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Enregistrement W7011390873

Lightweight Cellular Concrete as Flexible Pavement Subbase Material: Field Performance and Sustainability Study

2022· dissertation· en· W7011390873 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2022
Typedissertation
Langueen
DomaineEngineering
ThématiqueAsphalt Pavement Performance Evaluation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSubbaseSustainabilityField (mathematics)Section (typography)Production (economics)Pavement engineeringControl (management)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Enhancing the long-term performance of road infrastructure is an important goal for engineers in Canada, especially with changing climatic conditions. This has brought about varying approaches in design and construction techniques with alternative materials. Some factors considered in selecting these materials include advantages in terms of sustainability, lower costs, ease of construction, and increased structural capacity. Lightweight Cellular Concrete (LCC) is one such material that could be a viable option in Canada, where the pavement structure is subject to the freeze-thaw effect yielding weaker subgrades. Previous research acknowledges that as a subbase material, it could yield promising results as it has shown good freeze-thaw resistance, ease of placement as it is semi-liquid, and potential sustainability benefits such as the reduction in the use of virgin materials through the usage of industrial by-products, less pollution, and lower lifecycle costs. However, there is a need to quantify these benefits and develop unified standards and specifications for using this material in the pavement structure in Canada. \n \nThis study evaluated three densities of LCC with two production methods (wet and dry mix) in terms of constructability, field performance, and sustainability. This involved pre and post-construction monitoring of test and Control sections to evaluate the performance of the LCC layer. Two test sections were built in Waterloo, Ontario. One of the trial roads (Erbsville) incorporated 250 mm and 350 mm of 475 kg/m³ LCC as subbase material and compared it with a Control constituting of 450 mm granular B subbase. The second test section (Notre Dame Drive) employed 200 mm LCC with densities of 400, 475, and 600 kg/m³ compared with a 150 mm granular A subbase. \n \nConstructability evaluation involved field testing, onsite, and instrumentation monitoring during construction. Field performance tracking was achieved across varying climatic conditions using pre-installed instrumentation to monitor stress and strain responses, layer temperature, moisture, and LCC maturity. Precipitation and ambient temperature conditions were monitored using a weather station at both field test locations. Also, pavement material properties from the field sections were tested at the CPATT laboratory. The tests performed included dynamic modulus for the asphalt concrete, unconfined compressive strength, modulus of elasticity and Poisson’s ratio, and water absorption tests for LCC, California Bearing Ratio (CBR) test for the pavement unbound layers. \n \nFurthermore, stiffness and structural capacity were evaluated using the Falling Weight Deflectometer (FWD) and Lightweight deflectometer. Roughness assessment was done with SurPro and Dipstick equipment. Regular visual inspections were conducted to capture distresses on the pavement sections. Finally, Lifecycle Assessment (LCA) and Lifecycle Cost Analysis (LCCA) were performed to quantify economic and environmental outcomes compared with current industry standards. \nThe results revealed that applying a Lightweight Cellular Concrete subbase within the pavement structure is a feasible alternative to traditional subbase material, especially when subgrade insulation is required and weak subgrades are encountered. It showed that excessive vehicles and trucks over the LCC pavement sections before asphalt paving could be detrimental to LCC pavement performance by inducing higher stresses and strains. Lightweight Cellular Concrete with densities between 400 and 600 kg/m³ has excellent insulation properties within the pavement structure. It can reduce subgrade pressure due to traffic by up to three times compared to unbound granular material and strain responses by four times. These attributes were seen to increase with an increase in density. Layer temperature and moisture were influenced by ambient temperature and precipitation events and, in turn, influenced pavement stress and strain responses. A structural coefficient of 0.22 was determined for 475 kg/m³ and proposed as a benchmark for designing LCC pavements with a density between 400 and 600 kg/m³. \n \nLifecycle assessment results showed that LCC pavements could lower environmental costs by reducing total life CO2 emissions by up to 16% while significantly reducing the environmental impact of SO2, CO, NOx, PM10, and total PM compared to unbound granular pavements. Environmental impact was seen to increase with an increase in LCC density. Total life costs of the LCC sections were 10% to 13% more than pavement sections with granular A and 4% to 6% more than granular B pavements. However, when only the initial construction and maintenance phase were considered, the LCC sections were 3% to 6% less expensive than the granular A pavement and 9% to 12% less than the granular B pavement. The 400 and 475 kg/m³ LCC pavements had comparable costs. \n \nThis study provided an understanding of the pavement behavior of lightweight cellular concrete subbase and developed pavement layer temperature regression models for LCC and unbound granular subbase pavements with very good predictability. It presents a way to assess LCC pavements' environmental and economic impacts and recommends design, construction specifications, and guidelines for using lightweight cellular concrete subbase within flexible pavements.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,007
Tête enseignante GPT0,210
Écart entre enseignants0,203 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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

Citations3
Publié2022
Routes d'admission1
Résumé présentoui

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