SEISMIC SHAKING IN SINGAPORE DUE TO PAST SUMATRAN EARTHQUAKES
Bibliographic record
Abstract
In 1996, the Meteorological Service of Singapore (MSS) installed a network of seven seismic stations. Nanyang Technological University (NTU) has also installed two additional seismic stations. Together, the nine stations form a network called the Singapore Array for Earthquake Response (SAFER). One of the stations installed by NTU consists of two sets of four accelerometers installed in a 66-storey commercial building for the study of building response to far-field earthquakes. This paper summarizes the research work that has been developed from the network of sensors. During the operation of the SAFER array, far-field earthquake ground motions have been recorded for many Sumatra earthquake events. From this, local site characteristics have been studied and hazard maps showing the amplified peak ground acceleration of the earthquake has been developed for the local sites. A case study for the hazard map due to the Bengkulu earthquake (M w = 7.7) of June 4, 2000, is shown. Based on numerical studies of typical building structures in Singapore, an additional response map showing spatial variation of approximate base shear of buildings has been developed for Singapore. A case study of the response map due to the Bengkulu earthquake (M w = 7.7) of June 4, 2000, is also shown. For future seismic hazard assessments of Singapore, a set of attenuation relationships that can reasonably predict the ground-motion intensity in Singapore generated by potential seismic sources have to be established. These attenuation relationships have to be developed using synthetic seismograms because the ground motion data that have been recorded within the last 10 years is not sufficient to develop them empirically. However, the available ground motions play a critical role in validating the synthetic attenuation relationships. The ultimate objective of this continuing research work is to incorporate a real-time monitoring system with the ground motion prediction models into hazard and response maps for scenario earthquakes. Such an integrated system when developed may assist in the planning of emergency responses to various earthquake scenarios.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".