Advancements in Pavement Performance Monitoring: A Comprehensive Analysis of Sensor-Based and Model-Driven Assessments
Notice bibliographique
Résumé
Pavements in cold regions endure extreme climatic conditions and fluctuating, often increasing, traffic loads that contribute to various structural failures over time. To extend the lifespan of these pavements and maintain their functionality, continuous monitoring and well-planned maintenance strategies are essential. This thesis presents an integrated approach to pavement performance assessment, utilizing embedded instrumentation to collect critical data on environmental conditions and the stresses and strains induced within the pavement structure. This approach enables professionals to make informed maintenance decisions based on real-time data. The research is divided into four main sections. The first part provides a literature review on sensor technologies used in pavement instrumentation, highlighting different sensor applications and their significance for accurately analyzing pavement conditions. This review examines a range of case studies that demonstrate how sensors such as Horizontal Asphalt Strain Transducers (HASTs), Vertical Asphalt Strain Transducers (VASTs), Earth Pressure Cells (EPCs), and environmental sensors play a pivotal role in gathering data on temperature, moisture, strain within various pavement layers. The insights drawn from these case studies underline the importance of sensor-based monitoring for effective infrastructure management and maintenance. The second part of this research focuses on the construction and instrumentation of a test road section built in July 2022 in Edmonton, Alberta, on a high-traffic access road leading to the Edmonton Waste Management Center. This "smart road" test section includes 36 embedded sensors that provide continuous data on pavement conditions, capturing the response to traffic loads through strain measurements and environmental monitoring. The third part of the study employs both dynamic sensor data and outputs from a Weigh-in-Motion (WIM) system, along with finite element modeling, to provide a comprehensive analysis of pavement performance from July 2023. Findings indicate that the outer wheels of vehicles predominantly influence strain sensors in the middle longitudinal row, validating the need for multiple sensor placements. A comparative analysis between sensor data and finite element model predictions showed differences of 4.3% for the horizontal strain and 22.5% for the vertical strain, underscoring the accuracy and limitations of sensor-based measurements in practical applications. The final section addresses the critical issue of pavement fatigue life, as repeated traffic loading induces strains and stresses that can lead to cracking, rutting, and other structural deteriorations over time. In this section, a one-year field study was conducted, with data collected from strain transducers and EPCs installed in the instrumented road section. Traffic data was obtained via a WIM system, and environmental data, such as temperature and solar radiation, was sourced from a nearby weather station. Additionally, a Dynamic Modulus (DM) test provided input on the viscoelastic properties of the asphalt concrete layer under different temperatures and frequencies. KENPAVE finite element software was then used to model the pavement’s response to these inputs, with results compared against field measurements. Findings showed general consistency between the predicted and observed data, though discrepancies occurred due to seasonal temperature changes and variable loading conditions. This analysis highlights the potential of combining real-world sensor data with predictive modeling to enhance the accuracy of pavement performance assessments, ultimately contributing to more resilient and sustainable pavement infrastructure. Overall, this research demonstrates the effectiveness of integrating sensor technology with modeling techniques for the continuous monitoring and assessment of pavement performance in cold climates. The findings offer valuable insights for transportation engineers seeking to design, maintain, and optimize pavement systems that can withstand the demands of both environmental and traffic-induced stresses over time.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».