Structural-fire responses forecasting via modular AI

Journal article (2023)

Authors

Z. Nan The Hong Kong Polytechnic University
Mhd Anwar Orabi The Hong Kong Polytechnic University
Xinyan Huang The Hong Kong Polytechnic University
Yaqiang Jiang Sichuan Fire Research Institute of Ministry of Emergency Management
Asif Usmani The Hong Kong Polytechnic University
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LSTM Artificial intelligence RNN Real time Structural response
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Published Date

2023

Language

English

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Abstract

This study analyses the structural response of an aluminium reticulated roof structure that is constructed at Sichuan Fire Research Institute (Sichuan, China), and to be tested in fire. The structural fire behaviour under 960 localised fire scenarios is considered first, and then used to construct a database for training a modular artificial intelligence (AI) system for real-time forecasting. The system consists of several AI models, each of which predicts the displacement at a specific monitoring point. These individual predictions are then combined to generate a comprehensive forecast of the global structural-fire behaviour. The individual AI model utilized is a Long Short-Term Memory Recurrent Neural Network (LSTM RNN). The modular design allows different models to be modified or added as needed, making the system flexible and adaptable, and improving the accuracy and reliability of the predictions. The results demonstrate the effectiveness of the modular AI approach in accurately forecasting fire-induced structural collapses as indicated by the sensitivity the local models can have. The key objective of this research is to help to make informed decisions and prioritize efforts to minimize the risk of structural collapse in fire.