Liquefaction Potential Assessment for the City of Mamuju Sulawesi by using N-SPT based methods
Bibliographic record
Abstract
Mamuju is the capital city of West Sulawesi Province which has experienced severe damages along its infrastructures due to Majene Earthquake Mw. 6.2 on January 15th 2021. On that event, liquefaction phenomenon has been found on several places, triggering foundation settlements of buildings. Unfortunately, information on Mamuju’s earthquake hazard is still inadequate, while earthquake hazard assessment is urgently needed. Therefore, this study aims to assess liquefaction potential for the city of Mamuju. Serial geotechnical investigations were undertaken through a number of boreholes and N-SPT measurements. For liquefaction assessment, methods of estimating CRRM=7.5 were used including NCEER (1996), Vancouver Task Force (2007), Chinese Code, Japanese Highway Bridge Code, Shibata (1981), Boulanger & Idriss (2014), Cetin et al. (2004), Seed et al. (1983), Tokimatsu & Yoshimi (1983), and Kokusho et al. (1983), while estimation of CSR, the Simplified method (Seed, 1974) was employed. The results show that the coastal areas in the city has a high level of susceptibility to liquefaction. The liquefaction thickness of the ground would be 8 m deep for a 0.367g seismic acceleration (200 years return period earthquake), and 10 – 16 m for 0.414g seismic acceleration (deterministic Mw 7.0 of Fault Mamuju). Ground settlement induced by liquefaction was computed based on Ishihara & Yoshimi (1992) method. It was found that the ground settlement could be 18 – 50 cm, and 31 – 71 cm for each assumed seismic acceleration. The validity of the method used in this study was examined through the comparation of predictive liquefaction thickness and ground settlement based on the empiric methods with the measured ones in the field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".