Development of a Practical Tool to Consider Climate and Climate Change in Subgrade Resilient Modulus for Road Pavements
Bibliographic record
Abstract
Abstract The prediction of the resilient modulus of foundation soil or subgrade for road pavements has been a subject of significant interest in recent years. This study presents a summary of the factors that influence resilient modulus performance, including climate and climate change, and how this property is fundamental for pavement design. To incorporate climate and climate change in the resilient modulus of the pavement subgrade, a practical tool was developed based on the Climate Information System for Road Design (SICliC) and the Enhanced Integrated Climate Model (EICM) of Mechanistic-Empirical Pavement Design Guide (MEPDG), where the Thornthwaite Moisture Index (TMI) can be estimated based on historical monthly precipitation and mean temperature data and using climate change scenarios for Mexico. The proposal determines the prediction of the behavior of variables that influence the subgrade resilient modulus for unbound granular materials (UGMs) which are associated with climate, such as moisture content, soil suction and degree of saturation, by means of which an environmental factor for unbound materials can be estimated. This factor adjusts the “Resilient modulus at optimum” to an “Resilient modulus at equilibrium” for the project site. Incorporating the climate and climate change of the project site into pavement design, particularly into the resilient modulus of the subgrade, will increase the durability and resilience of pavements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".