Agent-based post-earthquake evacuation simulation to enhance early-stage architectural layout and non-structural design
Bibliographic record
Abstract
In the indoor design process, architects make crucial decisions regarding architectural layout and the selection of non-structural elements. However, there is a lack of comprehensive consideration for human evacuation behavior, specifically in the event of earthquake evacuation, during the design process. This paper bridges this gap by presenting the application of Agent-Based Building Earthquake Evacuation Simulation (AB 2 E 2 S). The paper assesses a post-earthquake evacuation simulation prototype, which integrates an agent-based simulation technique with probabilistic earthquake damage assessment. The model is applied to the case of the engineering building at The University of Auckland, to evaluate the impact of earthquake intensity, design, and behavioral variables on Safe Evacuation Time and number of casualties. Overall, this paper demonstrates the potential of the AB 2 E 2 S prototype to inform architects and designers about the effective selection of non-structural elements and architectural layout scenarios for post-earthquake evacuation during the schematic design process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".