Bibliographic record
Abstract
Within the last 10 years, there has been an increase in the number of wildfires affecting urban areas, primarily in the western United States and Canada. After the fires are suppressed, infrastructure experts, including engineers, are tasked with investigating the extent of fire-related damage to determine whether repairs to the building or structure are possible, if any code upgrades are required, or if the damage meets a threshold to warrant the replacement of the building or structure. Often, the investigations include an examination of the concrete elements of the building, such as walls, elevated slabs, or on-grade foundations. However, challenges frequently arise when accessing the sites after an urban wildfire, as entry to the affected areas is often limited to medical examiners, law enforcement, and other government organizations. When access to the site is finally granted, in many cases, the forensic engineer has limited time and resources to evaluate and quantify the damage. This paper will explore some of the complexities in handling an engineer’s response to urban wildfires from the investigation phase, including destructive and non-destructive evaluation of materials, through the development and permitting of repair documents needed to restore damaged buildings or structures. Because buildings and structures are assets typically covered by insurance policies, common questions from insurance professionals unique to an engineer’s investigation of urban wildfire claims will also be discussed. Additionally, this paper will discuss the development of repair documents for these repair or reconstruction projects, including potential code upgrades triggered, possible challenges encountered during the construction phase, and unique situations the authorities having jurisdiction may encounter.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".