Analytical procedures for evaluating factors that affect joint faulting for jointed plain concrete pavements using the Long-Term Pavement Performance database
Bibliographic record
Abstract
The objective of this study was to identify and quantify design and construction features most important to joint faulting of joint plain concrete pavements. With data obtained from the Long-Term Pavement Performance (LTPP) database, an analysis approach that combined pavement engineering expertise and modern data analysis techniques was to develop guidelines for improved design and construction of Portland cement concrete (PCC) pavement. The approach included typical preliminary analyses, but emphasis was placed on using a series of multivariate data analysis techniques. Discriminant analysis was used to develop models that classify individual pavement into performance groups developed by cluster analysis, which was used to partition the pavements into three distinct groups representing good, normal, and poor performance. These models can be used to classify and evaluate additional or new pavements performance throughout the pavement's design life. To quantify the levels of the key design and construction features that contribute to performance, the classification and regression tree procedure was used to develop tree-based models for performance measure. The analysis approach described was used to develop the guideline on the key design and construction features that can be used by designers to decrease joint faulting of jointed plain concrete pavements (JPCPs).Key words: faulting, Long-Term Pavement Performance (LTPP), jointed plain concrete pavement (JPCP), cluster analysis, discriminant analysis, classification and regression tree (CART) analysis.
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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 | 0.000 | 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".