Optimization of inputs for the application of ANN to rail track granular materials
Bibliographic record
Abstract
Machine learning (ML) models such as artificial neural networks (ANNs) have gained increasing popularity in geotechnical engineering applications as an alternative to conventional empirical and computational models. At present, very few ML models exist for predicting the mechanical responses of track granular materials such as ballast and subballast which may even comprise of composite mixtures of blended granular materials. Moreover, the performance of any ML model depends not only on the quality and quantity of available data but also on the selection process for input parameters, which often lacks adequate justification in the past literature. In this context, the current study introduces ANN models for track granular materials based on published laboratory data with special emphasis on the selection of an optimal set of input parameters. Two applications of ANN are considered to (i) predict the peak friction angle ([Formula: see text]) of a variety of granular mixtures under static loading and (ii) predict ballast breakage under cyclic loading. The selection process involves prudent analysis of key influential parameters in a geotechnical perspective, while also ensuring that they are conveniently measurable. Performance evaluation of these models with various input combinations is carried out, while proposing optimal input parameters for both applications.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".