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Record W4400455263 · doi:10.1061/jpcfev.cfeng-4525

Displacement-Based Seismic Design and Assessment of Friction-Dissipating Light-Timber Frames Coupled with a Self-Centering CLT Wall

2024· article· en· W4400455263 on OpenAlexaff
Konstantinos Skandalos, Anastasios Sextos, Solomon Tesfamariam

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2024
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of WaterlooBGC Engineering (Canada)
Fundersnot available
KeywordsStructural engineeringDisplacement (psychology)EngineeringSeismic analysisGeotechnical engineering

Abstract

fetched live from OpenAlex

A dual structural system for low-to-medium-rise buildings is examined, comprising light-timber frames (LTF) coupled with a cross-laminated timber (CLT) wall. To enhance the energy-dissipating capacity of LTF featuring pinching behavior, friction sheathing-to-frame connections have been proposed in place of conventional nail connectors. The resulting friction LTFs (FLTF) exhibit sustainably rich hysteresis loops that significantly enhance energy dissipation capacity. Nevertheless, the friction-dissipating mechanism leads to nonuniform story drift distributions and residual drifts in multistory FLTF buildings. To address this issue, a CLT wall with self-centering hold-down connections is coupled to the multistory FLTF building for imposing uniform story drifts and for reducing residual drifts. A direct displacement-based design (DDBD) approach is employed to design the dual CLT-FLTF system and ensure (i.e., impose) uniform seismic demand across the height of the building. Nonlinear-time-history analysis (NTHA) and incremental dynamic analysis (IDA) show that the DDBD approach can lead to safe designs and effectively control the displacements of the proposed dual 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 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 score0.068
Threshold uncertainty score0.538

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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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