Application of GPR and FWD in Assessing Pavement Bearing Capacity
Bibliographic record
Abstract
Abstract The process of pavement maintenance and rehabilitation starts by collecting the data which will form the base for evaluation of pavement functional and structural condition. Collection of data can be performed by destructive and non-destructive testing. Usually preferred are the non-destructive methods, that do not damage the pavement, and the process of pavement evaluation is objective and repeatable. Non-destructive testing methods are becoming more and more popular, especially for assessing the structural condition of the pavement. Non-destructive testing by a Falling Weight Deflectometer (FWD) and the analysis of so collected data by the process of backcalculations is today the usual tool for assessing pavement bearing capacity. One of the basic input parameters for analysis of the data collected by FWD is pavement layers thickness. The practice in Croatia is to determine pavement layers thickness by coring. This destructive method affects pavement integrity, so the number of such tests should be kept to the minimum. By coring the accurate thickness of all pavement layers is obtained on specific point locations. Thus, numerous deviations in layer thickness remain unnoticed, and in the end, use of such data for the process of backcalculations does not provide ac urate values of layer moduli. Coring can be replaced with non-destructive method of testing by Ground Penetrating Radar (GPR), which provides continuous information on thickness of all pavement layers. The paper shows the method for assessing the bearing capacity of the pavement based on the data collected by FWD, GPR and coring. The calculation for layer moduli was performed by the ELMOD software, separately for the layers thickness data obtained by coring, and separately for the thickness obtained by GPR tests. Analysis and comparison of the results of calculated elasticity moduli obtained by using various methods for collecting layer thickness data were performed in the paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".