Nonlinear seismic response of reinforced‐concrete free‐standing towers with application to TV towers on flexible foundations
Bibliographic record
Abstract
Abstract Reinforced‐concrete (R/C) free‐standing towers such as TV towers are often analysed using elastic analyses as fixed‐base cantilever beams, ignoring the effect of soil–structure interaction. To take the capacity of structures after yielding into account, most designers usually prefer to decrease the peak values of the elastic response spectrum for the maximum credible earthquake (MCE) anticipated at the site by a factor called the ductility capacity factor, which varies with the design earthquake level and the structural characteristics of the structure neglecting the effect of supporting soil. To investigate the effect of foundation flexibility on the response of R/C free‐standing towers deforming into their inelastic range during intense ground shaking, a linear sway‐rocking model is applied in numerical modelling of the soil–structure system. The effect of concrete cracking and reinforcement yielding on the elements used in the structure modelling is taken into account by introducing a nonlinear model for R/C frame elements using the moment–curvature (M–ϕ) relation. A method called pseudo‐dynamic analysis is presented to quantify the inelastic seismic response spectrum of a soil–R/C free‐standing system using response spectrum analysis method and push‐over analysis technique. The earthquake responses of cracked and uncracked systems for a practical TV tower and a practical range of soil shear wave velocity are calculated and compared with the objective of understanding how soil–structure interaction influences structural responses. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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) | 0.001 | 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 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".