Developments in Quantifying the Response Factors Required for Linear Analytical and Seismic Design Procedures
Bibliographic record
Abstract
Despite the recent initiatives and developments in building design provisions using performance-based design, practicing engineers frequently adopt force-based design approaches, irrespective of the structural system or building irregularity. Modern seismic building codes adopt the concept of simplifying the complex nonlinear response of a structure under seismic loading to an equivalent linear response through elastic analytical procedures using seismic design response factors. Nevertheless, code-recommended seismic design response factors may not result in a cost-effective design with a uniform margin of safety for different structural systems. Previous studies also adopted different methodologies for quantifying the seismic response factors. Hence, there is a pressing need for a comprehensive review covering the developments in quantifying these vital design factors. This paper presents a systematic review of the response factors used with linear analytical procedures recommended by modern building design provisions and the techniques employed in previous studies to evaluate these factors using SDOF and MDOF systems covering analytical, experimental and hybrid assessment approaches. Limitations and gaps identified from the previous studies indicated that most investigations focused on 2D analysis and regular low-rise buildings, while limited studies were directed to shear wall structures, considering mostly unidirectional seismic loading. This comparative review provides insights into previous studies’ methodologies and constraints and identifies the need for future research to calibrate seismic response factors to achieve more economical designs with consistent safety margins for different structural systems.
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 | 0.000 |
| 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".