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Record W1976815456 · doi:10.1139/l05-119

Effect of second-order forces on seismic response

2006· article· en· W1976815456 on OpenAlexaffvenue
J. L. Humar, Mohamed Mahgoub, Mohammad Ghorbanie-Asl

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsInstabilityStiffnessStructural engineeringNonlinear systemSeismic loadingGeotechnical engineeringDisplacement (psychology)Structural stabilityHardening (computing)Amplification factorStability (learning theory)EngineeringGeologyMechanicsMaterials scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

In a building structure subjected to seismic forces, the gravity loads acting through the lateral displacements lead to additional shears and moments. This is generally referred to as the P–Δ effect; it tends to reduce the capacity of the structure to resist the seismic forces and may lead to instability. It has been suggested that an increase in structural strength, in stiffness, or in both would mitigate the P–Δ effect and ensure stability of the structure. It is shown here that instability results when the P–Δ effect causes the stiffness of the structure to become negative in the post-yield range, in which case increasing the strength, the stiffness, or both does not ensure stability. In a single-storey structure, stability can be ensured if there is sufficient strain hardening that the post-yield stiffness is positive even in the presence of the P–Δ effect. For a multistorey building the vulnerability of the structure to P–Δ instability can be judged by obtaining a pushover curve. It is shown that as long as the maximum displacement produced by the design earthquake lies in the region of positive slope of the pushover curve, the structure will remain stable.Key words: seismic response, P–Δ effect, dynamic instability, stability coefficient, amplification factor, pushover analysis, nonlinear 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.173
Teacher spread0.170 · 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

Citations16
Published2006
Admission routes2
Has abstractyes

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