Explained fire resistance machine learning models for compressed steel members of trusses and bracing systems

Abstract

Trusses and bracing systems are usually constructed from monosymmetric and built-up cross-sections, which under compression stresses may buckle in torsional or flexural–torsional modes. In fire, this phenomenon is utterly important as failure in bracing systems or trusses may cause the collapse of buildings and result in loss of lives or severe economic impacts. Machine learning models, including neural networks, random forests and support vector machines, are developed considering a dataset with 21879 samples and are further assessed in this study as an alternative with greater accuracy and ease of application over existing design methods, namely the Eurocode 3 Part 1-2 and a recent proposal for its improvement. The machine learning models are explained using a combination of domain knowledge inference, partial dependence plots and SHapley Additive exPlanations. The accuracy versus safety trade-off is discussed for a better-informed model selection. The proposed approach and discussion create an additional confidence layer for applying these techniques for the fire design.