Application of the tunnel fire scenario dynamic reconstruction based on Intelligent Disaster Prevention Platform in Shanghai

Abstract

Effective evacuation and fire-fighting response require an accurate, prompt and dynamic reconstruction and visualization of tunnel fire scenarios. This paper aims to introduce the application of the Intelligent Disaster Prevention Platform integrating tunnel fire knowledge with the machine learning approach in Shanghai tunnels. The platform can provide key fire parameters consist of fire location, real-time heat release rate, temperature distribution and smoke movement, and reconstruct the tunnel fire scenario. The platform uses machine learning algorithms and tunnel fire knowledge base to conduct in-depth mining and analysis of multi-source heterogeneous monitoring data of tunnel disaster prevention equipment, including four modules: environmental parameters perception, intelligent fire warning, fire scenarios reconstruction and personnel dynamic evacuation, so as to provide scientific decision support for intelligent fire firefighting. Meanwhile, the platform has been demonstrated and applied in many urban underground roads in Shanghai, such as Hongmei South Road Tunnel, Yan’an East Road Tunnel and Dalian Road Tunnel, which significantly reduces the false alarm rate of tunnel fires and provides early warning in fire accidents. The relevant experience can provide guidance for underground infrastructure.