Reliability-Based Safety Evaluation of the BISTOON Historic Masonry Arch Bridge
Bibliographic record
Abstract
This research examines the probabilistic safety assessment of the historic BISTOON arch bridge. Probabilistic analysis based on the Load-Resistance model was performed. The evaluation of implicit functions of load and resistance was performed by the finite element method, and the Monte-Carlo approach was used for experiment simulation. The sampling method used was Latin Hypercube. Four random variables were considered including modulus of elasticity of brick and infilled materials and the specific mass of brick and infilled materials. The normal distribution was used to express the statistical properties of the random variables. The coefficient of variation was defined as 10%. Linear behavior was assumed for the bridge materials. Three output parameters of maximum bridge displacement, maximum tensile stress, and minimum compressive stress were assigned as structural limit states. A sensitivity analysis for probabilistic analysis was performed using the Spearman ranking method. The results showed that the sensitivity of output parameters to infilled density changes is high. The results also indicated that the system probability of failure is equal to p f system =1.55 × 10−3. The bridge safety index value obtained is βt = 2.96, which is lower than the recommended target safety index. The required safety parameters for the bridge have not been met and the bridge is at the risk of failure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".