Random Finite Element Reliability Assessment of Existing Concrete Structures – Case Studies and Research Direction
Bibliographic record
Abstract
The material properties of concrete and reinforcing steel in reinforced concrete structures vary spatially across the structural dimensions due to the inherent heterogeneous nature of concrete. The spatial variation is further affected by active deterioration mechanisms such as freeze thaw damage, alkali silica reactivity, and corrosion of the reinforcing steel as all of these mechanisms are random. The spatial variation of concrete mechanical properties (compressive strength, tensile strength, modulus of elasticity) and corrosion effect of reduced section loss influence the structural reliability, and hence, the structural risk. Random finite element (RFE) simulation has been recently employed in engineering consultancy to assess structures with spatially varying properties using reliability analysis. The objectives of this paper are to 1) document the application of RFE in real-life case studies of structural reliability assessment projects conducted by the authors in Canada, and 2) provide recommendations for future research to improve the practicality of analysis for consulting jobs. The paper will provide readers with insight into the application of advanced methods of analysis in consulting work for risk-based condition assessment of critical infrastructure. Recommendations for future research and analysis refinements are discussed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".