4.0 Strength of Single Pole Utility Structures
Bibliographic record
Abstract
The term "utility structure" covers a very broad range of structural configurations. These structures vary in size, shape, load capacity, material used and importance. However, conventional Allowable Stress Design (ASD) does not account for the interactions between load, resistance and importance. The probability of failure is affected by variability in both resistance and load. While ASD procedures generally do evolve to provide acceptable levels of reliability (1- failure probability), they do not provide a rational approach to quantifying that reliability or extending it uniformly to the broad range of structural configurations. To develop a design algorithm, which provides a uniform level of reliability it is necessary to quantify the reliability for any structure. Quantifying reliability for a variety of conventional structures provides a basis for setting target levels for future design. The resulting algorithms then also provide a means of adjusting conventional designs to common reliability basis. While it may not be possible to quantify the "absolute" reliability, it is possible to provide "relative" value by using a standard "Reliability" basis for derivation of resistance factors to be used with a Load and Resistance Factor Design (LRFD). The greatest challenge to adopting a reliability basis for derivation of resistance factors for an LRFD methodology, however, is characterizing the probability density functions (PDF) for load capacity of a broad range of utility structures. In the case of wood structures, the Accredited Standards Committee 05 (ASC 05) maintains a database of full scale pole test results to provide an empirical basis for the PDF. For manufactured poles (concrete, steel, fiber reinforced polymers (FRP)), however, the PDFs are more likely to be characterized using analytical models, which reference raw material engineering properties. These models range in complexity depending on the pole configuration and the number of failure modes that must be considered. In general their ability to predict performance of a range of pole configurations is verified by full-scale tests, but quantifying the true variability of a structure type using analytical simulation can involve complex interactions and be difficult to verify.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".