Lessons Learnt From the 2009 Padang Indonesia, 2011 Tōhoku Japan and 2016 Muisne Ecuador Earthquakes
Bibliographic record
Abstract
This paper presents the observations during EEFIT’s post-earthquake reconnaissance missions to the September 20, 2009 Padang (Mw7.6), March 11, 2011 Tōhoku (Mw9.0) and April 16, 2016 Muisne (Mw7.8) earthquakes. The performance of buildings and geotechnical structures within the affected regions were investigated to gain insights on their design and construction deficiencies. Findings on these damage observed are compared along with the characteristics of the earthquake and nature of building codes in these countries. They include building damage caused by resonance, deficiencies in reinforcement detailing, vulnerability to soft storey collapse, ground settlement, soil liquefaction and landslides. It was demonstrated that buildings which were severely damaged had natural building frequencies coinciding with the dominant frequencies of the ground shaking. The locations of damage of several such buildings showed insufficient confining reinforcements and lapping of stirrup links. Soft storey collapses were also observed in the three earthquakes, although many were attributed to old building codes that were less effective. In areas affected by the Muisne earthquake, soft storey collapses were mainly found at mid height of the building rather than at the ground floor as observed in the Padang and Tōhoku earthquakes, likely due to extension of building long after the bottom floors were completed. In the aspect of geotechnical failure, foundations of buildings found on piles performed reasonably well, except for areas affected by soil liquefaction. Landslides occurred following these earthquakes led to large concentration of casualties and property losses, motivating the EEFIT teams to invest efforts in hazard mapping and ground-truthing exercises using satellite images at Padang and Muisne earthquakes respectively. Such geospatial tools applied in these three earthquakes were reviewed and demonstrated to be capable of identifying landslide sites and producing reliable landslide hazard map.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".