An Investigation of the Optimal Sample Size, Relationship between Existing Tests and Performance, and New Recommended Specifications for Flexible Base Courses in Texas
Bibliographic record
Abstract
The purpose of this study was to improve flexible base course performance within the state of Texas while reducing TxDOT’s testing burden. The focus of this study was to revise the current specification with the intent of providing a “performance related” specification while optimizing sample sizes and testing frequencies based on material variability.\n\nA literature review yielded information on base course variability within and outside the state of Texas, and on what tests other states, and Canada, are currently using to characterize flexible base performance. A sampling and testing program was conducted at Texas A&M University to define current variability information, and to conduct performance related tests including resilient modulus and permanent deformation. In addition to these data being more current, they are more representative of short-term variability than data obtained from the literature. This “short-term” variability is considered more realistic for what typically occurs during construction operations.\n\nA statistical sensitivity analysis (based on the 80th percentile standard deviation) of these data was conducted to determine minimum sample sizes for contractors to qualify for the proposed quality monitoring program (QMP). The required sample sizes for contractors to qualify for the QMP are 20 for gradation, compressive strength, and moisture-density tests, 15 for Atterberg Limits, and 10 for Web Ball Mill. These sample sizes are based on a minimum 25,000 ton stockpile, or “lot”. After qualifying for the program, if contractors can prove their variability is better than the 80th percentile, they can reduce their testing frequencies. The sample size for TxDOT’s verification testing is 5 samples per lot and will remain at that number regardless of reduced variability. Once qualified for the QMP, a contractor may continue to send material to TxDOT projects until a failing sample disqualifies the contractor from the program.\n\nTxDOT does not currently require washed gradations for flexible base. Dry and washed sieve analyses were performed during this study to investigate the need for washed gradations. Statistical comparisons of these data yielded strong evidence that TxDOT should always use a washed method. Significant differences between the washed and dry method were determined for the percentage of material passing the No. 40 and No. 200 sieves. Since TxDOT already specifies limits on the fraction of material passing the No. 40 sieve, and since this study yielded evidence of that size fraction having a relationship with resilient modulus (performance), it would be beneficial to use a washed sieve analysis and therefore obtain a more accurate reading for that specification.\n\nFurthermore, it is suggested the TxDOT requires contractors to have “target” test values, and to place 90 percent within limits (90PWL) bands around those target values to control material variability.
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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 | 0.000 | 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 | 0.001 |
| 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".