Weigh-in-Motion and Structural Deflection Based Mechanistic ESAL Factors for Urban Pavement Asset Management
Bibliographic record
Abstract
Equivalent Single Axle Loads (ESALs) have been the standard measure of traffic loadings used in new highway engineering design and life cycle performance prediction. However, truck and bus traffic in urban centre field state conditions often pose a higher probability for overloading relative highway applications. In addition, many urban pavement systems are not constructed to the same structural standard as typical AASHTO type primary highway pavement systems in terms of layer thicknesses as well as cross sectional drainage. This research employed a mechanistic-based road structural response methodology to calculate ESALs from commercial truck and bus loadings on various classes of urban streets based on actual primary road response impact load spectra. This project integrated weigh-in-motion (WIM) and pavement deflection measures to quantify City of Saskatoon urban traffic load spectra, and the impact of this load spectra on typical in-service urban streets in typical field state conditions. Based on measured pavement primary responses across City of Saskatoon road structures and the measured urban load spectra, this research found that conventional Equivalent single Axle loads may significantly underestimate the impact of truck loadings on in-service urban roads. This research also showed that based on primary deflection response, multiplying factors approaching 40 may be required to calculate load equivalencies. Therefore, the structural design, transit routing, and construction bypass routing should incorporate the actual detrimental impact of specific vehicle types and loadings on the in-situ structural condition of the in-service road structure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".