Evacuation Path Research Articles

Dynamic Evacuation Routes Navigation for Passenger Ship Fire Based on Intelligence Algorithms

AbstractTo explore fire hazards and provide real‐time path navigation services for passenger ships, this study proposes an improved artificial fish swarm algorithm (IAFSA) to give the optimal evacuation route for evacuees when a passenger ship is on fire. First, the PyroSim software is used to simulate the fire scene on the sixth deck of the passenger ship. Through which the real‐time temperature, carbon monoxide (CO) concentration, and visibility of each exit are obtained, and the spread speed of the fire hazard area is determined. Then, the artificial fish swarm algorithm is improved by using the dynamic Visual and step, and the optimal path planning for the evacuation of passenger ships is achieved. The Visualized Evacuation Guidance System (VEGS) is developed to provide the optimal route for evacuees on the passenger ship. Finally, VEGS is applied to the fire evacuation of passenger ships. The results show that VEGS can provide the optimal evacuation path for evacuees and avoid detours in fire hazard areas compared with other algorithms. Seventy‐five percent of the evacuees who used VEGS in the event of a passenger ship can escape in a time shortened by up to 40.8%.

Online Indoor Fire Evacuation System

Many methods for indoor fire escape evacuation have been proposed to assist victims in escaping. Greedy approaches focus on finding the closest exit and providing victims with the shortest evacuation path. However, they are time consuming to respond to a dynamic fire spread. Optimal approaches determine evacuation exits and routes based on the characteristics of indoor fires using probabilistic or gravitational algorithms. However, their disadvantage is the excessive amount of computation required. Compared with previous methods, this article provides a lightweight exit decision formulation paired with dynamic path planning. The experimental results indicate that our approach accelerates the computation time by approximately 1.5 times and achieves a victim survival rate higher than 90%.

Dynamic Dose-Based Emergency Evacuation Model for Enhancing Nuclear Power Plant Emergency Response Strategies

The safe evacuation of residents near a nuclear power plant during a nuclear accident is vital for emergency response planning. To tackle this challenge, considering the dynamic dispersion of radioactive materials in the atmosphere and its impact on evacuation routes under different meteorological conditions is crucial. This paper develops a dynamic dose-based emergency evacuation model (DDEEM), which is an efficient and optimized nuclear accident evacuation model based on dynamic radiological dose calculation, utilizing an improved A* algorithm to determine optimal evacuation routes. The DDEEM takes into account the influence of radiological plume dispersion and path selection on evacuation effectiveness. This study employs the DDEEM to assess radiological consequences and evacuation strategies for students residing 5 km from a Chinese nuclear power plant. Under various meteorological conditions, including the three typical meteorological conditions, random ordered and random unordered meteorological sequences, optimal routes obtained through the DDEEM effectively reduce radiological dose exposure and mitigate radiation hazards. The results indicate that all evacuation paths generated by the DDEEM have a maximum dose of less than 1 mSv. Through simulations, the model’s effectiveness and reliability in dynamic radiological environments in terms of radiological consequences and evacuation analysis is verified. The research provides valuable insights and a practical tool for nuclear power plant emergency decision-making, enhancing emergency management capabilities during nuclear accidents. The DDEEM offers crucial technical support and a solid foundation for developing effective emergency response strategies.

Design Optimization of Building Exit Locations Based on Building Information Model and Ontology

There are usually multiple functional spaces that meet different usage needs on each building floor. The number of people in different functional spaces varies, resulting in an uneven distribution of people on each floors. When the building is subject to hazards, users inside buildings usually evacuate to safe areas through safety exits. Obviously, when the exit location is close to the crowded area, more people can be evacuated as quickly as possible. However, existing methods for optimizing exit locations of buildings mainly by shortening evacuation paths with limited considerations of the dynamic distributions of evacuees. Since the ontology has semantic description capability and reasonability. Therefore, the ontology is used to organize the design information related to the optimization of exit locations. The rules are further set to reason about the distribution of evacuees and the optimal exit location. Therefore, a design optimization method for exit locations based on BIM and ontology is proposed. Firstly, the ontology is developed to describe the information needed for design optimization. Then, the plan layout information is extracted from BIM as ontology instances. Next, based on the evacuation simulations, the exit location optimization strategies were created for the different distribution of evacuees, and the strategies were translated into SWRL rules. Finally, the ontology instance and SWRL rules are combined to reason about the exit location optimization strategies for specific projects. Therefore, this study provided the exit location optimization strategies to make the evacuation time shorter by considering the distribution of evacuees. The automation of exit location reasoning is further enhanced by using the constructed ontology to organize the design information and by transforming the optimization strategies into SWRL rules. The feasibility of the method was verified using several cases. By comparing the evacuation time of people under the exit location recommended by this method and the exit location designed by the shortest evacuation path, it is concluded that the method proposed in this paper takes into account the distribution of people in the plane and the evacuation time is shorter.

