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Record W7115733796 · doi:10.71846/18-wcee-1616

LOW-COST SEISMIC ISOLATION IN CANADA

2025· article· en· W7115733796 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIsolation (microbiology)Seismic isolationPerspective (graphical)ScrapBase isolation

Abstract

fetched live from OpenAlex

Research interest in low-cost seismic isolation technology has been increasing over the past two decades. Activity in this area has led to the development of numerous unique low-cost isolation systems (e.g., unbonded fiber-reinforced elastomeric isolators, flat sliders, scrap tire pad isolators, and composite systems that combine two or more types of isolators). Often, the term ‘low-cost’ is used to specifically refer to the cost of the isolation device. The research community has comparatively overlooked other aspects of the life-cycle cost of a low-cost isolation system such as the performance, maintenance and replacement, design, and prototype testing requirements. Canada has severely lagged other countries in terms of application of seismic isolation technology. Most single-family residential structures in Canada are designed and constructed according to Part 9 of Division B of the National Building Code of Canada (or a similar provincial standard) using non-engineering methods. Such structures perform well from a life-safety perspective but may sustain severe economic losses during an earthquake. Currently, application of seismic isolation requires comprehensive engineering and prototype testing which form substantial perceived cost barriers. In this paper, these cost barriers and a proposed pathway to widespread low-cost application of seismic isolation technology in Canada are reviewed. These challenges are then presented from the perspective of developing and underdeveloped countries. Areas of future research are identified and discussed.

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 score0.397
Threshold uncertainty score0.938

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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
Published2025
Admission routes1
Has abstractyes

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