were an empirically based approach developed by the Minnesota Department of Transportation (M n/DOT), a semi-empirical approach developed by the University o f Waterloo and a thermal numerically based method using the finite element code TEMP/W.
\nEach of the methods was calibrated for two study sites, Highway 569 in northeastern Ontario and Highway 527 in northwestern Ontario. These methods were calibrated using historical data collected from these study sites including air temperature data and observed frost and thaw depths determined from thermistor measurements. The Highway 569 study site
\nwas calibrated fo r the 2005/2006, 2007/2008 and 2008/2009 seasons while the Highway 527 study site was calibrated for the 2008/2009 season only. The calibrated methods were then used to predict the application and removal dates for SLRs for the two sites for the 2009/2010 season.
\nPavement stiffness testing was conducted during the freezing and thawing seasons at the two sites using a Light W eight Deflectometer (LWD). The purpose of LWD testing was to examine the changes in pavement stiffness resulting from progressive freezing and thawing of the pavement structure. The results of the LWD testing indicate a significant decrease in pavement stiffness during pavement structure thawing between depths of 0.2 and 0.4 m. Based on these results and an extensive literature review, a 0.3 m threshold thawing depth was
\nselected to trigger SLR application. LWD testing also indicated a slight increase in pavement stiffness with in 2 weeks of complete pavement structure thawing. Using these results it was decided that, for this research, SLRs could be removed 7 days after complete pavement
\nstructure thawing.
\nDuring the calibration of the three SLR methods it was discovered that the Waterloo method requires significant adjustments to the frost and thaw depth algorithm coefficients at the onset of the thawing period. It was also determined that the accuracy of the thermal numerical modelling simulation is strongly associated with the boundary conditions used for the model.
\nThe assessment of the three methods indicates that the Mn/DOT method can closely predict the SLR application date (within 1 to 2 days) and was less accurate in predicting the SLR removal date (within 6 to 9 days). The Waterloo and TEMP/W methods did not display the same degree of accuracy as the M n/DOT method when used in a predictive mode.
\nBased on the LWD test results and the SLR calibration and prediction results, it was decided that the LU method should follow the M n/DOT method and use threshold Cumulative Thawing Index (CTI) values representative o f northern Ontario conditions as a trigger for application of the SLRs. In this method air temperatures are adjusted by reference
\ntemperatures which are then used to calculate a CTI. When the CTI exceeds a value corresponding to a 0.3 m pavement structure thawing depth, SLRs will be implemented. SLR removal will be based on average pavement structure thawing duration. Furthermore, LWD testing during the predicted thawing season will be used to further develop the method by qualifying pavement stiffness reductions during the onset of thaw and stiffness rebound after complete pavement structure thawing. Also, the TEM P/W thermal numerical model will be used as a tool to further refine the LU method through assessment of other pavement structures and environmental conditions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,887
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,210
Écart entre enseignants0,192 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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