Numerical modelling of chain-link steel wire nets with discrete elements
Bibliographic record
Abstract
Chain-link mesh is one of several net types used as protection against rockfall, shallow landslides, and debris flows. The dynamic impact and the corresponding nonlinear barrier response require numerical models. Chain-link meshes show a nonlinear anisotropic behaviour caused by the geometry of the wire. Resolving this geometry and its deformation results in a bottleneck of numerical costs. We present a discrete element model that covers the nonlinear and anisotropic behaviour of the chain-link mesh, using results from either small-scale, quasi-static tension tests or from a detailed mechanical model as material-law input. The mesh stiffness, resistance, and failure depend on the inner mesh opening angle and thus on the direction of deformation. This information enters the model through the transformation of the nonlinear, three-dimensional deformation processes into a nonlinear material law, with an interpolated dependency on the inner mesh angle. The model maps the resistance of the mesh against impacting masses and covers the energy absorption and it is capable of predicting the dynamic behaviour of different protection barriers with high accuracy, optimized calculation time, and minimized calibration efforts. This is illustrated by high impact energy tests that follow the ETAG027 standard, and also with a rockfall attenuating system.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".