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Enregistrement W2531158071

Optimum Deicing and Anti-icing for Snow and Ice Control of Parking Lots and Sidewalks

2015· dissertation· en· W2531158071 sur OpenAlexfundaboutno aff
Shahdat Hossain

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2015
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueSmart Materials for Construction
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaColorado Department of TransportationStrongUniversity of WaterlooMontana State UniversityU.S. Department of TransportationIowa State UniversityMontana Department of TransportationNorges Teknisk-Naturvitenskapelige UniversitetWisconsin Department of Transportation
Mots-clésIcingSnowCivil engineeringEngineeringSnow removalMeteorologyEnvironmental scienceTransport engineeringGeography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Snow and ice cause pavement surfaces to become slippery and unsafe for both foot and vehicular traffic. To alleviate the hazards of pedestrians slipping and vehicular accidents, various forms of maintenance operations such as, deicing and anti-icing are conducted to control snow and ice from transportation facilities including roadways, parking lots and sidewalks. These efforts use a significant amount of resources every winter season. For instance, over $1 billion is spent annually for snow and ice control in Canada. This large cost includes the use over 5 million tons of salts (TAC, 2013). The application of excessive amount of salts has, however, raised concerns among environmental and regulatory agencies as well as the public about their detrimental effects on the environment and corrosive effects to the infrastructure (e.g., pavement, roadside structures) and vehicles. A sensible and optimal salting strategy is therefore necessary in order to reduce the harmful effects of salt while keeping the various transportation facilities safe. 
\nTo realize an optimal salting strategy, one of the first steps is developing salting guidelines that specify salt application rates and treatment options for the conditions of any given snow event. A significant amount of research has been conducted in the past to develop such guidelines; however, most of these efforts focused on roadway maintenance with little concern about parking lots and sidewalks. The salt application rates developed for roadways are not applicable for the latter due to differences in traffic characteristics (vehicular vs. pedestrian) and service requirements (i.e., desirable bare pavement regain time). The main goal of this research is to develop a quantitative understanding of the snow melting performance of common snow control materials and methods, through a systematic field study, so that optimal application rates can be determined for parking lots or sidewalks under any specific weather events; this will ultimately lead to the development of a comprehensive winter maintenance guideline for parking lots and sidewalks. 
\nThe field tests were conducted over the winter seasons of 2011-2012, 2012-2013 and 2013-2014 in Waterloo, Ontario, Canada. In these testing seasons, there were about 100 snow events in total with pavement surface temperatures ranging from about -20°C to 3°C, and snow precipitation from about 0.2cm to 22cm. Approximately 5000 tests were conducted using different salts (e.g., regular rock salt, alternative solid salts-semi to full organic, pre-wetted salts, liquid organic salts) and treatment methods (i.e., deicing and anti-icing), including tests with plowed and unplowed snow, with and without traffic, and in both stall areas, driveways and sidewalks. In order to closely simulate the way parking lot maintenance is performed in the real world, 60 to 70% of the test operations started between 3am and 7am. 
\nThe field tests have resulted in a unique database covering the field performance of various winter maintenance materials and techniques. This performance data has then been rigorously analyzed using statistical tools to develop a quantitative understanding of the conditions that influence the effectiveness of various maintenance treatment options and to facilitate the establishment of a set of recommended treatment options and application rates for a wide variety of winter events. A majority of the tests covered deicing application of different salts. The performance of a given treatment has been measured as the time needed to reach 80% bare pavement status from the time salt was applied on top of snow. With the performance data from the deicing operations, an extensive exploratory data analysis has been conducted to investigate the factors that influence the performance of salt as a deicer. From this analysis, it was found that salt application rate, pavement temperature, snow depth, snow density and traffic are highly correlated with the snow melting performance of salt. A multivariate regression analysis was then conducted for a more rigorous analysis, quantitative information of effect, and statistical reliability of the influencing factors. The results of the regression has confirmed that all the initial factors suspected are statistically significant on the snow melting performance of salt at a 95% confidence level. With the understanding gained on physical behavior of the snow melting of salt and from the collected empirical data, a physical-empirical model has been developed. This model was then used to determine minimum application rate for a given snow event. Factors to adjust the base application rate have also been developed for some facility or treatment specific conditions such as different traffic patterns, pavement types, or using alternative salt as a deicer. 
\nIn addition to deicing treatments, a significant amount of anti-icing tests have been conducted using various common and emerging anti-icers. Since, the main objective of anti-icing is to prevent bonding between snow and the pavement’s surface form occurring, the co-efficient of friction was measured on treated sections and control sections using a friction tester after the end of a snow event. The friction data and event conditions data were then rigorously analyzed using various statistical tools to determine the optimal application rate for anti-icing purposes. 
\nIn summary, this research first investigated the direct link between the snow melting performance of salt and weather characteristics. The results derived from this work were based on impressive amount of field testing data that reflected real-world conditions. With the collected performance and weather data, a snow melting model has been developed that is the first of its kind based on the literature reviewed. The model was then used to determine minimum salt application rates for a number of given scenarios. The performance model has also been used to prescribe adjustments to the recommended application rates based on some external or site specific factors (e.g., traffic, pavement type). This research is also the first to conduct an in-depth analysis on the investigation of the effectiveness of anti-icing operations and tested both common and emerging anti-icers. Based on a significant amount of data, an analysis of variance on the friction data has been conducted to determine the effectiveness of treatment and determine optimal application rates with statistical reliability. The field test results and insight that have been gained from this research have been used to develop a decision support tool for snow and ice control in the real world. These tools are the first of their kind and are currently in use among a number of winter maintenance contractor for parking lots and sidewalks. This research also provides deep insights on the optimal winter maintenance of other types of transportation facilities, such as roadway winter maintenance operations.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,567
Score d'incertitude au seuil0,998

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 ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,008
Tête enseignante GPT0,195
Écart entre enseignants0,187 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations5
Publié2015
Routes d'admission2
Résumé présentoui

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