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Record W2034093666 · doi:10.1193/1.3280115

Seismic Vulnerability Assessment of Reinforced Concrete Buildings Using Hierarchical Fuzzy Rule Base Modeling

2010· article· en· W2034093666 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEarthquake Spectra · 2010
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of OttawaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsVulnerability assessmentVaguenessVulnerability (computing)Fuzzy logicPrioritizationFuzzy ruleFuzzy setComputer scienceHeuristicEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A reliable building vulnerability assessment is required for developing a risk‐based assessment and retrofit prioritization. Tesfamariam and Saatcioglu (2008) proposed a simple building vulnerability module where the building performance modifiers are in congruence with FEMA 154 . This paper is an extension of the building vulnerability assessment that include detailed performance modifier in congruence with FEMA 310 that is represented in a heuristic based hierarchical structure. Some of the input parameters are obtained through a walk down survey and are subject to vagueness uncertainty that is modelled through fuzzy set theory. A knowledge base fuzzy rule base modeling is developed and illustrated for reinforced concrete buildings damaged in the 1 May 2003 Bingöl, Turkey earthquake.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.302
Teacher spread0.281 · 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