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Seismic performance of reinforced concrete (RC) shear walls with and without damped outriggers and controlled rocking

2025· article· en· W4407778157 on OpenAlexafffund
Lisa Tobber, T.Y. Yang, Fawad Ahmed Najam

Bibliographic record

VenueEngineering Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicSeismic and Structural Analysis of Tall Buildings
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersState Key Laboratory for Disaster Reduction in Civil EngineeringNational Natural Science Foundation of ChinaCanadian Institute of Steel Construction
KeywordsStructural engineeringReinforced concreteShear wallShear (geology)Geotechnical engineeringMaterials scienceGeologyEngineeringComposite material

Abstract

fetched live from OpenAlex

Reinforced concrete shear wall system (RCSW) is one of the most prevalent lateral force-resisting systems in high-rise buildings worldwide. With increasing demands for superior seismic performance , constructability , and cost-effectiveness, various new technologies have been proposed in recent years. Among these, the controlled outriggered rocking wall (CORW) system is developed as a promising solution to improve the seismic performance of conventional RC shear wall systems. CORW system combines a damped outrigger (at roof level) and a controlled rocking (at the base) to a conventional RC shear wall system. In this study, the seismic performance of the conventional RC shear wall system is compared with the same system with a damped outrigger (at roof level), the same system with controlled rocking (at the base) and the CORW system. The results show that the seismic performance of conventional RC shear wall system can be significantly improved by introducing either, (a) damped outrigger at roof level, (b) controlled rocking at base level, or using the proposed CORW system.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.002
GPT teacher head0.180
Teacher spread0.178 · 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

Citations10
Published2025
Admission routes2
Has abstractyes

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