The impact of people with mobility limitations on exit choice

Demographic trends and advances in accessibility have led to more heterogeneous populations in public buildings. While providing accessible evacuation, it may be necessary to direct evacuees to different evacuation paths depending on functional capacity. The presence of people with mobility limitations (e.g., wheelchair users) may affect the evacuation of others, possibly influencing the decision making of neighbouring evacuees. This study investigates how exit choice of people is influenced by the functional capacity of other evacuees and the design of the exit. Virtual Reality was used to design a discrete choice experiment in which participants were exposed to multiple choice situations and a mixed logit model was fitted to the data. Results from 40 participants indicate that exit choice was positively influenced by other people choosing the exit, and a larger effect was found when the other person was a wheelchair user. The design of the exit also influenced the exit choice of the participants. Large inter-person differences existed. Strategies reported by the participants in the subsequent questionnaire included altruistic behaviour. These results can inform fire safety engineers when outlining evacuation strategies and evacuation model users in their assumptions concerning exit choice in building evacuation.

Machine learning driven smart fire safety design of false ceiling and emergency response

In modern buildings, false ceilings are widely used for building services systems and aesthetic purposes, but they also pose challenges in terms of fire safety. A fire accident typically results from the failure of multiple safety measures or components. In many fire accidents, fire and smoke reached the false ceiling and kept spreading in the interstitial space without any detection. This study first generates a numerical database of false ceiling fire scenarios by varying the room dimensions, false-ceiling leakage area, fire size and locations. Then, a smart model based on machine learning to predict the fire smoke motion below and above the false ceiling is developed. The trained model is capable to predict the activation time of fire detectors and sprinklers for any given false ceiling design and fire scenario. This methodology enables a designer to generate multiple fire scenarios and determine the available safe egress time (ASET) for performance-based fire engineering designs especially in terms of fire detection time. In case of a real fire, with the data feed from the fire sensor network, the trained machine learning model can further predict the critical building fire events with the false ceiling, such as multi-compartment fires, smoke in the evacuation path, and structural failures. This work proposes a smart framework for improving the building fire safety design of false ceilings and the sensor-driven fire forecast to support firefighting.It enhances the emergency response processes by enabling dynamic risk assessment through prediction of critical events.

Fire propagation-driven dynamic intelligent evacuation model in multifloor hybrid buildings

Modern multifloor hybrid (MFH) buildings usually have complex structures and intense closeness, significantly increasing difficulties for the safe evacuation ofpeople in the event of an accidental fire occurrence. Facing the incapacities of traditional evacuation strategies in pointing out the most reasonable evacuation paths in such MFH buildings, it is necessary to study the intelligent evacuation process considering the fire propagation, architectural features, optimal path, and dynamic guidance. To determine the optimal dynamic evacuation strategy after an accidental fire occurrence in MFH buildings, this study proposes a fire propagation-driven dynamic intelligent evacuation (FPDDIE) model. The FPDDIE model includes the optimal path planning method, evacuation time prediction model based on the stacking integration strategy and framework design of active instructions. Among them, the evacuation time prediction model can help people choose safe paths. Finally, a large gymnasium is taken as a real case study to verify the effectiveness of the proposed model and method. The results obtained from the numerical analysis and simulations indicate that the overall evacuation time of the crowd is reduced, the prediction error of individual evacuation time is acceptable and the path replanning can ensure evacuation safety in some fire scenarios.

Study on optimization of shelter locations and evacuation routes of gas leakage accidents in chemical industrial park

Safety hazards cannot be ignored during the promotion of local economic development in chemical parks. In a crowded chemical industrial park, heavy casualties and serious economic losses may be incurred in a toxic gas leak accident. Accordingly, the location of emergency shelters and occupant escape routes in chemical parks during gas leakage accidents were examined in this study. Additionally, the risk of leakage in chemical parks throughout the year was assessed by selecting the average values of wind direction, wind speed and temperature across four seasons, and criteria for selecting emergency shelters were set according to the minimum risk of toxic gas leakage throughout the year. Moreover, a 30,000 t/a carbon-five separation construction project was adopted as a case study, where instantaneous leakage of an auxiliary raw material liquid ammonia storage tank occurred. The Floyd algorithm was then used to calculate the evacuation paths for people at multiple sources and multiple endpoints. The influence of the dynamic diffusion of toxic gas around the leakage source point on evacuation route safety was thoroughly analyzed, and occupant evacuation plans were employed in the chemical park based on the shortest evacuation path with the least impact from toxic gases. The findings produced by this study may provide reliable technical support guidance for safety evacuation in regard to toxic gas leakage accidents for personnel in chemical industry parks.

