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Record W2796279682 · doi:10.11159/icgre18.153

Effect of the Cross-Sectional Rigidity on the Static and SeismicBehaviour of CSP Culverts

2018· article· en· W2796279682 on OpenAlexaffvenueabout
Ahmed Mahgoub, Hany El Naggar

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCulvertRigidity (electromagnetism)Structural engineeringGeotechnical engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

In Canada and around the world there is a growing trend to use wide span soil-steel arch structures as a substitute for the more conventional types of bridges and rigid culverts. CSP culverts (corrugated steel plate culverts) are flexible structures which gain load-bearing capacity by interaction with the surrounding engineered backfill enabling them to carry significant overburden and vehicular loads. In this paper, comprehensive finite element analyses were carried out to study the effect of changing the cross-sectional rigidity on the static and seismic behaviour of CSP culverts. First, the static behaviour of a field case study of a wide span culvert was verified. Then, the full dynamic analyses were utilized using seismic records suitable for the city of Vancouver in Western Canada. Subsequently, the culvert's cross-sectional properties were changed to examine their effect on the culvert's performance. Finally, the results of the finite element analyses were compared with the simplified design equations of the Canadian Highway Bridge Design Code (CHBDC). This study shows that the culvert's cross-sectional rigidity has a significant effect on the soil structure interaction (SSI) between the culvert and the surrounding backfill, which affects the performance and the load carrying capacity of the culvert.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.553

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 head0.004
GPT teacher head0.198
Teacher spread0.193 · 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 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

Citations6
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207