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Record W7162027338 · doi:10.82308/44322

A novel three-dimensional seismic assessment method (3D-SAM) for buildings based on ambient vibration testing

2016· dissertation· en· W7162027338 on OpenAlexaboutno aff
Farshad Mirshafiei

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAmbient vibrationModalSeismic noiseVibrationNatural frequencyModal testingModal analysisEarthquake engineering

Abstract

fetched live from OpenAlex

Most of current detailed seismic evaluation methods for buildings are based on numerical approaches. However, there is a need to use state-of-the-art interdisciplinary technologies and techniques to further facilitate such evaluations and improve their reliability, especially in many situations where detailed design documentation is not available. This study introduces a novel approach for seismic assessment of buildings, 3D-SAM, based on in-field ambient vibration measurements using acceleration/velocity sensors located on building platforms (floors and roofs). In the experimental phase of this project, sixteen low and mid-rise irregular buildings designated as emergency shelters in Montreal, Canada were subjected to ambient vibration tests (AVT) and their lowest natural frequencies, corresponding mode shapes and estimates of modal damping ratios are reported. The rate of success of AVT in this study to capture at least the three lowest natural frequencies/modes is unlike previous studies where difficulty of performing AVT and modal extractions in low-rise buildings were reported. Furthermore, the measured natural periods of concrete structures and braced steel frames of the database are compared with those obtained from the Canadian building code period formulas and the results show agreement in the case of braced steel frames. Due to the fact that in-situ experimental modal tests are low cost, and also owing to advances in sensing techniques and analysing procedures to derive the essential structural characteristic of buildings (operational modal analysis is well accepted in other engineering disciplines), the author developed a new three-dimensional seismic assessment method and software, called 3D-SAM in short form, that use this information to perform seismic assessment. The method incorporates torsional effects in predicting response, and therefore can deal with existing structural irregularities, an important limitation of other existing simplified methods. It does not require the creation of any artificial numerical model and can easily be integrated into existing modal identification software. Applications of 3D-SAM to four buildings, low to high-rise, located in Montreal are presented in this study to illustrate and validate the proposed method; results are compared with those obtained using detailed and updated linear dynamic analysis of finite element models of the buildings. Next, a modified 3D-SAM is introduced that incorporates modification factors for the adjustment of modal properties to further extend the application of the method to stronger ground motions that may cause nonlinear response. Finally, the method is used for deriving the dynamic amplification portion of natural torsion on all floors of 16 low to mid-rise irregular buildings located in Montreal, Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.036
GPT teacher head0.352
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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