Dynamic Evacuation Path Planning for Multi-Exit Building Fire: Bi-Objective Model and Algorithm

Dynamic Evacuation Path Planning for Multi-Exit Building Fire: Bi-Objective Model and Algorithm

The thermal decomposition and combustion of building and finishing materials

The paper investigates specific aspects of the methods for studying the composition of thermal decomposition and combustion products of typical indoor synthetic and natural materials. The purpose of this research is to build up an experimental database with the concentrations of gaseous pyrolysis products during the thermal decomposition of typical materials in compartments. The utilized techniques are gas and liquid chromatography, effusion mass spectrometry, IR spectroscopy, gas analysis equipment (laboratory and industrial gas analyzers). The conducted research compared the promising methods of chemical analysis of gaseous products of thermal decomposition and combustion of synthetic and natural materials that carry a fire load in residential and public spaces. Data on the quantitative and qualitative composition of combustion products of wood, textile and interior design items were obtained. Toxicity indices of combustion product components were calculated. The data presented in the tables and diagrams for the smoke aerosol component yield in the thermal decomposition and combustion of materials can be used to determine gas hazards from combustion products in an area and to solve other applied problems. These findings are fundamental for predicting the toxic substance concentrations, developing fire safety measures and planning the evacuation paths. It is instructive to use these findings as the basis for developing the physical and mathematical models of heat and mass transfer, phase transformations and chemical reactions to determine the patterns of propagation of gaseous components of thermal decomposition and combustion in spaces with different geometries and dimensions.

Exposure risk and emergency evacuation modeling of toxic gas leakage in urban areas under the influence of multiple meteorological factors

Frequent toxic gas leakage accidents in urban environments cannot be timely controlled and often cause great harm due to the many factors affecting gas diffusion. In this study, based on the coupling method of the Weather Research and Forecasting Model (WRF) and the open source OpenFOAM software platform, the chlorine gas diffusion process at different temperatures, wind speeds, and wind directions in a chemical laboratory and nearby urban areas in Beijing was studied numerically. A dose-response model was used to calculate chlorine lethality and assess exposure risk at the pedestrian level. To predict the evacuation path, an improved ant colony algorithm, a greedy heuristic search algorithm based on the dose-response model, was applied. The results demonstrated that the combination of WRF and OpenFOAM could consider the effects of various factors such as temperature, wind speed, and wind direction on the diffusion of toxic gases. The direction of chlorine gas diffusion was affected by wind direction, and the range of chlorine gas diffusion was affected by temperature and wind speed. The area of high exposure risk (fatality rate above 40%) at high temperatures was 21.05% larger than that at low temperatures. When the wind direction was opposite the building, the high exposure risk area was 78.95% smaller than that under the building direction. The present work provides a promising approach for exposure risk assessment and evacuation planning for the emergency response to urban toxic gas leakage.

Simulation and optimization of hybrid evacuation model for high-rise building under emergency condition

With the characteristics of high-speed transportation, the elevator has an overwhelmingly crucial meaning footnote in an auxiliary evacuation high-rise building with more than 30 floors causing the time to be the deciding factor under the emergency condition. The study shows that using the stairway or elevator separately cannot achieve the best effect. The elevator’s evacuation time depends on the number of occupants on each floor and the elevator’s carrying capacity. This paper discusses how to use the stair and elevator to optimize the hybrid evacuation in high-rise buildings and proposes two goals for the evacuation model of high-rise buildings under emergency conditions. One is that both the last occupant to use the stairway and the last occupant to use the elevator reach the safe place simultaneously. The other is that we fine-tune the evacuation destinations of the occupants to balance the utilization of each evacuation path as much as possible. We present the optimization method with three steps, and the evacuation time shrunk by up to 57% according to the baseline. Finally, we compare the optimized hybrid evacuation and the single evacuation method to the previous work and give the results of the experiments.

