NUMERICAL EVALUATION OF SEISMICALLY DEFICIENT RC JOINTS RETROFITTED WITH FE-SMA
Bibliographic record
Abstract
Existing reinforced concrete (RC) structures are seismically vulnerable given their design deficiencies and inadequate structural behaviour. Ageing and increasing weather-related hazards accelerate deterioration rate in regions subjected to harsh weather conditions. Particularly, Canadian civil infrastructure is affected by all these factors which increases their vulnerability to seismic actions. RC joints devotes special interest since they are integral structural components allowing continuity and transmission of forces. Therefore, joints are required to be the strongest elements in beam-columns joints. This permits a correct ductile behaviour and/or failure sequence in a structure. Generally, in poorly detailed RC joints this criterion is achieved through passive retrofitting techniques such as externally bonded fibre-reinforced polymer (FRP). Nevertheless, passive interventions may not be as effective as active retrofitting measures. Active retrofitting techniques, like shape memory alloys (SMAs), provide restorative forces which helps to close cracks (i.e., repair existing damage). This study conducts the assessment of an old RC frame building to seismically retrofit weak RC joints by applying a strength hierarchy approach. The strength hierarchy criteria identities joints lacking structural capacity and assist in the subsequent design of the retrofitting scheme. The potential of iron-based SMAs (Fe-SMA) as an active measure is explored by seizing the thermal activated shape memory effect. This generates a self-prestressing action in the material that can be harnessed to close cracks. Numerical models are developed to evaluate the effectiveness of implementing active retrofitting techniques over passive approaches. The outcomes reported that the strength hierarchy assessment is effective to size the retrofitting configuration for weak RC joints. The proposed retrofitting techniques not only improve the strength capacity of the joints but also their deformation capacity. A better overall behaviour is attained when the active intervention is implemented. After conducting the seismic retrofitting, beams become the weakest components in a RC beam-column joint.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".