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Record W4412754673 · doi:10.11159/iccste25.198

Numerical Investigation of Optimized Chamfered Concrete-Filled Steel Tubular Columns under Axial and Lateral Loads

2025· article· en· W4412754673 on OpenAlexvenueno aff
Janhavi Singh, K. K. Pathak, Ganga Prakhya, Ishan Jha

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceStructural engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

The study presents a new iterative corner-chamfering strategy to optimize tapered Concrete-Filled Steel Tubular (CFST) columns under axial and lateral loads.Finite element simulations examine square, octagonal, and chamfer-optimized sections at taper angles of 0, 2, and 4.Through systematic iteration, a 14.65% chamfer level is identified that exceeds the unchamfered square column's axial strength by approximately 1.6%, while consuming less material.Compared to the octagonal column, this chamfer-optimized geometry provides a 20% improvement in axial capacity and up to 16% higher lateral capacity.As the taper angle increases, both axial and lateral strengths consistently rise, with the chamfer-optimized column demonstrating a 31% enhancement in axial capacity from 0 to 4, alongside a near 100% escalation in lateral load capacity.Nonetheless, these gains accompany a modest reduction in ductility, attributed to the stiffening effect of taper.Overall, this research underscores that precisely controlled corner-chamfering, in tandem with tapered geometry, effectively augments CFST columns' load-bearing capacity, achieving a balance between strength and ductility.

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 score0.627
Threshold uncertainty score0.591

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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