Bibliographic record
Abstract
This thesis deals with the assessment of seismic accelerations in buildings and the seismic analysis of components installed on building rooftops, with special focus on operational telecommunition towers during and after earthquake shaking. First, acceleration data recorded during the 1999 Chi Chi earthquake from 11 instrumented buildings located in Taiwan were studied. Fundamental building periods were extracted by system identification and compared to those evaluated according to the equations proposed in the 2005 edition of the National Building Code of Canada (NBCC). Next, rooftop acceleration spectra and time histories were evaluated using 3-D finite element building models; three models of instrumented buildings were calibrated using accelerograms from the Chi Chi earthquake and the fourth model is a building located in downtown Montreal. The building models were subjected to 44 historical strong motion accelerograms and 30 synthetic accelerograms compatible with the target uniform hazard spectra specified in NBCC 2005 for Montreal. Based on both the experimental and numerical results, a maximum rooftop acceleration amplification of 4 is proposed for low/medium rise buildings and 3 for flexible high-rise buildings (T > 1.7 s). In the second stage, a simplified method for the prediction of seismic shear forces and overturning moments at the base of self-supporting steel lattice telecommunication towers mounted on building rooftops is presented. The proposed method involves the estimation of four parameters: the rooftop seismic acceleration, the mass distribution profile of the tower along its height, the maximum acceleration amplification at the tower top, and the fundamental sway mode shape of the tower on a rigid base. The method was validated by means of numerical results of nine generated building-tower combinations composed of three towers assumed to be mounted on three of the building models studied in the first stage of the research. The building-tower combinations were subjected to the same sets of earthquake records used for the prediction of accelerations. It was found the proposed method yields conservative results in all the cases analyzed. In addition, the empirical component force amplification factor for telecommunication towers as proposed in the NBCC 2005 was compared to the factors evaluated for the towers of the 16 building-tower combinations. Improved component force amplification factors based on rational analysis are proposed.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".