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 aim to 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 the development of 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

SteelTemp

Predicting high temperature strength of steels using machine learning

SteelTemp estimates the residual tensile strength and Eurocode or 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

Predicting impact induced stress in multi cell steel columns

SigmaBuckling predicts peak stress and deformation in 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

Section based buckling capacity prediction of rack uprights

OmegaBuckling 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: Resources in Engineering, 2025 (In Press) DOI: 10.1016/j.jcsr.2025.109458

Scientific Validation and Credibility

Each software tool is derived from peer reviewed scientific research and validated through experimental testing and finite element analysis. All underlying models are fully published in international academic journals, ensuring scientific transparency and professional reliability. We do not offer black box tools. Every result is traceable, explainable, and referenced.

Who Uses Our Tools?

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

Licensing and Support

All software is offered on an annual subscription basis: 12 month full access All updates and improvements included CSV or PDF report export Email based technical support Academic discount is not available.

Our Vision

We believe that 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.