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Record W2953581389 · doi:10.1680/jstbu.19.00023

An assessment of damper placement methods considering upfront damper cost

2019· article· en· W2953581389 on OpenAlexaff
Giuseppe Marcantonio Del Gobbo, M. S. Williams, A. Blakeborough

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Structures and Buildings · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsDamperStructural engineeringWork (physics)Iterative methodComputer scienceDamping torqueControl theory (sociology)EngineeringMechanical engineeringAlgorithm

Abstract

fetched live from OpenAlex

Previous studies have shown that earthquake repair costs can be minimised by using large levels of supplemental damping and uniform damper placement. However, it is not always feasible to achieve this damping due to structural or financial restraints. Iterative damper placement methods may be able to achieve higher levels of damping than simple methods such as uniform damping for the same total damper cost. In this work, six damper placement methods were assessed based on structural and non-structural repair costs. The total damper cost was constrained to be the same in each case. The scope of work was limited to linear fluid viscous dampers, concentric braced frames and regular structures. The iterative methods were found to provide a greater total damping coefficient to the structures than the simple methods. This resulted in a higher supplemental damping ratio and lower repair costs. If upfront funds are limited, or if architectural constraints prevent the placement of dampers in lower storeys, then iterative methods provide the most favourable total-building seismic performance. However, the conclusions should be extended cautiously. Although iterative methods are favourable when the upfront damper investment is strictly limited, in terms of total-building seismic performance, it is advantageous to provide a large damping ratio using uniform damping.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.283
Teacher spread0.274 · 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 teacher head, not a consensus.

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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Structures and BuildingsSame topicSeismic Performance and AnalysisFrench-language works237,207