Random Finite Element Reliability Assessment of Existing Concrete Structures – Case Studies and Research Direction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it