The force cone method: a new thinking tool for lightweight structures
Bibliographic record
Abstract
The force cone method developed by Claus Mattheck enables computer-free topology designing and offers a profound knowledge for lightweight structures. Thus, the recently developed method enhances the series of the so-called thinking tools. The method's basic idea is the force distribution of a single force in an elastic plane. The symmetrically placed cones appear in front of the force and behind it. These cones intersect with 90 angles at primary points that quickly lead to a structural design proposal. Furthermore, the method is very useful for the evaluation of structures and their lightweight potential. With the knowledge of the load case, it is easy to identify the main tension and compression paths leading to a deeper understanding of lightweight results. Natural structures such as trees can also be understood in another way, highlighting the structural principles at the root, leaf, treetop or even the entire tree. Nowadays, technical lightweight solutions can be found with different methods, including the soft kill option developed at the KIT 20 years ago. The method resembles that of the biological mineralization process of living bone and results in structures that can be seen as optimized lightweight design proposals. The comparisons of those structures with the state-of-the-art designs used in the industry and with those found by the force cone method indicate the high potential of the new method. For the confi rmation of the basic rules and principles, different assembly positions of force and supports as well as different types of supports, such as fi xed supports or torsion anchors, have been analyzed.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".