Backbone model for confined masonry walls for performance-based seismic design
Bibliographic record
Abstract
Nonlinear models are of paramount importance in the emerging field of performance-based earthquake engineering. In this study, an analytical model is developed capable of simulating the measured backbone of typical confined masonry (CM) walls whose response under lateral loads is mainly governed by shear deformations. Equations are developed for the cracking and maximum shear strength, and the cracking and ultimate deformation capacities. This model is based on the results of both monotonic and reversed cyclic experiments assembled in an extensive database, and developed through an iterative linear regression analysis. Owing to their anomalies, specimens with compression diagonal loading, height-to-length aspect ratio greater than 1.2, axial stress to masonry compressive strength ratio larger than 0.12, and those with column longitudinal reinforcement ratio below 1%, are not considered for the purpose of creating the, empirical equations. Several statistical and graphical tools are utilized to identify the most significant panel and tie column design variables; to set the functional forms that best relate them to model parameters; and to diagnose influential points that may exert undue impact on the analysis results. The effect of openings and panel aspect ratio on the strength characteristics of CM walls, the capability of existing equations to predict the observed backbone response, and the limitations of the proposed equations are discussed in detail. Model variability is also presented in lognormal fragility curves for different model parameters and at all limit states. The proposed backbone model is found to simulate reasonably well the seismic behaviour of CM walls whose properties conform to the assumptions of the model. However, it fails to track the observed backbone response of anomalous or atypical CM walls.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".