Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges
Bibliographic record
Abstract
Efficient seismic vulnerability and resilience assessment is essential for ultra-high-performance concrete (UHPC) bridges, given their distinctive mechanical and structural properties. However, existing single-parameter-based probabilistic seismic demand (PSD) models overlook critical bridge‐specific characteristics and uncertainties. Besides, studies on seismic vulnerability and resilience assessment of UHPC bridges are scarce. Thus, this study proposes a hybrid machine learning (ML)-enabled multivariate bridge-specific seismic vulnerability and resilience assessment framework for UHPC bridges. Key design parameters and associated uncertainties are identified, and a Latin Hypercube Sampling (LHS) technique is employed to establish a representative UHPC bridge database, which is used to develop a hybrid ML model-based multivariate PSD model. A comparative analysis with the conventional PSD model, as well as widely used ML algorithms, demonstrated that the proposed PSD model achieves the highest predictive performance, characterized by the highest coefficient of determination and lowest prediction errors. Additionally, SHapley Additive exPlanation (SHAP) analysis is used to investigate the effect of different parameters on the PSD of UHPC bridges. The results of SHAP show the peak ground acceleration (PGA) as the most important factor, followed by bridge span and column diameter. The hybrid ML-enabled multi-variate bridge-specific fragility analysis results are used to investigate the functionality recovery and resilience of the bridge, which demonstrate the reduction in the residual functionality and overall bridge resilience with the increase in the ground motion intensity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".