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Record W3164753589 · doi:10.1186/s43251-021-00036-9

Effect of bonding or unbonding on seismic behavior of bridge elastomeric bearings: lessons learned from past earthquakes in China and Japan and inspirations for future design

2021· article· en· W3164753589 on OpenAlexaff
Nailiang Xiang, Yoshiaki Goto, M. Shahria Alam, Jianzhong Li

Bibliographic record

VenueAdvances in Bridge Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersJapan Society for the Promotion of Science
KeywordsBridge (graph theory)Bearing (navigation)ElastomerStructural engineeringSeismic loadingEngineeringForensic engineeringMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Economical laminated elastomeric bearings are well-adopted options for load transmission components of bridges from superstructure to substructure. In most cases, the design of such elastomeric bearings primarily depends on the requirements from service-level conditions such as superstructure thermal movements, with little consideration of extreme loads like earthquakes. However, bridge elastomeric bearings are very likely to be subjected to earthquake hazards, particularly for bridges located in high seismic regions. This study presents an overview of the observed typical damages of bridge elastomeric bearings in the past major earthquakes mainly in China and Japan. Comparisons of different damage patterns are conducted based on the different installations of bridge elastomeric bearings (bonding or un-bonding). The effect of bearing installation methods on the overall seismic behavior of bridges is also discussed. A desirable installation method for bridge elastomeric bearings against strong earthquake loads is recommended, which is expected to overcome the critical limitations of current design practice.

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 score0.261
Threshold uncertainty score0.820

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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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

Citations33
Published2021
Admission routes1
Has abstractyes

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