Optimal tradeoffs between the security and cost of critical buildings and infrastructure systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Explosive terrorist attacks targeting critical buildings and infrastructure systems pose a formidable threat worldwide, having caused 12,425 casualties and $20 billion in direct economic losses in 2015 alone. Designers of these critical buildings attempt to minimize the security risks to site personnel and buildings by analyzing and selecting the most effective combination of: (1) increasing the standoff distance between site assets and potential locations of explosive attacks; (2) constructing blast-mitigating perimeter walls; and (3) hardening site facilities. To support designers in this critical and challenging task, this paper presents the development of a multi-objective optimization model capable of generating optimal tradeoffs between minimizing total site destruction levels and minimizing site construction costs. The model computations are performed utilizing the nondominated sorting genetic algorithm II (NSGA-II) because of its proven capability in modeling non-linear objective functions and constraints, and its successful modeling of previous facility layout problems. The model performance was evaluated using a case study of a hypothetical military forward operating base, and the results illustrated the novel capabilities of the developed model in identifying design configurations that generate optimal tradeoffs between the aforementioned optimization objectives. These capabilities are expected to support designers in their ongoing efforts to construct cost-effective sites that minimize the security risks to personnel and buildings from the threat of explosive terrorist attacks.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it