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Record W2025442325 · doi:10.1117/12.919866

Seismic fragility assessment of concrete bridge pier reinforced with shape memory alloy considering residual displacement

2012· article· en· W2025442325 on OpenAlexaff
A. H. M. Muntasir Billah, M. Shahria Alam

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPierFragilityStructural engineeringSMA*Shape-memory alloyPlastic hingeBridge (graph theory)Displacement (psychology)Vulnerability assessmentStiffnessGeologyHingeComputer scienceGeotechnical engineeringMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Post-earthquake rapid recovery of bridge is one of the prime objectives for performance based design. Shape Memory Alloy (SMA) has the unique ability to undergo large deformation, but can regain its undeformed shape through stress removal (i.e. superelasticity), which brings about an added advantage in seismic regions. In an attempt to reduce permanent damage of concrete bridges, a hybrid RC bridge pier configuration is presented here. In the proposed configurations of bridge piers, the plastic hinge region is reinforced with SMA and the remaining portion with regular steel. Residual displacement is a critical parameter for performance based earthquake engineering as it dictates the functionality of a member after an earthquake. This paper evaluates fragility-based seismic vulnerability of SMA reinforced concrete bridge pier considering residual displacement. Fragility curves have also been used to assess the relative performance of SMA with conventional steel RC bridge pier. Probabilistic Seismic Demand Model (PSDM) has been used in generating the fragility functions. The development of these fragility curves for bridge piers aid in expressing the potential impact of SMA on the bridge pier vulnerability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.237
Teacher spread0.225 · 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.

Study designBench or experimental
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

Citations8
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSeismic Performance and AnalysisFrench-language works237,207