MétaCan
Menu
Back to cohort
Record W4293074062 · doi:10.11159/iccste22.162

Semi-Active Control of Building Frames subjected to Earthquakes using Smart Tendons composed of Shape Memory Alloys

2022· article· en· W4293074062 on OpenAlexvenueno aff
Alok Madan, Vimal Kumar Gupta, Arvind K. Jain

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsShape-memory alloySMA*Frame (networking)Structural engineeringSmart materialComputer scienceVibration controlNickel titaniumMaterials scienceEngineeringVibrationMechanical engineeringAcousticsComposite materialPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a concept for semi-active control of earthquake induced vibrations in building frames based on smart stressing of building frame using shape memory alloys. Smart tendons or cables composed of (Ni-Ti) shape memory alloy (SMA) wires are proposed to be installed externally with building frame elements. In principle, the smart tendons or cables can be designed such that the SMA wires are elongated beyond their plastic limit under the action of earthquake loads on the frame. Upon regulated electrical heating, the Ni-Ti SMA wires will undergo a martensite to austenite phase transformation resulting in large shrinkage strains. The strain energy thus induced can be used to generate a significantly effective control forces in the building frame. The present study proposes the implementation of smart tendons constituted with Ni-Ti SMA wires appropriately connected to building frame members which can be electrically actuated to induced desired variable control forces in building frame. The proof of concept proposed in the present study is theoretically illustrated by numerical simulations of proposed semi-active control scheme in an idealized eight story building frame model

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 score0.309
Threshold uncertainty score0.599

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicStructural Engineering and Vibration AnalysisFrench-language works237,207