Design a safe firefighting time (SFT) for major fire disaster emergency response

Zhang, Yuxin; Zhang, Xiaoning; Huang, Xinyan

doi:10.1016/j.ijdrr.2023.103606

Cited by 18 publications

(3 citation statements)

References 41 publications

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“…Accordingly, their proposal compared the characteristics of various route optimization algorithms. Yuxin et al proposed a concept termed safe fire suppression time (SFT), which combines the characteristics of fire suppression and evacuation behavior to ensure firefighter safety [19]. In addition, the available safe firefighting time (ASFT) and the required safe firefighting time (RSFT) were defined, and detailed calculation methods were provided.…”

Section: Fire Safety Managementmentioning

confidence: 99%

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

Park,

Lee,

Jang

et al. 2023

Buildings

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The scale of human accidents and the resultant damage has increased due to recent large-scale urban (building) fires, meaning there is a need to devise an effective strategy for urban disasters. In the event of a fire, it is difficult to evacuate in the early stages due to the loss of detection function, difficulty in securing visibility, and confusion over evacuation routes. Accordingly, for rapid evacuation and rescue, it is necessary to build a city-level fire safety service and digital system based on smart technology. In addition, both forest and building fires emit a large amount of carbon dioxide, which is the main cause of global warming. Therefore, we need to prepare both energy and fire management to achieve carbon neutrality by 2030. In this study, we developed an AI-based smart fire safety system for efficient urban integrated management using a city-based fire safety architecture. In addition, we designed a fire management infrastructure and an energy management system for buildings. The proposal was demonstrated by building a test bed in the A building, and the AR-based mobile/web application was tested for optimized evacuation management. Furthermore, AI-based fire detection and the optimal evacuation of occupants were implemented through deep learning-based fire information data analysis. As a result, this paper presents four points for safety and energy management, and we demonstrate that the optimization of occupant evacuation ability and energy saving can be achieved. We also analyze the efficiency of the data transfer rate to prevent data communication delays by using Virtual Edge Gateway (VEG) management. In the future, we expect that the appearance of future fire and energy management buildings through this research will produce more accurate data prediction technology and the development of cutting-edge smart technology in smart city infrastructures.

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Section: Fire Safety Managementmentioning

confidence: 99%

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

Park,

Lee,

Jang

et al. 2023

Buildings

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“…-Firefighting: To help firefighters put out flames in hot, toxic conditions, firefighting robots are being created. These robots reduce the risk to firefighters by entering burning buildings, moving through smoke-filled spaces, and performing functions including water spraying, gas detection, and victim identification [32].…”

Section: Robotics and Automation In Dangerous Environmentsmentioning

confidence: 99%

Discussing Artificial Intelligence's Role in Combatting the COVID-19 Pandemic: A Review

2023

Mesopotamian Journal of Artificial Intelligence in Healthcare

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The COVID-19 pandemic is considered one of the most dangerous diseases that have emerged in recent years, as it began at the end of 2019 in China and quickly spread to become a severe epidemic. This virus has become, in a short period of time, threatening millions of the earth's populations, and infected individuals show symptoms that may lead to the loss of their lives. In this article, the author concentrates on the significance and methods of artificial intelligence strategies in facing COVID-19 and the primary role of these technologies. AI techniques have been applied in various areas, including creating diagnostic tools, predicting disease transmission, identifying new treatments, and improving resource allocation. Moreover, these techniques have been employed to analyze medical images to find symptoms of COVID-19 in patients, speeding up diagnosis and reducing transmission risks. Public health professionals can more efficiently allocate resources thanks to machine learning models that have been trained on data from other pandemics to anticipate the spread of COVID-19 and identify possible outbreaks. Large datasets of currently available medications have also been analyzed using AI to find potential new molecules.

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“…Smoke accumulation reduces visibility and hinders firefighter movements. Thermal imagers cannot detect the occupants trapped in vehicles (Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Firefighters’ behavior via vertical shafts in natural-ventilated tunnels

Zhang,

Rui,

Zhu

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Tunnel load traffic flows day and night, and is highly susceptible to catching tunnel fires. When a tunnel is on fire, occupants are endangered by the rapid deterioration of the tunnel environment during burning, and their self-evacuation is limited by their unfamiliarity with tunnels. Therefore, efficient and effective firefighting and rescue methods are urgently required to guarantee the safety of occupants during tunnel fires. During firefighting, one of the most important issues is that a fire brigade may encounter difficulties identifying the fire source. They have to deal with congestion and navigate cars piling into the tunnel, which wastes significant time. Therefore, this study provides an alternative approach for firefighters to access fire sources in naturally ventilated tunnels equipped with vertical shafts. This study explored three routes for firefighters from the ground to a fire source in a tunnel. The duration and speed of taking the three routes, along with the use of facilities, firefighters’ communication, and emotions, were presented and analyzed. This indicates that firefighting and rescue via vertical shafts is a feasible and optimized method for naturally ventilated tunnels because the estimated duration decreased by 56.2% (without smoke) and 60.0% (with smoke) compared to traditional routes when the minimum distance from the fire to the nearest exit is 2000 m. In addition, the study provides field experimental data on vertical firefighting in tunnels, which are expected to be used to build and calibrate models for firefighters in future studies.

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Design a safe firefighting time (SFT) for major fire disaster emergency response

Cited by 18 publications

References 41 publications

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

Discussing Artificial Intelligence's Role in Combatting the COVID-19 Pandemic: A Review

Firefighters’ behavior via vertical shafts in natural-ventilated tunnels

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