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Record W2279702970 · doi:10.1193/110914eqs185m

Cost‐Benefit Analysis of Buildings Retrofitted Using GIB Systems

2016· article· en· W2279702970 on OpenAlexaff
Hossein Agha Beigi, Constantin Christopoulos, Timothy Sullivan, Gian Michele Calvi

Bibliographic record

VenueEarthquake Spectra · 2016
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Toronto
FundersNational Outstanding Youth Science Fund Project of National Natural Science Foundation of ChinaInternational Union of Soil Scientists
KeywordsRetrofittingMasonrySeismic retrofitBraceEngineeringFrame (networking)Cost analysisColumn (typography)Structural engineeringCivil engineeringComputer scienceReliability engineeringReinforced concreteMechanical engineering

Abstract

fetched live from OpenAlex

Recently, the gapped‐inclined brace system (GIB) has been developed as an effective retrofitting solution for soft‐story buildings. This paper presents a cost‐benefit study of a building retrofitted using the GIB system. A six‐story, reinforced concrete (RC) frame with an open story at the ground level and masonry infills on all other floors is studied. To investigate the effectiveness of alternate retrofit configurations, different scenarios of GIB systems are numerically analyzed, expected repair costs for various levels of seismic intensity are computed, and cost benefit values are compared to each other and to those obtained when the building is strengthened and stiffened at the ground floor using conventional methods. Results show that GIB retrofit solutions are likely to represent significant cost benefits compared to traditional retrofit solutions. The results also indicate that GIBs do not need to be positioned at all column locations of the soft story, which could be beneficial in reducing the overall retrofit cost and improving architectural functionality of the retrofitted structure.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.235
Teacher spread0.215 · 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 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

Citations6
Published2016
Admission routes1
Has abstractyes

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