Towards more efficient shallow foundations for low-rise concentrically braced frame buildings
Bibliographic record
Abstract
Earthquakes pose a significant threat to communities, necessitating ongoing efforts to evaluate and mitigate potential damages. Current seismic design codes are intended to protect building occupants during severe earthquakes, and the performance of code-compliant structures in past events indicates their general effectiveness. However, these codes permit a certain level of damage, which can lead to considerable economic losses and prolonged recovery times. Consequently, decision-makers are increasingly emphasizing objectives that not only ensure individual safety but also enhance building performance and community resilience, aiming to exceed minimum code requirements. Recently, economic loss and recovery time have emerged as critical metrics for assessing seismic resilience. Among lateral force-resisting systems, concentrically braced frames (CBFs) are recognized for their efficiency and cost-effectiveness. Despite the strong performance of low-rise CBF buildings during real earthquakes, some analyses suggest a higher collapse probability under high-intensity seismic events, particularly as the structural period decreases. This discrepancy, known as the short-period building seismic performance paradox, may be partially addressed by incorporating soil-foundation-structure interaction (SFSI) into numerical analyses. Effective SFSI implementation requires careful consideration of soil and foundation modeling methods, uncertainties in soil characteristics, and variations in footing design philosophy across different codes. This thesis addresses several key research gaps. First, it evaluates how including soil and foundation effects in numerical analyses influences the computed performance of CBF buildings regarding displacement, acceleration, and economic loss, considering various footing sizes and site classes. Second, it explores efficient foundation designs for Canadian short-period buildings using Latin Hypercube Sampling to examine uncertainties and their impact on performance and repair costs. Third, it develops analytical equations to predict the sliding and rotation of shallow rectangular footings during seismic events. A practical design guide for engineers is provided as an appendix, featuring examples for calculating foundation movement by applying the new equations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".