An optimal guidance strategy for fire evacuations: A hybrid modeling approach

Evacuation plans are subject to many restrictions in the event of a fire, and unreasonable evacuation guidance methods can affect efficiency and even cause danger. In the current study, we investigate the optimal guidance strategy for fire evacuation to obtain the optimal guider layout and the evacuation paths. The environmental constraints are generated in conjunction with FDS fire simulation to transform the evacuation space into a pseudo-static obstacle grid. A multi-objective model of the guider layout is constructed, and the guider number is used as an optimization variable. The pseudo-static obstacle grid is combined with the exit fluency strategy to plan paths. The evacuation time and cost of the crowd are adopted as two objective functions. Finally, the optimal guider layout is generated by iterative optimization of NSGA-II algorithm with the introduction of a chromosome fragment deletion operator. The simulation results and comparative analysis indicate that the model can improve fire evacuation safety, enhance the utilization and balance of each exit, and reduce evacuation time and resource redundancy.

Developing pedestrian evacuation path parameters based on the requirements of Indonesia National Agency of Disaster Management and the Indicators of the Global Walkability

The tsunami incident in 2004, in Aceh, was killed around 230,000 people and destroyed more than 1,000 buildings. Many accidents also occurred related to the earthquakes and tsunamis in Indonesia. For example, when the big earthquakes happened in Padang City, people evacuated themselves by using cars or motorbikes (motorized movement). It created a problem; and became worse when the movement was not effective. All research related to evacuation movement suggested that walking is better than moving using vehicles. Thus, people who will evacuate by walking should be supported by good and sufficient infrastructure, such as the path. Up to recent time, there has been no research conducted in relation to finding the solution this problem. Therefore, this article reports on the research that focused on the parameters of the path design. The validity and reliability of the test, were measured by experts. The research respondents filled the form and their responses were taken as the data analyzed to get the parameters, indicators, and levels based on the adjustment of BNPB (National Agency of Disaster Management) and GWI (Global Walkability Indicators) guidelines. In conclusion, the four parameters: walking path mode conflict, availability of walking path, amenities, and obstruction were found.

The Digital Twin Modeling Method of the National Sliding Center for Intelligent Security

There are some problems in the security management of large stadiums, such as complex situations and a lack of coordination among systems. An intelligent security system can effectively improve the efficiency of security management. The digital twin concept is applied to intelligent security systems in large stadiums, and an intelligent security modeling method for large stadiums based on digital twin is proposed. The modeling method of the physical model is presented for the security equipment and building entities. The virtual model is based on geometric, physical, behavioral, and rule models. Considering the particularity of building security, the environmental model is added to describe the environmental information. The application mode of the digital twin model is proposed. In the security management process, multi-source data and virtual models are integrated to analyze and control the security management process of buildings, forming a closed loop of “perception-analysis-control” in security management. Taking the National Sliding Center as an example, this paper verifies the digital twin model and its operation mode of intelligent building security through several possible situations in the operation process of the stadium. The analysis of security data and evacuation path guidance in emergencies are simulated. The digital twin model for intelligent security integrated the building security data and simulation models to assist in identifying the types of dangers and the treatment of emergencies. Furthermore, the control of building equipment was integrated into the security system. The digital twin model for intelligent security improved the integration and intelligence of the security system.

Dynamic risk assessment of emergency evacuation in large public buildings: A case study

Large-scale public buildings are characterized by huge internal space, a large number of people, long evacuation paths, along with many internal risk factors and uncertain or unpredictable emergency events. If the condition of risk factors in large buildings could be monitored in real-time, to achieve dynamic assessment and prediction of personnel evacuation risk factors, it can provide early warning, give auxiliary support for emergency decision-making, and improve the risk management and control capability of large public buildings. For the importance of improving risk prediction and evaluation capability in practice, this paper proposes a dynamic risk assessment method that integrates dynamic object detection and risk assessment base on DNN (Deep Neural Network). A real-time detection method based on YOLO (You Only Look Once) algorithm is proposed to extract the risk factors about human factors, which have very important influence in the evacuation processes. Combining building structural features with these dynamic human factors as the input parameters, DNN is finally used as the risk assessment model and to realize the real-time evaluation of evacuation risk level.

