Computational turf model validation with Clegg Impact Soil Tester experimental impacts on a wide range of synthetic turf constructions
Bibliographic record
Abstract
The use of synthetic turf has been increasing in professional sports, where evaluation of novel turf designs for mechanical response, consistency, and athlete playability currently requires time-consuming and complex physical testing on physical turf, and more efficient drop-weight impact testing. A variety of drop tests are used to measure the impact response and surface stiffness of synthetic turf, including the Clegg Impact Soil Tester (CIST). Finite element computational models offer an opportunity to assess new turf designs prior to the construction of physical turfs but require validation for a range of turf constructions. In the present study, 14 different synthetic turf designs with varying infill and fibers were constructed and experimentally evaluated using the CIST device. Peak accelerations and acceleration-versus-time responses were measured. The turf constituent materials were mechanically characterized, FE models of all the turfs were created, and the CIST tests were simulated. The peak acceleration from the simulation followed the experimental trend for the wide range of turf designs, with an average difference of 9.7%. When considering conventional turf constructions, the average difference in peak acceleration was 8.8%, indicating the models provided a good prediction of turf impact response and can be used to assess new turf designs virtually, potentially reducing design times and physical test requirements.
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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 | 0.000 |
| 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".