Estimation of the Annualized Earthquake Loss (AEL) for Residential Buildings in the Greater Montreal area using HAZUS and OpenQuake
Bibliographic record
Abstract
Annualized Earthquake Losses (AEL) are estimated for residential buildings in the Greater Montreal region using two software, Hazus and OpenQuake. The loss estimation for each return period requires the following inputs: probabilistic hazard map with soil effect, building exposure model, census demographic information, and vulnerability. The losses for a range of return periods are used to calculate AEL. AEL estimates the average loss per year in a region that accounts for the variability in the location of the epicenter and the magnitude of earthquakes. It is an important information for public safety officials to identify the area most at risk as well as for determining the potential economic and human losses. AEL also provides a basis to compare the relative risk between various types of natural hazards and to prioritize risk mitigation measures. Probabilistic hazard models in Canada are provided by Natural Resources Canada (NRCan). The analysis was first performed according to the 5th generation seismic hazard model (SHM5), which is used for the 2015 Canadian National Building Code and to a limited extent with the 6th seismic hazard model (SHM6), which is used for the 2020 Canadian National Building Code. The total annualized residential earthquake loss based on SHM5 is estimated at Can$ 6.18 million with Hazus. The AEL is dominated by non-structural and content losses, which represents approximately 90% of the total AEL. A sensitivity analysis is conducted and indicates that the effect of ground motion level has the greatest effect on AEL followed by building value, construction type and code level. The result from Hazus is also compared with AEL calculated for US by FEMA, which indicates that the AEL for the Greater Montreal Area is consistent with values obtained in the US for urban areas with similar seismicity and exposure. The AEL was also estimated with OpenQuake since the software has been adopted by NRCan to implement SHM6 as well as for future generations of seismic hazard maps in Canada. Hazus uses fragility function while OpenQuake can operate with fragility functions as well as with vulnerability functions. The estimates with OpenQuake using vulnerability functions were obtained with functions provided by NRCan. The AEL of OpenQuake with the vulnerability approach is Can$ 6,16 million and is similar to AEL obtained by HazCan. The AEL of OpenQuake with the damage approach is Can$ 12.4 million and overestimates AEL in comparison to Hazus. The discrepancy is mainly in relation to non-structural damage. Estimates of AEL with OpenQuake based on fragility analysis is obtained by calibrating the fragility functions with those of HazCan. This could be done accurately for structural losses but could only be done approximately for non-structural losses, which need to be derived separately for acceleration-sensitive and displacement-sensitive losses. The formulation of fragility curves for non-structural damage and content needs to be further investigated. Additionally, estimates of losses based on SHM6 were obtained for the return period of 2475 years. These preliminary results indicate that losses from SHM6 greatly increases for the Greater Montreal Area due to the increased average ground motions. It is recommended that the full probabilistic approach to calculate AEL be implemented as the next phase to this project. The analysis should also be extended to other populated regions of the St-Lawrence valley with high seismic hazards to provide a comprehensive assessment of residential seismic hazards in Quebec. Future applications should also provide estimates for social and other costs due to earthquakes
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".