Towards the development of a fire safety systems evaluation for public assembly buildings

The current paper presents an application of an alternative probabilistic risk assessment methodology that incorporates technical, human, and organizational risks (T-H-O-Risk) using Bayesian network (BN) and system dynamics (SD) modelling. Seven case studies demonstrate the application of this holistic approach to the designs of high-rise residential buildings. An incremental risk approach allows for quantification of the impact of human and organizational errors (HOEs) on different fire safety systems. The active systems considered are sprinklers, building occupant warning systems, smoke detectors, and smoke control systems. The paper presents detailed results from T-H-O-Risk modelling for HOEs and risk variations over time utilizing the SD modelling to compare risk acceptance in the seven case studies located in Australia, New Zealand, Hong Kong, Singapore, and UK. Results indicate that HOEs impact risks in active systems up to ~33%. Large variations are observed in the reliability of active systems due to HOEs over time. SD results indicate that a small behavioral change in ’risk perception’ of a building management team can lead to a very large risk to life variations over time through the self-reinforcing feedback loops. The quantification of difference in expected risk to life due to technical, human, and organizational risks for seven buildings for each of 16 trial designs is a novel aspect of this study. The research is an important contribution to the development of the next generation building codes and risk assessment methods.

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Smoke Simulation in an Underground Train Station Using Computational Fluid Dynamic

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.663.366 ◽

2014 ◽

Vol 663 ◽

pp. 366-372 ◽

Cited By ~ 5

Author(s):

Zambri Harun ◽

Muhammad Saiful bin Sahari ◽

Taib Iskandar Mohamad

Keyword(s):

Computational Fluid Dynamic ◽

Fire Safety ◽

Fluid Dynamic ◽

Fire Department ◽

Cfd Simulations ◽

Train Station ◽

Hazardous Gases ◽

Design Software ◽

Underground Station ◽

Safety Systems

The design of the ventilation and fire safety systems for the Johor Bahru Sentral, a semi-underground train station, part of the Integrated Custom, Immigration and Quarantine Complex (ICIQ) is based on normal Malaysian Standards (MS), British Standards and the local fire department’s requirements. However, the large and complex space in the underground station coupled with scheduled diesel-powered locomotives which frequent the station by stopping or passing require detailed simulations. Both ventilation and the fire safety systems employ Computational Fluid Dynamic (CFD) methods to provide realistic balance against the typical calculations based on spread sheets and certain design software. This study compares smoke simulations results performed by the mechanical and fire consultants with the simulations carried out through this project. An assumption of a locomotive catches fire near the main platform is made. The burning locomotive is the source of the smoke while the occupants on platforms and waiting areas are the subjects to escape safely. The process of the simulation includes modelling and meshing processes on the structure of the railway station imported from Inventor CAD Autodesk software drawing. The CFD simulations are performed using Star-CCM+. The smokes flow around the building with buoyancy forces and extracted via exhaust fans. Through these simulations, we found that when a locomotive catches fire, the passengers could evacuate the building safely before the fire department machinery arrives. Furthermore, we notice that the ventilation fans activation based on detection of hazardous gases may not be efficient way to remove the latter. A schedule clean-up sync with train arrivals effectively removes toxic gas.

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Review on Existing Fire Monitoring and Extinguishing Systems In Various Domain Verticals

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst12656 ◽

2021 ◽

Vol 23 (3) ◽

Author(s):

Naina Mahile ◽

◽

Dipali Chakole ◽

Nikita Kotangale ◽

Mitali Charde ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Fire Safety ◽

Public Awareness ◽

Fire Control ◽

Main Motivation ◽

Fire Monitoring ◽

The Public ◽

Drastic Increase ◽

Safety Systems

Fire is one of the most frequently occurring and destructive disasters and it is extremely serious hazard to people life safety. It is an undesirable mishap which emits heat, smoke or flame and gets converted in the huge fire. Over the last few years, the demand of fire safety systems has taken a drastic increase due to the public awareness. The main motivation of this paper is to review the existing fire monitoring and extinguishing systems in various verticals of the working domains. Also it gives the brief about the design of automatic sensor based fire alerts, and extinguishing system inferring the Artificial Intelligence and machine learning. The system will be able to locate the victim location and intimation to various stations to be included in the fire control the fire exposures. By implementing the proposed system in a particular area, it is possible to spot the fire within small course of time, and extinguish it without risking human lives.

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Improving Fire Safety Systems Based On Internet Of Things And Deep Learning

10.32920/17315693.v1 ◽

2021 ◽

Author(s):

Mohamed Gamaleldin

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Real Time ◽

Fire Safety ◽

Ground Truth ◽

Current Status ◽

Dynamic Nature ◽

Actual Fire ◽

People Movement ◽

Safety Systems

Structure fires are one of the main concerns for fire safety systems. The actual fire safety of a building depends on not only how it is designed and constructed, but also on how it is operated. Computational fluid dynamics software is the current solution to reduce the casualties in the fire circumstances. However, it consumes hours to provide the results in some cases that makes it hard to run in real-time. It also does not accept any changes after starting the simulation, which makes it unsuitable for running in the dynamic nature of the fire. On the other hand, the current evacuation signs are fixed, which might guide occupants and firefighter to dangerous zones.<div><br><div>In this research, we present a smoke emulator that runs in real-time to reflect what is happening on the ground-truth. This system is achieved using a light-weight smoke emulator engine, deep learning, and internet of things. The IoT sensors are sending the measurements to correct the emulator from any deviation and reflect events such as fire starting, people movement, and the door’s status. This emulator helps the firefighter by providing them with a map that shows the smoke development in the building. They can take a snapshot from the current status of the building and try different virtual evacuation and firefighting plans to pick the best and safest for them to proceed. The system will also control the exit signs to have adaptive exit routes that guide occupants away from fire and smoke to minimize the exposure time to the toxic gases<br></div></div>

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