MétaCan
Menu
Back to cohort
Record W4309152181 · doi:10.1061/9780784484432.019

Seismic Fragility Assessment of Seismic Isolated Bridges in Cold Climates

2022· article· en· W4309152181 on OpenAlexaff
A. H. M. Muntasir Billah, Asif Iqbal

Bibliographic record

VenueLifelines 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsLakehead UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsFragilityStructural engineeringStiffnessGeotechnical engineeringSeismic loadingBearing (navigation)GeologyDissipationSeismic isolationPendulumShear (geology)EngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Seismic isolation has been extensively used around the world for seismic protection of structures. Several high seismic regions around the world experience seasonal freezing that can drastically alter isolation bearing properties and structural response under earthquake loads. Under subfreezing temperature, mechanical properties of bearings are affected as well as the properties of concrete and steel reinforcement. The objective of this study is to evaluate the seismic performance of a base isolated bridge equipped with Friction Pendulum System and Lead Rubber Bearing located in extreme cold climate. Considering the change in mechanical properties of isolation bearings such as, shear stiffness, yield strength, and friction coefficient, due to temperature change, the seismic performance of isolated bridges is evaluated. Detailed nonlinear three-dimensional finite element models of the isolated bridges considering the material properties at cold temperature are developed. Finally, fragility curves are developed for seismic vulnerability assessment of isolated bridges at subfreezing temperature.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLifelines 2022Same topicSeismic Performance and AnalysisFrench-language works237,207