Simplified Dynamic Analysis Methods for Guyed Telecommunication Masts under Seismic Excitation
Bibliographic record
Abstract
Earthquake-resistant design of essential infrastructure is paramount in areas with high seismic hazard and risk. Such critical infrastructure includes tall telecommunication masts which are highly nonlinear structures owing to the behaviour of their multi-level guy clusters. During earthquakes, their multi-support foundation is subjected to seismic wave delays that vary with the shear wave velocity of underlying soil. Such effects can be properly described in detailed nonlinear seismic analysis models, which are mainly used to explain exceptional situations (for example, mast failures) and for research purposes. However, the degree of complexity and sophistication these models require is too high for routine engineering design, and simplified procedures are necessary to perform design checks on the seismic vulnerability of tall masts. As a first step towards a simplified dynamic analysis procedure, this paper presents a method to obtain the equivalent dynamic properties of guy clusters. The method is based on rational cable mechanics and is verified with results from detailed finite element analysis of selected detailed models of real guy cables. However, the number of towers and the frequency range studied are limited, as this paper presents an ongoing research. More case studies are necessary to validate the method before it can be used in engineering practice.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".