Evaluation of Structural Materials to Maximize Infrastructure Survivability in Wildfires
Bibliographic record
Abstract
Wherever possible, utilities favor routing their transmission, sub-transmission, and distribution lines outside of urbanized areas in grassland and forested areas. This minimizes right-of-way costs, aesthetic impacts, and public objections. Recently due to global climate change, the frequency and intensity of wildfires in grassland and forested environments have dramatically increased. This has negatively impacted the utilities with higher levels of structural damage, power outages, lower system reliability, and higher system restoration costs. After experiencing overhead infrastructure losses through several wildfires, in 2017, Southern California Edison (SCE) started to explore and investigate the options for increasing the fire resistance of its assets in high fire prone areas. The investigation was extensive and included an evaluation of new and alternate structural materials as well as protective coatings and barriers which could be installed on new and existing structures. After performing a high-level analysis of all products, SCE developed a short list of viable products. SCE worked with the manufacturers of those products to test their products to simulated wildfire conditions. SCE adopted two fire test standards to which all products would be tested. One was based on an ASTM draft standard, the other was developed in consultation with a wildfire expert from the University of Alberta. These tests provided a common ground to compare the effectiveness of the products. The fire resistance of the products was determined by comparing a structure’s strength before and after the fire testing. Based on the results of the tests, SCE has developed a revised structural strategy for new and existing structures in high fire areas. The strategy includes the use of lab tested, fire-resistant structural materials for new structures and fire-resistant coatings/wraps for existing structures. Wildfires in 2020 have given SCE the opportunity to see how these new fire-resistant materials perform in actual wildfire conditions and compare the results with the simulated wildfire testing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".