Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System

Chen, Wang; Wood, Lincoln C.; Li, Heng; Aw, Zhenye; Keshavarzsaleh, Abolfazl

doi:10.1155/2018/7962952

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Moreover, AI techniques are used to predict fire spread, simulation, and optimization in modeling fire evacuations. For example, Wang [117] proposed a fire evacuation routing model called "Bee-Fire" that uses artificial bee colony optimization to find optimal evacuation routes, reducing both the clearance time and total evacuation time. In the fire spread prediction, Chetehouna et al [118] developed an artificial neural network (ANN) to predict the physical and geometrical parameters of a forest fire front, such as the rate of its spread and flame height.…”

Section: Virtual Building Layermentioning

confidence: 99%

Digital-Twin-Based Fire Safety Management Framework for Smart Buildings

Almatared,

Liu,

Abudayyeh

et al. 2023

Buildings

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In recent years, the implementation of digital twin (DT) technology has gained significant attention in various industries. However, the fire safety management (FSM) sector has been relatively slow in adopting this technology compared to other major industries. Therefore, this study aims to explore the limitations, opportunities, and challenges associated with adopting DT technology in the FSM sector and further develop a DT-based FSM framework towards smart facility management (FM). To achieve this objective, this research started by reviewing several promising DTs for FSM, including building information modeling (BIM), the Internet of Things (IoT), artificial intelligence (AI), and augmented reality (AR). On this basis, a conceptual framework was synthesized in consideration of the benefits of each technology. A questionnaire was conducted for FM professionals to evaluate the proposed framework and identify the challenges of adopting DT in the FSM sector. The survey results reveal that the proposed framework can assist decision makers in obtaining comprehensive information about facilities’ communication among stakeholders. The survey results validate the potential of the adoption of DTs toward smart FM practices in FSM. The survey results provide insights into the perception of DT technology among FM practitioners and identify the current state of DT technology in the FSM sector, its expected benefits, and its potential challenges. The main barriers to adopting DTs in FSM are a lack of knowledge about DTs, their initial costs, user acceptance, difficulties in systems integration, education training costs, a lack of competence, development complexity, difficulties in data management, and a lack of trust in data security.

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Section: Virtual Building Layermentioning

confidence: 99%

Digital-Twin-Based Fire Safety Management Framework for Smart Buildings

Almatared,

Liu,

Abudayyeh

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…The basic idea of the ant colony algorithm is based on the cooperative behavior of the evacuee to search for a safe emergency exit. In [ 16 ], the bee colony optimization approach is proposed to find the evacuation path via the idea of swarm intelligence from the bee colony. In [ 17 ], the first multi-exit evacuation algorithm is proposed to evacuate crowded people in large buildings.…”

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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“…Wang et al proposed a solution for¯re evacuation routing problems by applying arti¯cial bee colony optimization (BCO) algorithm. 7 The BCO algorithm is swarm intelligence algorithm inspired by the foraging behavior of bees. They simulated this solution for evacuation from buildings with multiple exits to improve the total evacuation time.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Base Evacuation Support System Using MANET

Taga

Matsuzawa

Takimoto

et al. 2019

Vietnam J. Comp. Sci.

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In this paper, we propose an evacuation support system that provides evacuation routes in case of disasters, and verify the usefulness of the system. Current popular wireless communication infrastructure is supported by a series of base stations and one base station handles a lot of communication. Therefore, when communication base stations break down due to disasters such as an earthquake, it may become di±cult for people to use their smartphones based on the Internet. When the communication infrastructure is paralyzed, people will have great di±culty collecting information about the conditions of transportation systems and about the safety of family and friends using smartphones. Our proposed system addresses this problem by using multiple kinds of mobile agents in addition to static agents on smartphones that use a mobile ad hoc network (MANET). The proposed system collects information with mobile agents and di®uses information via mobile agents so that the system is able to provide an optimized evacuation route for each user in a dynamically changing disaster environment. In this paper, we extend our previously proposed evacuation support system to consider elevation information when constructing evacuation routes. When a tsunami or a°ood tide occurs, low elevations may be under water. Therefore, this revised evacuation support system

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Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System

Cited by 11 publications

References 22 publications

Digital-Twin-Based Fire Safety Management Framework for Smart Buildings

Digital-Twin-Based Fire Safety Management Framework for Smart Buildings

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

Multi-Agent Base Evacuation Support System Using MANET

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