TOWARDS MORE EFFICIENT FOOTINGS FOR CONCENTRICALLY BRACED FRAMES: EFFECTS OF VARIABILITY
Bibliographic record
Abstract
Footings for steel concentrically braced frames are a major contributor to the overall cost of the seismic force-resisting system (SFRS). In general, there are two approaches to design a footing for earthquake loads. Approach 1 involves designing the footing to resist the capacity of the SFRS. Alternatively, Approach 2 entails designing the footing to withstand the design seismic load, which has been reduced from the elastic earthquake demand in accordance with the ductility of the SFRS. The second approach can produce much smaller footings than the first approach, but considering the overstrength of the SFRS, it is likely that the real seismic demands on the footing and underlying soil will exceed the design demands. As such, the size of the footing is an influential factor in the response of the buildings. Additionally, to reliably predict the seismic behaviour of buildings, it is important to consider the inherent uncertainties in the system. Consequently, it is necessary to investigate how different footing sizes affect the building's performance while taking into account both system uncertainty and record-to-record variability. In this study, a 2-storey concentrically braced frame (CBF) building with an X-bracing configuration is selected to study the effects of footing size on the behaviour of the building. This building is located in Vancouver, Canada, on a site Class D condition. The superstructure is designed following the requirements of the Canadian design code and standards, while the footing size is bounded between the most conservative Canadian approach (Approach 1 above) and the least conservative American approach (Approach 2). An advanced computational model, including gravity framing, is developed in OpenSees, and the seismic performance is assessed through nonlinear response history analysis. The uncertainty of the system parameters, including the properties of the superstructure and substructure, as well as uncertainty in the seismic demand on the building, is accounted for using Latin hypercube sampling. The findings of this research suggest the potential for finding an efficient footing size for Canadian low-rise CBF buildings that achieves desirable seismic performance without undue construction cost.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".