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Record W2094503022 · doi:10.1139/l03-052

Influence of effective parameters of non-orthogonal smeared crack approach in seismic response of concrete arch dams

2003· article· en· W2094503022 on OpenAlexvenueno aff
Radin Espandar, Vahid Lotfi, A. Ghani Razaqpur

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
FundersCore Research for Evolutional Science and Technology
KeywordsArch damStructural engineeringCrackingArchFinite element methodNonlinear systemGeologyGeotechnical engineeringEngineeringMaterials science

Abstract

fetched live from OpenAlex

A rigorous and relatively efficient algorithm based on the non-orthogonal smeared crack approach is coded in a special finite element program to study the seismic response of arch dams. The formulation is briefly presented. The 130 m high Shahid Rajaee arch dam in Iran subjected to the Friuli-Tolmezzo earthquake is selected to present a practical application of the technique. Under the same geometry and loading conditions, six nonlinear analyses with different parameters are performed, and the results are compared with each other and a linear case. The varied parameters include secant and elastic unloading–reloading options, threshold angle, and tensile strength of the material. It is concluded that the non-orthogonal smeared crack approach can redistribute the state of stresses and produces a more realistic profile of stresses in the dam. A drift in the crest displacements forms the prominent characteristics of the cracking behavior. The results also suggest that the dam can suffer significant cracking during a strong earthquake and still remain stable. Moreover, the influences of the mentioned parameters in the seismic response of the dam are comprehensively discussed.Key words: nonlinear dynamic analysis, concrete arch dam, smeared crack approach.

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)0.001
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 score0.341
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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

Citations7
Published2003
Admission routes1
Has abstractyes

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