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Record W2955858596

Impact of friction dampers and ductility factor on the seismic response of concrete moment resisting frame buildings

2018· dissertation· en· W2955858596 on OpenAlexfundaboutno aff
Anne-Gaelle Pilorge

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2018
Typedissertation
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersConcordia University
KeywordsDamperStructural engineeringDissipationStiffnessEngineeringVibrationVibration controlSeismic analysisDuctility (Earth science)Reduction (mathematics)Moment (physics)Materials science
DOInot available

Abstract

fetched live from OpenAlex

Infrastructures around the world are impacted by seismic events and therefore can suffer different types of losses that include: life, structure, economy and much more. It is important to control the vibration in structures using appropriate design methods, materials, and energy dissipation devices. There are many different types of energy dissipating devices providing supplemental damping to structures and control their vibration response. This research focuses on friction devices, particularly, the inline friction dampers used in diagonal bracings to control the vibration in buildings. There is no standard design process available in the National Building Code of Canada to design buildings with friction dampers. The procedure suggested in FEMA guidelines is quite complicated to use. The focus here is to use a rational method for building design with friction dampers and demonstrate the impact of friction dampers in the design process, and seismic response. Currently, dampers are used as a device which are supplementary to the structure post- design, to increase its strength and stiffness that benefit structures. However, that produces a highly conservative design which may not be economically justified. By letting the dampers take about a tart of the lateral forces, structure can be optimized. The effect of fiction dampers was observed by the reduction of moment and shear on columns, the reduction of cost. Six structures were designed for this study: elastic with and without dampers, moderately-ductile with and without dampers and ductile with and without dampers. \n \nThis study demonstrates that by designing and applying friction dampers into the design stage, the beams and columns attract less moment and shear impacting their sizes. While designing the structure, adding dampers helped reduce the cost in material for all three structures by around 7.5% in contrasts to the same model without dampers. An optimization of the structure section was made after adding the dampers into the structure. The impact of moment and shear into the columns and beams was shown to also be reduced of nearly 25-40% (the average is 29.5%). The seismic response of the different building models was determined using nonlinear pushover and time-history analyses. The results show that despite having smaller sections for beams and columns, the structures with dampers have reduced drift as compared to those without dampers. It was clearly demonstrated that friction dampers have an impact into the design of structures making them stronger with a higher response and lower cost. After calculating the cost of material and the overall results of analysis, the ductile structure is found to be the most economical choice. However, considering the post-earthquake damage and repair cost, the moderately-ductile structure with dampers will be a better option.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.271
Teacher spread0.252 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2018
Admission routes2
Has abstractyes

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