Application of artificial neural networks for agent-based simulation of emergency evacuation from buildings for various purpose

Tkachuk, Konstantin; Song, Xiao; Maltseva, I. N.

doi:10.1088/1757-899x/365/4/042064

Cited by 7 publications

(4 citation statements)

References 15 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of its advantage of extracting hidden rules from real data, it has a broad range of developments in computer science and is quickly expanding to economics and medicine [28], and then to a wide range of fields. Up to now, several studies have been developed around the theme of machine learning, such as investigating the factors influencing people movement patterns during evacuation [29], detecting the trend of crowd flow during evacuation [30], creating a training system to improve evacuation capability by inducing crowd movement state through dynamic guidance signs [31], applying ANN to precisely model people's behavior during evacuation and their responses to other people and obstacles [32], and developing a rescue route planning algorithm that takes credit for all aspects of local safety performance [33]. These papers demonstrate how useful the prediction results of machine learning can be as a basis for evacuation studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

Zhou

Zhang

et al. 2023

Fire

View full text Add to dashboard Cite

A fire egress system is one of the most critical aspects of fire emergency evacuation, which is the cornerstone technology of building fire safety. The high-rise teaching buildings on campus, where vast crowds of people gather, need to be qualified for rapid evacuation in the event of a fire especially. Conventional teaching building egress system design places more emphasis on individual elements (e.g., stairwells, evacuation doors, and evacuation walkways) rather than on their co-regulation as a whole. Furthermore, there are not enough holistic and effective optimal design strategies, which is because most of the existing studies rely on experiments or simulations and often suffer from a lack of sufficient data to fully reveal the interactions of individual variables. In this study, the co-effectiveness of stairwells, walkways, and room doors in reducing total evacuation time was investigated by simulation and machine learning. We selected a typical high-rise teaching building as an example and integrated two simulation software, Pyrosim and Pathfinder, to compare the available safe evacuation time (ASET) and required safe evacuation time (RSET). Then, a framework consisting of five factors—stair flight width (SFW), stairwell door width (SDW), corridor width (CW), room door width (RDW), and location of the downward stair flight (LDSF)—was established for the optimization through statistical analysis of big data obtained by the preferred machine learning algorithm. Results indicate that (1) By modifying just one factor (SFW), the total evacuation time (TET) can be reduced by at most 12.1%, with the mortality rate dropping from 26.5% to 9.5%; (2) although ASET could not be achieved either, among 4000 cases of multi-factor combinations, a maximum TET improvement degree, 29.5%, can be achieved for the evacuation optimization compared to baseline model, with a consequent reduction in mortality to 0.15%; (3) it shows that the emphasis of the egress system optimization is on the geometric features of the evacuation stairwell; furthermore, the multi-factor combination approaches have better compromised evacuation performances than the single-factor controlled schemes. The research results can be applied as rational design strategies to mitigate fire evacuation issues in high-rise teaching buildings and, in addition, the methodology suggested in this paper would be suitable to other building types.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

Zhou

Zhang

et al. 2023

Fire

View full text Add to dashboard Cite

show abstract

“…Real world evacuation data were exploited for the training of a deep neural network that predicted the human behavior, depending on the surrounding situation. In 2018, Tkachuk et al presented a program that solved practical problems connected with emergency evacuation from buildings using system simulation based on Artificial Neural Networks (ANNs) (Tkachuk et al, 2018). In 2019 Sharm et al, proposed the first fire evacuation environment based on the OpenAI gym 12 (Brockman et al, 2016) (Sharma et al, 2020).…”

Section: Crisis Simulationmentioning

confidence: 99%

Crowd simulation for crisis management: The outcomes of the last decade

Sidiropoulos

Kiourt

Moussiades

2020

Machine Learning with Applications

View full text Add to dashboard Cite

“…Facing the dynamic situation, the authors in references [34][35][36][37] used dynamic planning to search for the optimal paths and informed everyone on the ship how to move toward the exit. Neural networks are good at learning the hidden knowledge from sample data, and the authors in [38][39][40][41] used neural networks to decide which paths are available for people to evacuate dynamically. Because the smog keeps spreading during the fire accident, feasible evacuation paths should consider the future changes of dangerous areas.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Path Optimization with Real-Time Information for Emergency Evacuation

Zhang

Zhao

Cheng

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to find the optimal path for emergency evacuation, this paper proposes a dynamic path optimization algorithm based on real-time information to search the optimal path and it takes fire accident as an example to introduce the algorithm principle. Before the accidents, it uses the Dijkstra algorithm to get the prior evacuation network which includes evacuation paths from each node to the exit port. When the accidents occur, the evacuees are unable to pass through the passage where the accident point and the blocking point are located, then the proposed method uses the breadth-first search strategy to solve the path optimization problem based on the prior evacuation network, and it dynamically updates the evacuation path according to the real-time information. Because the prior evacuation network includes global optimal evacuation paths from each node to the exit port, the breadth-first search algorithm only searches local optimal paths to avoid the blockage node or dangerous area. Because the online optimization solves a local pathfinding problem and the entire topology optimization is an offline calculation, the proposed method can find the optimal path in a short time when the accident situation changes. The simulation tests the performances of the proposed algorithm with different situations based on the topology of a building, and the results show that the proposed algorithm is effective to get the optimal path in a short time when it faces changes caused by the factors such as evacuee size, people distribution, blockage location, and accident points.

show abstract

Application of artificial neural networks for agent-based simulation of emergency evacuation from buildings for various purpose

Cited by 7 publications

References 15 publications

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

Crowd simulation for crisis management: The outcomes of the last decade

Dynamic Path Optimization with Real-Time Information for Emergency Evacuation

Contact Info

Product

Resources

About