Development of Indonesia’s Probabilistic based Bridge Loading Code
Bibliographic record
Abstract
Indonesian bridge loading code, SNI 1725 was first introduced in 1989. Due to insufficient data at that time, the nominal bridge live load and load factor values specified in the code was based on consensus rather than data, referring to overseas loading code, such as US’s AASHTO LRFD Bridge Design Specifications and Australia’s AS Bridge Loading Code. On the other hand, AASHTO themselves was derived using probabilistic approach based on Ontario’s truck measurement data in 1970’s. In this research, the load factor calibration for Bina Marga standard designed bridges in Indonesia was conducted using reliability analysis. The research team collected vehicle load measurement data using weigh-in-motion technology and used it to evaluate the reliability of standard bridges with different types and span lengths. The target reliability index was set at 3.72, in accordance with the Strength I Limit State in the SNI Bridge Loading Code. The analysis results showed that the current SNI Bridge Loading Code resulted in an under-designed bridge superstructure. The recommended load factors to reach the target reliability index were a resistance factor of 0.90, a dead load factor of 1.60, and a live load factor of 1.96. This study is significant as it is the first time in Indonesia that the development of a bridge loading code has been based on actual load measurement data using a probabilistic approach.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.003 |
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".