Beyond Digital Representation, pages 363-384

Applying Machine Learning to Evaluate the Performance of Thin-Walled Steel Members in Fire

Qi Tong ¹

Carlos Couto ²

Thomas Gernay ¹

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Department of Civil and Systems Engineering, Johns Hopkins Univesity, Baltimore, USA |

Civil Engineering Department, RISCO, University of Aveiro, Aveiro, Portugal |

Publication type: Book Chapter

Publication date: 2024-01-25

Springer Nature

Journal: Beyond Digital Representation

SJR: —

CiteScore: —

Impact factor: —

ISSN: 27317269, 27317277

DOI: 10.1007/978-3-031-48161-1_15

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Abstract

Structural fire safety is crucial for building design and construction. Fires in buildings can affect strength and stability of structures and cause devastating consequences on human life and property.

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Thin-Walled Structures	Thin-Walled Structures, 1, 50% Thin-Walled Structures 1 publication, 50%
Fire Safety Journal	Fire Safety Journal, 1, 50% Fire Safety Journal 1 publication, 50%
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Elsevier	Elsevier, 2, 100% Elsevier 2 publications, 100%
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Publisher

Springer Nature

Journal

Beyond Digital Representation

SJR

—

CiteScore

—

Impact factor

—

ISSN

27317269 (Print)

27317277 (Electronic)