Advancements in Pavement Performance Monitoring: A Comprehensive Analysis of Sensor-Based and Model-Driven Assessments
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".