Thermal Digital Twins of Asphalt Pavements using Physics-informed Neural Networks
Bibliographic record
Abstract
To address the adverse effects of climate change on road surfaces, the creation of a thermal digital twin for asphalt pavements is proposed in this paper.The global increase in temperatures, coupled with heavy traffic loads, has resulted in the premature deterioration of asphalt roads.In response to these early failures, recent efforts have focused on enhancing pavement structural integrity by incorporating asphalt modifiers and cool pavement strategies.Regardless of the chosen approach, continuous monitoring of pavement characteristics using embedded sensors plays a crucial role in enabling timely maintenance and rehabilitation (M&R) decisions.One of the ways to achieve this is through the estimation of the thermal diffusivity of the pavement layers which can be directly related to structural condition.To estimate the diffusivity, an inverse-Physics-informed Neural Network model is proposed, which facilitates the integration of sensor data and the heat transfer mechanisms within the pavement.A finite-difference numerical model is adopted to serve as the groundtruth model, to provide the temperature data within the pavement layer, given the boundary conditions.Using the weather data of Dubai, this study has shown the feasibility of adopting Physics-informed Neural Networks (PINNs) to predict the temperature within the pavement layer, despite the complex mixed boundary conditions.Moreover, the i-PINN can be used to estimate the thermal diffusivity with an error of around 0.0001 m 2 /h, using temperature data taken from five depths of the asphalt layer.The contribution of this study, is therefore, a novel condition monitoring of asphalt pavements that can significantly improve existing road maintenance programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".