SmartEscape: A Mobile Smart Individual Fire Evacuation System Based on 3D Spatial Model

Atila, Ümit; Ortakcı, Yasin; Özacar, Kasım; Demiral, E.; Karaş, İsmail Rakıp

doi:10.3390/ijgi7060223

Cited by 29 publications

(16 citation statements)

References 16 publications

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“…Carbon monoxide (CO) is a dangerous gas emitted during a fire and, according to Pu and Zlatanova [40], is responsible for more than 50% of the deaths in fires. Atila et al [41] indicates that there will be no negative effect on human health when the CO density is less than 50 particles per million (ppm). A CO density between 50 ppm and 100 ppm will negatively impact human health, and people will lose consciousness if the CO density reaches between 100 ppm and 3200 ppm and the duration of inhalation is more than 1 h. When the CO density is between 3200 ppm and 12,600 ppm, people will lose consciousness within 30 min.…”

Section: Methodsmentioning

confidence: 99%

A Framework for an Intelligent and Personalized Fire Evacuation Management System

Guo

Xiong²,

Liu

et al. 2019

Sensors

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Many research studies have focused on fire evacuation planning. However, because of the uncertainties in fire development, there is no perfect solution. This research proposes a fire evacuation management framework which takes advantage of an information-rich building information modeling (BIM) model and a Bluetooth low energy (BLE)-based indoor real-time location system (RTLS) to dynamically push personalized evacuation route recommendations and turn-by-turn guidance to the smartphone of a building occupant. The risk score (RS) for each possible route is evaluated as a weighted summation of risk level index values of all risk factors for all segments along the route, and the route with the lowest RS is recommended to the evacuee. The system will automatically re-evaluate all routes every 2 s based on the most updated information, and the evacuee will be notified if a new and safer route becomes available. A case study with two testing scenarios was conducted for a commercial office building in Tianjin, China, in order to verify this framework.

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Section: Methodsmentioning

confidence: 99%

A Framework for an Intelligent and Personalized Fire Evacuation Management System

Guo

Xiong²,

Liu

et al. 2019

Sensors

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“…A ZigBee-based localization module that identifies people's positions and understands whether they are evacuating was proposed by D'Orazio et al [36]. The systems described by Jiang [37] and Atila et al [38] both employed purposely deployed RFID beacons to collect personnel localization in buildings. Furthermore, personal localization using Global Positioning System (GPS) and Wi-Fi access points were also mentioned by Dominicis et al [39].…”

Section: Review Of the State-of-the-art Smart Building Fire Evacuatio...mentioning

confidence: 99%

Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era

Fang

2021

Buildings

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Complex and tall buildings have been constructed in many cities recently. Fire safety should be a major concern of building designers, engineers, and governments. Previous fire experience has made us understand the importance of acquiring fire-ground information to facilitate firefighting operations, evacuation processes, rescues, etc. Recently, the rapid advancement in Information Technology, Data Analytics, and other detection and monitoring systems has provided the basis for fire safety researchers to re-think fire safety strategies in the built environment. Amongst all fire safety studies, evacuation in tall buildings, including elevator evacuations, has attracted much attention. IoT-aided building fire evacuation is a new concept of the building evacuation mode, which improves the building evacuation process by making decisions of escape based on the real-time fire-ground information, such as the fire environment and occupant situations. Focusing on IoT applications in building fire evacuation, this paper explores the advantages and insufficiencies of current smart building fire evacuation systems. A conceptual design of an IoT-aided building fire evacuation control system is described. The system is introduced in the sequence of information needs, information sources and data transmission, and potential services and applications. Finally, new insights into promising 5G technologies for future building fire evacuations are discussed.

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“…The fire evacuation model in large buildings needs to address more on the building architecture and floor plan that determines the evacuation routes. Artificial neural network (ANN) based fire evacuation algorithm is proposed in [ 34 ] to identify the best evacuate route by calculating the risk value on each link. However, fire evacuation planning by ANN algorithm or reinforcement learning algorithm is a challenging problem because there is no standard benchmark environment to train these artificial intelligence agents.…”

Section: Related Workmentioning

confidence: 99%

Time-Aware and Temperature-Aware Fire Evacuation Path Algorithm in IoT-Enabled Multi-Story Multi-Exit Buildings

Yen

Lin

Tsao

2020

Sensors

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Temperature sensors with a communication capability can help monitor and report temperature values to a control station, which enables dynamic and real-time evacuation paths in fire emergencies. As compared to traditional approaches that identify a one-shot fire evacuation path, in this paper, we develop an intelligent algorithm that can identify time-aware and temperature-aware fire evacuation paths by considering temperature changes at different time slots in multi-story and multi-exit buildings. We first propose a method that can map three-dimensional multi-story multi-exit buildings into a two-dimensional graph. Then, a mathematical optimization model is proposed to capture this time-aware and temperature-aware evacuation path problem in multi-story multi-exit buildings. Six fire evacuation algorithms (BFS, SP, DBFS, TABFS, TASP and TADBFS) are proposed to identify the efficient evacuation path. The first three algorithms that do not address human temperature limit constraints can be used by rescue robots or firemen with fire-proof suits. The last three algorithms that address human temperature limit constraints can be used by evacuees in terms of total time slots and total temperature on the evacuation path. In the computational experiments, the open space building and the Taipei 101 Shopping Mall are all tested to verify the solution quality of these six algorithms. From the computational results, TABFS, TASP and TADBF identify almost the same evacuation path in open space building and the Taipei 101 Shopping Mall. BFS, SP DBFS can locate marginally better results in terms of evacuation time and total temperature on the evacuation path. When considering evacuating a group of evacuees, the computational time of the evacuation algorithm is very important in a time-limited evacuation process. Considering the extreme case of seven fires in eight emergency exits in the Taipei 101 Shopping Mall, the golden window for evacuation is 15 time slots. Only TABFS and TADBFS are applicable to evacuate 1200 people in the Taipei 101 Shopping Mall when one time slot is setting as one minute. The computational results show that the capacity limit for the Taipei 101 Shopping Mall is 800 people in the extreme case of seven fires. In this case, when the number of people in the building is less than 700, TADBFS should be adopted. When the number of people in the building is greater than 700, TABFS can evacuate more people than TADBFS. Besides identifying an efficient evacuation path, another significant contribution of this paper is to identify the best sensor density deployment at large buildings like the Taipei 101 Shopping Mall in considering the fire evacuation.

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SmartEscape: A Mobile Smart Individual Fire Evacuation System Based on 3D Spatial Model

Cited by 29 publications

References 16 publications

A Framework for an Intelligent and Personalized Fire Evacuation Management System

A Framework for an Intelligent and Personalized Fire Evacuation Management System

Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era

Time-Aware and Temperature-Aware Fire Evacuation Path Algorithm in IoT-Enabled Multi-Story Multi-Exit Buildings

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