Development of rapid visual screening form for Nepal based on the data collected from - its 2015 earthquake
Bibliographic record
Abstract
Abstract An earthquake on the 25th April 2015 in Nepal caused more than 9,000 deaths and 22,000 causalities. The main reason for such huge casualties is the lack of earthquake awareness and poor construction practices. With a large number of vulnerable houses, Nepal faces a huge risk from future earthquakes, due to the recent construction in urban areas of poorly designed and constructed buildings. Such unplanned and haphazard growth will be potentially dangerous from natural events like earthquakes. A vulnerability map gives the exact location of sites where people, natural environments and or properties are at risk due to a potentially catastrophic event. This will allow them to decide on mitigating measures to prevent or reduce loss of life, injury and environmental consequences before a disaster occurs. While Rapid Visual screening is a classical method of preliminary vulnerability study, it is one of the most economical, reliable, simple and efficient methods to determine the vulnerability level of buildings. The most known rapid visual screening methods have been developed in countries of high seismic risk such as the USA, Japan, Indonesia, New Zealand, India and Canada and are briefly described in this paper. The main objective of this paper with the help of ordinal regression is to calculate variables of damage grade model in a rapid visual screening form. This is useful to identify vulnerable and non-vulnerable buildings very quickly, and help make a plan for the implementation of a disaster risk mitigation program. Based on SPSS Statistics Software, an ordinal regression method was used to model the relationship between outcome variables. The analysis was performed by employing a database after the 25th April Gorkha earthquake of 2015. Preparing RVS special features of buildings in Nepal, have been given due consideration, and were evaluated for adherence of age, plan configuration, position, land surface condition, plinth area, building height, floor count, foundation types, ground floor types, roof types, condition, are highly significant parameters in analysing the vulnerability of them during an earthquake.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".