Agent-based simulation of the effectiveness of policies for the adoption of seismic retrofits
Bibliographic record
Abstract
Recent developments in engineering design have resulted in substantial improvements to the seismic performance of new buildings. However, buildings constructed before recent upgrades to the seismic regulations comprise a large portion of the existing building inventory. Buildings with insufficient seismic capacity are susceptible to extensive damage or collapse during an earthquake, contributing to economic losses and casualties. Effective risk mitigation strategies such as seismic retrofitting could potentially address the vulnerability of the existing building stock to earthquake hazards. Yet, seismic retrofit programs suffer from low take-up rates. Thus, identifying barriers for seismic retrofit adoption and strategies to increase take-up rates can help mitigate losses from future events. This study develops an agent-based model to assess homeowner response to multiple seismic retrofit promotion strategies. The simulation framework is applied to a case study of owner-occupied, residential-detached dwellings in Vancouver, British Columbia, Canada, to evaluate the effectiveness of various potential seismic retrofit promotion strategies. These strategies are compared regarding the number of adopters and the reduction in total annual losses to residential building structures and contents in the City of Vancouver. The main barriers to adopting retrofit measures among different income groups are identified, and appropriate interventions to target those barriers are suggested. Modeling the impact of policies allows policymakers to evaluate their effects and fine-tune the policy interventions before their implementation.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".