Risk analysis of people evacuation and its path optimization during tunnel fires using virtual reality experiments

The number of urban tunnels has been increasing rapidly, accompanied by frequent tunnel fire accidents owing to the complex tunnel structure and large traffic flow. In this study, a full-size tunnel virtual reality (VR) scenario and computational fluid dynamics (CFD) construction model were established to investigate the evacuation behavior and corresponding risk of people in the early stage of vehicle fires considering four scenarios: normal circumstance, without VR agents, without emergency evacuation signs, and without fire extinguishers. Firstly, the cumulative values of CO, CO2, and temperature along the evacuation path were monitored using CFD. Secondly, the smoke toxicity was calculated using the N-GAS model, and the total risk value was computed based on the analytic hierarchy process which was defined as “smoke hazard: temperature hazard = 7:3.” Thirdly, a multiple regression model was created based on accident data. Finally, to minimize accidents, the design of the evacuation path was optimized using the established mathematical model and A* algorithm to verify the effectiveness of the risk assessment model. The results show that the effects of VR agents, emergency evacuation signs, and fire extinguishers on the evacuation behavior of people are mutual influence. This study combined time and route in the VR evacuation experiments to overcome the limitations of the existing control and VR experiments in quantifying the evacuation results. This research can be utilized to improve emergency evacuation plans and emergency response decision making. Furthermore, it can broaden the application of VR in the field of tunnel lifecycle safety management.

An improved social force model (ISFM)-based crowd evacuation simulation method in virtual reality with a subway fire as a case study

ABSTRACT Crowd evacuation simulation using virtual reality (VR) is significant for digital emergency response construction. However, existing evacuation simulation studies suffer from poor adaptation to complex environments, inefficient evacuations, and poor simulation effects and do not fully consider the impacts of specific disaster environments on crowd evacuation. To more realistically express the crowd evacuation results obtained under the influence of fire environments and the subjective consciousness of pedestrians in subway stations, we designed a dynamic pedestrian evacuation path planning method under multiple constraints, analysed the influences of an ‘environmental role’ and a ‘subjective initiative’ on crowd evacuation, established an improved social force model (ISFM)-based crowd evacuation simulation method in VR, developed a prototype system and conducted experimental analyses. The experimental results show that the crowd evacuation time of the ISFM is affected by the disaster severity. In simulation experiments without disaster scenarios, the improved model's crowd evacuation efficiency improved by averages of 12.53% and 15.37% over the commercial Pathfinder software and the original social force model, respectively. The method described herein can effectively support real-time VR crowd evacuation simulation under multiexit and multifloor conditions and can provide technical support for emergency evacuation learning and management decision analyses involving subway fires.

Intelligent fire information system based on 3D GIS

BackgroundThis work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things (IoT) and achieve an actual intelligent fire rescue. A smart fire protection information system was designed based on the IoT. A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue. MethodsThe intelligent fire visualization platform based on the three-dimensional (3D) Geographic Information Science (GIS) covers project overview, equipment status, equipment classification, equipment alarm information, alarm classification, alarm statistics, equipment account information, and other modules. The live video accessed through the visual interface can clearly identify the stage of the fire, which facilitates the arrangement of rescue equipment and personnel. The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization: emergency rescue time and the number of vehicles. In addition, an evacuation path optimization method based on the Improved Ant Colony (IAC) algorithm was designed to realize the dynamic optimization of building fire evacuation paths. ResultsThe experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t=17s than the initial value. In addition, the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation, demonstrating that this model could detect fire. ConclusionsThe IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route. Therefore, the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.

BIM based framework for building evacuation using Bluetooth Low Energy and crowd simulation

Crowd evacuation is an area that has been studied by numerous researchers over the last years. Many models have been developed to simulate the evacuation. However, most of the developed models involved emergency routing of building occupants using hypothetical assumptions of their positions without integrating their actual locations into the models. Thus, in order to increase efficiency and accuracy of evacuation models, evacuees' real locations can be identified and then integrated into the model. In this research, Building Information Modelling (BIM), agent based simulation and Bluetooth Low Energy technology are integrated together to create an evacuation framework capable of indicating locations of building occupants and identifying the optimum evacuation path based on these locations. Point cloud data of a building is collected from 3D laser scanner, then converted to 3D BIM model, which is then imported into an agent based crowd simulation software. Also, an indoor positioning module is developed, which is composed of an indoor positioning system (IPS) developed by the authors. Through the IPS, evacuees’ locations are calculated, whereas Bluetooth emitters –beacons- held by evacuees emit signals that are detected by group of gateways mounted in the building detect. The locations are then sent to the crowd simulation software, then through integrating the modules, optimum evacuation route is identified, which is then converted to directions through a dynamic signage module. A case study is presented to illustrate the proposed framework. The results of the case study show that the developed indoor positioning system has an accuracy of 1.88 m with a maximum error range of 3.934 m. Moreover, the results indicate that the implemented indoor positioning system is capable of correctly detecting the evacuees in the rooms they are actually in with an accuracy of 93%. Finally, the simulation shows that the full evacuation of the floor under study took 2 min and 48 s.