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Record W4412754856 · doi:10.11159/iccste25.258

Seismic Vulnerability Assessment Using FEMA P-154 and Welded Mesh Reinforcement in Informal Settlements in Peru

2025· article· en· W4412754856 on OpenAlexvenueno aff
Axel Williams Chevarría Rojas

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)Informal settlementsWeldingVulnerability assessmentHuman settlementGeologySettlement (finance)Forensic engineeringSeismologyReinforcementEngineeringComputer scienceComputer securityStructural engineeringMechanical engineeringWorld Wide WebWaste managementEconomic growthMedicine

Abstract

fetched live from OpenAlex

The main objective of this study is to evaluate the seismic vulnerability of 30 self-built houses in the Juan Pablo II human settlement in San Juan de Lurigancho, Lima, Peru, which, due to their informal construction and the use of low-quality materials, present a high risk of seismic events; To do so, the FEMA-P154 methodology will be used, revealing that 31% have a risk of collapse greater than 50%.Likewise, the results of this research will allow us to identify the main structural deficiencies and propose appropriate reinforcement solutions, thus contributing to improving the safety of the inhabitants of the area and reducing human and material losses in the event of an earthquake.This study evaluates the alternative of welded mesh as a reinforcement proposal to improve the seismic behavior of the houses, which will be evaluated through dynamic and pushover analysis, using tools such as AutoCAD and Etabs to perform structural analysis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 designObservational
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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