Evaluation and Repair of Tornado Damage to a Large Manufacturing Plant
Bibliographic record
Abstract
On February 28, 2017, an EF3 tornado caused widespread damage to the little town of Naplate, Illinois, and the neighboring town of Ottawa, Illinois. In Naplate a large glass manufacturing plant was in the direct path of the tornado and sustained significant damage to portions of several buildings including tearing off or damaging over 390,000 sq. ft. of roofing, peeling off metal roof deck and structural steel purlins, twisting and deformation to multiple steel plate girders up to 40-inch deep and heavy wide-flange steel columns, and pulling steel column base plate anchors out of the concrete foundation. The equipment needed to be protected and serviced concurrently as the structure repairs and plant operations were performed around and above them. Engineering Systems Inc. (ESi) was retained to have a team of engineers evaluate the damage, prepare repair design, and provide onsite field engineering to create solutions for the ongoing issues as they developed. This paper will discuss the methods used to evaluate the damage to the building including the use of drones to evaluate roofing damage. Additionally, discussion will be provided regarding temporary protection methods to facilitate a compressed schedule with multiple trades literally working on top of each other and around plant activities while partially in operation, analysis of existing structural steel, and design of repairs to the structural steel and building envelope. Several existing plate girders required advanced analyses to resolve code compliance issues which were justified given the cost saving considerations of rehabilitation vs. replacement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".