Bibliographic record
Abstract
Structures, built in active earthquake zones, can be subjected to damaging dynamic loading. The health monitoring process for these structures is essential. When a structure is submitted to repetitive moderate earthquake events, it is expected to accumulate certain damage. The variation of the dynamic characteristics can be an indicator of the damage extension in a structure. The procedure of evaluating the dynamic characteristics of a structure is referred to as system identification. This investigation focuses on the analysis of different techniques for identifying linear structures with available recorded responses during earthquake events. These approaches are: (i) Fourier transform approach; (ii) Discrete-time filter method with Least Squares solver; and (iii) Discrete-time filter method with Instrumental Variables solver. A critical assessment of these methods is presented. The analysis of these methods was conducted considering four examples: (i) water tower subjected to blast loading; (ii) cantilever steel beam subjected to earthquake (1995 Kobe, Japan); (iii) ten-story residential reinforced-concrete building; and (iv) six-story commercial steel building. The dynamic responses of the first two examples are obtained numerically and therefore they are free of noise. For the real building examples the acceleration response recoded during the 1994 Northridge earthquake is used to identify the dynamic characteristics. The vibration data of the two buildings are obtained from the California Strong Motion Instrumentation Program (CSMIP).Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .R345. Source: Masters Abstracts International, Volume: 42-03, page: 1001. Adviser: Faouzi Ghrib. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".