Rapid forecasting of the structural failure of a full-scale aluminium alloy reticulated shell structure in fire

Zhuojun Nan, Mhd Anwar Orabi*, Xiaoning Zhang, Aatif Ali Khan, Xinyan Huang*, Liming Jiang, Yaqiang Jiang, Asif Usmani

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


First respondents to fires in structures face severe risks as both the fire and structural behaviour are unpredictable. While structural collapse may manifest some warning signs, these signs are not always easily identified which has led to the death of many fire fighters over the years. Both fire and structural fire simulation have come a long way and are now capable of assessing the thermomechanical behaviour of structures to a good degree of accuracy. However, such simulations take hundreds or thousands of engineering and computation hours. This paper explores performing these analyses a priori and using the generated database to train a recurrent neural network for real time prediction of potential failure. The analysis is performed on an aluminium reticulated roof structure that is constructed in Sichuan Fire Research Institute (Sichuan, China) and is expected to be tested to failure in fire in 2023. One hundred localised fire scenarios were used to cover the potential fire that will be used to induce the failure of the test roof. Heat transfer analyses for each section were then performed in OpenSEES followed by thermomechanical analysis in the same software. The generated results database was then cleaned and the data at several key locations were extracted and used to train a long short term memory recurrent neural network. The results of the predictions show that the artificial intelligence model can infer results with increasing accuracy the closer the structure is to failure. The real test of the accuracy of the model, however, will be during the fire experiment on the real structure. This would be the first time an artificial intelligence model for rapid forecasting of structural response in fire is built a priori and tested against a real fire.
Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Structures in Fire
Subtitle of host publicationHosted by The Hong Kong Polytechnic University
EditorsLiming Jiang, Paulo Vila Real, Xinyan Huang, Mhd Anwar Orabi, Jin Qiu, Tianwei Chu, Zhuojun Nan, Cheng Chen, Zhiruoyu Wang, Asif Usmani
PublisherThe Hong Kong Polytechnic University
Number of pages11
ISBN (Electronic)978-962-367-869-8
Publication statusPublished - 2022
Externally publishedYes
EventSiF 2022 - The 12th International Conference on Structures in Fire - Hong Kong Polytechnic University, Hong Kong, Hong Kong
Duration: 20 Nov 20222 Dec 2022


ConferenceSiF 2022 - The 12th International Conference on Structures in Fire
Abbreviated titleSiF 2022
Country/TerritoryHong Kong
CityHong Kong
Internet address


  • structures
  • real time
  • artificial intelligence
  • recurrent neural networks
  • LSTM


Dive into the research topics of 'Rapid forecasting of the structural failure of a full-scale aluminium alloy reticulated shell structure in fire'. Together they form a unique fingerprint.

Cite this