Temperature field model for large spatial structures: experiments, simulation and ANN prediction
File(s)
Author(s)
Wu, Yiwen
Fan, Shenggang
Zhang, Minze
Gardner, Leroy
Type
Journal Article
Abstract
Large spatial stainless steel structures can be susceptible to accidental events such as fires, given their high occupancy, wide range of combustible materials and diverse usage. Consequently, a comprehensive investigation into the temperature field in large spatial stainless steel structures in the event of a fire has been conducted, encompassing experiments, simulations, assessments and predictions. Eight scaled temperature field tests were performed using a pool fire as the fire source, a common scenario in fire incidents. Building on the completed tests, a calibrated CFD model was developed using the Fire Dynamics Simulator (FDS) software and employed to further analyse the temperature field in large spatial structures under various fire powers and radii. A total of 8064 sets of three-dimensional large spatial temperature field data were acquired. Existing temperature field models, both from codes and other research studies, were evaluated against a substantial dataset. The results indicated that current models tend to be conservative, especially in areas near the fire source. In response to these findings, a novel approach utilizing Artificial Neural Networks to predict the 3D spatial temperature field in large spatial stainless steel structures is introduced. In addition, compared with the complex calculation formulae of traditional models, the model proposed herein based on Artificial Neural Networks is more convenient to use in practice and exhibits better accuracy.
Date Issued
2025-05-01
Date Acceptance
2025-02-10
Citation
Journal of Constructional Steel Research, 2025, 228
ISSN
0143-974X
Publisher
Elsevier
Journal / Book Title
Journal of Constructional Steel Research
Volume
228
Copyright Statement
Copyright © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Article Number
109425
Date Publish Online
2025-02-20