Bibliographic record
Abstract
Transmission tower designers are left with little to no guidance on how to design and analyse towers when considering seismic excitation. There are few valuable published works with suggestions on how calculate and lump a tributary mass of the span of conductors, insulators, etc. to the attachment points on the tower supporting the span. This is normally followed by analysing the tower using simplified, quasi-static, or response spectrum methods to obtain the straining action necessary to perform the design. There are several valuable investigations and data collection on the real-life behaviour of transmission towers after seismic events that suggest the majority, if not all, of the recorded failures are attributed to foundation issues. It is still necessary, however, for a transmission tower designer to conclude with higher level of certainty whether a seismic load case is likely to govern the design of the tower, member of a tower or group of members, or not. As part of BC Hydro’s design plan for a new transmission project including three new, short 500 kV transmission lines, analysing the self-supporting lattice towers on these lines was required. The analysis is performed using several methods: 1. Simplified analysis of the towers with conductors lumped masses; 2. Response spectrum analysis of the towers using conductors lumped masses; 3. Full time history analysis of towers using conductor lumped masses and synthesized earthquake acceleration; and 4. Full time history nonlinear analysis of one of the three lines as coupled system consisting of towers and conductors using synthesized earthquake acceleration from the site design spectrum. This paper presents the results obtained from each of the four methods, comparison between forces obtained in several members as well as a discussion of the level of effort involved with each method.
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 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 | 0.001 |
| 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) | 0.005 | 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".