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This paper describes an experimental study on the behavior of load-bearing cold-formed steel (CFS) members under elevated temperatures from fire exposure. A custom-built electrical furnace with six independently controlled heating zones was installed in a loading frame, enabling testing of CFS members under uniform or non-uniform elevated temperatures. The ability to precisely control temperature gradients between the flanges allows testing a single member to failure under thermal conditions representative of a wall assembly exposed to fire on one side, capturing the effect of thermal gradient on the buckling behavior. Steady-state tests on short (18 in.) 600S200- 54 lipped channels in compression were conducted at temperatures up to 600 °C, including tests with uniform temperatures and tests with 100°C gradient between the two flanges. Coupon tests were also conducted to characterize the material properties at elevated temperatures. The members lost about 23% of their strength at 400°C and 66% at 600 °C. For these short specimens under nonuniform heating, with one flange 100°C hotter than the other, the member strength fell between the strengths associated with the temperatures of the hot and cold flanges, with limited asymmetric effect on the local buckling response. This experimental data can support the development of design methods for CFS members in fire, enabling performance-based fire design.
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This content will become publicly available on June 1, 2026
Strength of thin-walled steel beams under non-uniform temperature: Analytical and machine learning models
The resistance of thin-walled steel beams in fire is governed by a complex interaction between the buckling of the plates and the lateral-torsional buckling (LTB) of the member, combined with the temperature-induced reduction of steel properties. Besides, in many applications, steel beams are subjected to non-uniform thermal exposure which creates temperature gradients in the section. There is a lack of analytical design methods to capture the effects of temperature gradients on the structural response, which leads to overly conservative assumptions thwarting optimization efforts. This paper describes a study on the strength of thin-walled steel beams subjected to constant bending moment in the major-axis and thermal gradients through analytical and Machine Learning (ML) methods. A parametric heat transfer analysis is conducted to characterize the thermal gradients that develop under three-sided fire exposure. Nonlinear finite element simulations with shells are then used to generate the resistance dataset. Results show that the use of the Eurocode model with a uniform temperature taken as the hot flange temperature severely underestimate the moment strength with an R^2 of 0.61. The ML models, trained using physically defined features, are far superior to the Eurocode methods in predictive capacity. The ML-based models can be used to improve existing design methods for non-uniform temperature distributions.
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- Award ID(s):
- 2237623
- PAR ID:
- 10587724
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Fire Safety Journal
- Volume:
- 153
- Issue:
- C
- ISSN:
- 0379-7112
- Page Range / eLocation ID:
- 104357
- Subject(s) / Keyword(s):
- Machine learning Thin-walled beams Fire Temperature gradient Shell finite elements Lateral-torsional buckling Local buckling
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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