Work

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

Year: 2024

Type: article

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.

Source: Engineering Applications of Artificial Intelligence

DOI: 10.1016/j.engappai.2024.109571

Authors: Luca Possidente, Carlos Couto

Institutions: University College London, Riscos e Sustentabilidade na Construção, University of Aveiro

Language: English

Topic: Structural Load-Bearing Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,270

Funders: UK Research and Innovation, Fundação para a Ciência e a Tecnologia