Use of Structural Asset Management to Evaluate Road Substructure Drainage Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past three decades, many areas in western Canada have experienced increasing volumes of heavy commercial vehicles, primarily related to resource-based economic development. Combinations of changing moisture conditions, marginal granular materials, and heavy loadings often lead to premature road structure distress, failure, or both. In particular, slow-moving and turning truck traffic can significantly increase the applied stress states and moisture-pumping effects within the road structure, both at the surface and deep within the road structure. Strengthening a road structure to sustain severe heavy truck loadings often requires installing substructure drainage systems before placing the structural strengthening system. However, explicitly measuring the initial design requirements and the life-cycle performance of substructure drainage systems, as well as the impact of drainage systems on structural integrity, is difficult with traditional empirical model–based road structural evaluation and design methods. The use of a mechanistic model–based structural asset management approach to evaluate the performance of existing substructure drainage systems and to engineer the requirements of new drainage systems across three case studies is summarized. The findings of these case studies indicate that falling weight deflectometer and ground-penetrating radar are effective mechanistic model–based methods of structural assessment. They accurately quantify the spatial limits, the end-product structural asset value, and the performance of in-service drainage systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it