Alvenaria estrutural de concreto: modelagem numérica, desempenho sísmico, e aplicações em edifícios
Bibliographic record
Abstract
Partially grouted reinforced masonry (PGRM) structures are widely used worldwide, especially in regions with low to moderate seismicity, due to their economic and structural advantages. The nonlinear behavior of PGRM walls is complex, as it is influenced by the interaction between grouted/reinforced and ungrouted regions, which differ in stiffness and introduce non-uniformity in the cross-section. Additionally, factors such as aspect ratio, cross-sectional area, material properties, reinforcement detailing, and axial load significantly influence the nonlinear response. At the building level, further complexities arise from layout configurations, horizontal diaphragm interactions (slabs), and axial load distributions, making it insufficient to rely solely on isolated wall responses to predict overall performance. This thesis addresses these complexities by developing numerical modeling approaches to simulate multi-storey PGRM walls, with the aim of evaluating the seismic performance of building systems and propose practical design recommendations. The research comprises four independent but interconnected studies, each presented as a separate chapter in this thesis. In the first study, a simplified macro-modeling approach is evaluated to simulate the nonlinear in-plane behavior of PGRM walls with vertical reinforcement concentrated at the pier ends. The model, validated based on experimental data from four walls tested by the author’s research group, accurately predicted global behavior, including force-displacement responses and damage mechanisms, while presenting low computational costs. However, further refinement and validation are needed to address additional detailing scenarios, such as cases where vertical reinforcement and grouted regions are not continuous across stories or where reinforcement and grout are distributed non-uniformly along the wall length. In the second study, a unified finite element-based modeling strategy is introduced that combines macro-modeling for grouted regions and simplified micro-modeling for ungrouted regions. This approach facilitates the accurate simulation of various masonry wall typologies, including unreinforced masonry (URM), fully grouted reinforced masonry (FGRM), and PGRM. The numerical model effectively captured the nonlinear response across different experimental cases, and sensitivity analyses identified key parameters that influence numerical accuracy. This study provides valuable insights for future applications in the simulation of in-plane loaded masonry structures. The third study extends the unified modeling approach to investigate the seismic behavior of multi-storey PGRM walls within three-dimensional structural systems. Nonlinear static (pushover) analysis results revealed the significant impact of orthogonal walls (flanges), aspect ratios, axial load levels, and vertical reinforcement arrangements. Flanges have been shown to increase lateral stiffness and force capacity, higher axial loads enhance force capacity but reduce ductility, and a greater number of stories decreases stiffness and increases ultimate displacement. The vertical reinforcement concentrated at the wall ends exhibited an efficiency comparable to uniformly distributed reinforcement, demonstrating it as a suitable alternative. In the fourth study, the seismic performance of two real-world reinforced masonry buildings representative of Canadian (B1) and Brazilian (B2) practices is examined. Using a multi-step methodology that integrates eigenvalue analyses and pushover simulations, the study highlighted the critical role of structural layout in the seismic performance of buildings. Balanced wall distribution and flange contributions significantly improved performance. Notably, building B2, despite lacking explicit seismic design provisions and detailing, outperformed B1 under higher seismic demands, underlining the importance of optimized structural layouts in enhancing seismic performance. Overall, this thesis establishes a comprehensive framework for evaluating and improving the seismic performance of concrete masonry buildings. The proposed modeling approaches, validated based on experimental data, provide support for analyzing masonry systems and informing potential updates to seismic design standards. The findings emphasize the importance of regional adaptations in building design and structural layout optimization to achieve better seismic performance.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".