About SteelPredictor

Machine learning powered engineering tools for structural analysis and design — built on peer-reviewed research, validated by experiments, and trusted by engineers.

5
Software Tools
Peer-Reviewed Research
High-Impact Journal Publications
Instant Predictions

Who We Are

SteelPredictor is a research-oriented technology platform dedicated to delivering machine learning powered engineering tools for structural analysis and design. Founded by experienced researchers and structural engineers, we bridge the gap between advanced scientific knowledge and practical engineering workflows through transparent, high-accuracy, and academically validated software.

What We Do

We specialize in data-driven tools that transform traditional structural engineering processes. Our software eliminates the need for finite element simulations or physical testing by providing instant, interpretable, and engineering-grade predictions. Each tool we build is grounded in rigorous experimental data and scientific publication.

Our Products

Every tool is derived from published research and designed for real engineering workflows.

SteelTemp

High-temperature steel strength

Estimates the residual tensile strength and Eurocode/AISC-compliant reduction factors of steels exposed to elevated temperatures.

Inputs: Temperature (°C), thickness (mm), steel type
Outputs: Tensile strength (MPa), reduction factor (χ), stress–temperature charts
Published in Results in Engineering, 2025 — DOI: 10.1016/j.rineng.2025.104242
SigmaBuckling

Impact-induced stress in steel columns

Predicts peak stress and deformation in multi-cell steel columns subjected to axial impact, using a simulation-trained ML model.

Inputs: Cell geometry, wall thickness, steel yield strength, impact velocity
Outputs: Peak axial force, shortening, stress distribution, velocity charts
Published in International Journal of Impact Engineering, 2025 — DOI: 10.1016/j.jcsr.2025.109458
OmegaBuckling

Buckling capacity of rack uprights

Forecasts the axial buckling load of cold-formed storage rack uprights, using geometry-based regression models.

Inputs: Cross-section dimensions, moment of inertia, torsional constant, yield strength
Outputs: Critical buckling load (kN), feature contributions, report exports
Published in Results in Engineering, 2025 (In Press) — DOI: 10.1016/j.jcsr.2025.109458
StiffPredict

Connection stiffness prediction

Predicts the rotational stiffness of rack beam-to-upright connections using machine learning, replacing costly physical connection tests.

Published in Challenge Journal of Structural Mechanics, 2026 — DOI: 10.20528/cjsmec.2026.02.002
BeamCap

Beam load-carrying capacity

Instantly calculates a beam’s load-carrying capacity based on span (L), section, and material properties.

Built on OpenSees — the trusted open-source structural analysis framework (opensees.berkeley.edu)

Scientific Validation & Credibility

Each software tool is derived from peer-reviewed scientific research and validated through experimental testing and finite element analysis. We do not offer black-box tools — every result is traceable, explainable, and referenced.

  • Underlying models fully published in international academic journals
  • Validated against experimental testing and finite element analysis
  • Transparent, interpretable predictions — no black boxes
  • Every tool built only after its research was published in a high-impact academic journal

Who Uses Our Tools?

Our software serves professionals and institutions across the structural engineering field.

Structural engineers & consultants
Fire safety professionals
Impact mechanics researchers
Academic institutions & graduate programs
Infrastructure & industrial design firms

Licensing & Support

All software is offered on an annual subscription basis:

  • 12-month full access
  • All updates and improvements included
  • CSV / PDF report export
  • Email-based technical support

Academic discount is not available.

Our Vision

We believe machine learning should empower engineers, not replace them. Our tools are designed to complement professional judgment, accelerate decision making, and reduce design uncertainty. With SteelPredictor, engineers gain instant access to validated predictive models — helping them design safer, more efficient structures, faster.

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