Published research / software

Perforated-beam buckling prediction.

Compare beam configurations through a consistent prediction interface.

Supplied response comparisons and numerical/reference-test panels. Reference tests are not attributed to Sina. Publication · 10.12989/scs.2026.60.5.03

Role: Research author · credited developer
Period: 2026
Methods: PSO · Neural models
Status: Published research / software

Engineering software / Streamlit

A defined input.
An inspectable decision.

For engineers studying perforated composite beams

Compare beam configurations through a consistent prediction interface.

01 / INPUT

Set the engineering case

Section, opening, spacing and stability descriptors

02 / OUTPUT

Inspect the result

Predicted lateral-distortional buckling moment (kNm)

03 / DECISION

Compare the next options

Identify candidate configurations for detailed buckling assessment.

Technical basis

PSO · Neural models

Validation evidence

Published PSO-neural-model study, supplied plots and credited Streamlit source.

Current scope

Specific lateral-distortional buckling research domain; other failure modes need separate checks.

Public deployment has not been verified. The supplied interface and source establish the implementation shown.

Engineering record

Scope, method and evidence.

Published collaborative research and a supplied Streamlit prototype. The FE reference database retains its original attribution; production deployment and general buckling coverage are not established.

01Engineering question

Compare beam configurations through a consistent prediction interface.

Context: Structural steel.

02Role and documented scope

Research author · credited developer

03Methods and evidence

The published PSO/neural-model study explores lateral-distortional buckling prediction for composite perforated beams. Geometry and stability descriptors enter a supplied Streamlit interface. The collaborative FE reference database retains its source attribution.

Supplied research files support the scope shown. Authorship is distinguished from personal ownership of every dataset, test or model; an interface is not proof of current deployment.

Publication record · 10.12989/scs.2026.60.5.03

04Interpretation and limitations

No universal accuracy, engineering approval or field benefit is inferred from a selected output. Model domains and independent checks must be established for a new assignment.

Research supporting this project