Design Model for Roughness and Serviceability of Pavements on Expansive Soils
Bibliographic record
Abstract
A model was developed to predict pavement roughness caused by both expansive soils and traffic in terms of the serviceability index (SI) and the International Roughness Index (IRI). The model correlates the roughness analysis to the vertical movement estimated from the Texas A&M University suction-based method. The total vertical movement (including both swelling and shrinking) at the edge of pavement sections, the geometry of the pavement, site conditions, traffic, and the level of reliability were used as model parameters. Total movements calculated at the edge of pavement sections were based on a relationship between moisture content and suction, exponential suction envelopes, volume change coefficients, pavement treatments, and roadside conditions. Pavement treatments included vertical and horizontal barriers, inert soil, and lime-stabilized or cement-stabilized layers. The movements in wheelpaths at a distance from the edge of pavement were estimated on the basis of both field observations and the computed results of a transient finite element analysis. Transverse distribution of vertical movements on a pavement cross section was estimated. A relationship between IRI and SI was developed on the basis of surface profile measurements in several pavement study sections. The design equations that were developed for both flexible and rigid pavements include the effects of traffic and expansive soil and permit the selection of the desired level of reliability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".