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Record W2145127848 · doi:10.1002/eqe.2662

Minimal‐disturbance seismic rehabilitation of steel moment‐resisting frames using light‐weight steel elements

2015· article· en· W2145127848 on OpenAlex
Masahiro Kurata, Miho Sato, Lei Zhang, Oren Lavan, Tracy C. Becker, Masayoshi Nakashima

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2015
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsMcMaster University
FundersDisaster Prevention Research Institute, Kyoto UniversityJapan Society for the Promotion of Science
KeywordsRetrofittingRehabilitationWeldingStructural engineeringMoment (physics)EngineeringFrame (networking)Work (physics)Steel frameCivil engineeringMechanical engineeringPhysical therapy

Abstract

fetched live from OpenAlex

Summary This paper presents a rehabilitation technique developed under a design and construction scheme, termed minimal‐disturbance seismic rehabilitation . This scheme pursues enhancing the seismic performance of buildings with the intention of improving the continuity of business while minimizing obstruction of the visual and physical space of building users and the use of heavy construction equipment and hot work (welding/cutting). The developed rehabilitation technique consists of light‐weight steel elements and aims to decrease demands to beam‐ends of steel moment‐resisting frames. The behavior of the baseline model was verified through numerical analysis and proof‐of‐concept testing. Furthermore, the effectiveness of rehabilitation is studied through retrofitting a four‐story steel moment‐resisting frame originally designed with Japanese design guidelines. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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