Measuring social influence and group formation during evacuation process

Evacuees are likely to respond and move forming groups. However specific data about grouping is generally unavailable and the relationship between response and movement times and specific groupings are unknown. Using a simple method, we measure behavioural cohesion of occupants during evacuation processes. The case study involves using the method in a bus station, a sport centre and a library. Results suggest that proximity (visual/verbal contact) is an important factor but not decisive in the formation of evacuation groups. Social ties and whether occupants share a target and/or an activity before the alarm are also deemed to be important factors. This provides an exciting opportunity to advance our knowledge of social influence and group formation during evacuation.The authors would like to thank the European Union for the LETS-CROWD project received funding from the Horizon 2020 Research and Innovation Programme under the grant agreement Nº 740466 and the Spanish Ministry of Economy and Competitiveness for DEFENDER Project Grant, Ref: BIA2015-64866-R, co-funded by ERDS funds

Gender and public perception of disasters: a multiple hazards exploratory study of EU citizens

To explore gender influence on individual risk perception of multiple hazards and personal attitudes towards disaster preparedness across EU citizens. Method: An online survey was distributed to 2485 participants from Spain, France, Poland, Sweden and Italy. The survey was divided into two parts. The first part examined perceived likelihood (L), perceived personal impact (I) and perceived self-efficacy (E) towards disasters due to extreme weather conditions (flood, landslide and storm), fire, earthquake, hazardous materials accidents, and terrorist attacks. The overall risk rating for each specific hazard was measured through the following equation R = (L × I)/E and the resulting scores were brought into the range between 0 and 1. The second part explored people’s reactions to the Pros and Cons of preparedness to compute the overall attitudes of respondents towards preparation (expressed as a ratio between −1 and 1). Results: Although we found gender variations on concerns expressed as the likelihood of the occurrence, personal consequences and self-efficacy, the overall risks were judged significantly higher by females in all hazards (p < 0.01). We also found that, in general, most respondents (both males and females) were in favour of preparedness. More importantly, despite the gender differences in risk perception, there were no significant differences in the attitudes towards preparedness. We found weak correlations between risks perceived and attitudes towards preparedness (rho < 0.20). The intersectional analysis showed that young and adult females perceived higher risks than their gender counterparts at the same age. There were also gender differences in preparedness, i.e., females in higher age ranges are more motivated for preparedness than men in lower age ranges. We also found that risk perception for all hazards in females was significantly higher than in males at the same education level. We found no significant differences between sub-groups in the pros and cons of getting ready for disasters. However, females at a higher level of education have more positive attitudes towards preparedness. Conclusions: This study suggests that gender along with other intersecting factors (e.g., age and education) still shape differences in risk perception and attitudes towards disasters across the EU population. Overall, the presented results policy actions focus on promoting specific DRR policies and practices (bottom-up participatory and learning processes) through interventions oriented to specific target groups from a gender perspective.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 832576

Interdependence of flows when merge in rail tunnel evacuations

The understanding of merging flows during evacuation can have important implications for rail tunnels safety. This paper explores the interdependence of the merging of flows coming from the walkway with those exiting the train. Eight train exit configurations were tested using a mock-up of a rail car exit and a lateral walkway involving 77 participants (mean age 48; standard deviation 15; range 18-74). New measurements and data processing methods are proposed allowing statistical analysis to be performed. The results provide quantitative evidence of the preferences between flows. We found that the bias in the evacuation was slightly in favour of the walkway when train exit was at 0 m in height. Contrary to expectations a moderate dominance of walkway flow was observed at 0.8 m in height. Less variation was found for the train exit at 1.2 m in height with a clear priority of walkway flow. This happened despite deference behaviours performed by participants, i.e. people stopped to help those entering from the rail car. This novel contribution aims to provide a new method for those involved in development and validation of new and current evacuation modelling tools and those who want to improve their understanding of merging behaviour during evacuation in rail tunnels.The authors would like to thank the Ministry of Economy, Industry and Competitiveness (MINECO) for funding the SIGNAL project on the frame of the Subprogram RETOS-COLABORACIÓN 2016 call (Ref -RTC-2016-5474-4) as well as the European Union through ERDF funding under the objective of Strengthening Research, Technological Development and Innovatio

Evacuation management system for major disasters

Predicting and understanding mass evacuations are important factors in disaster management and response. Current modelling approaches are useful for planning but lack of real-time capabilities to help informed decisions as the disaster event evolves. To address this challenge, a real-time Evacuation Management System (EMS) is proposed here, following a stochastic approach and combining classical models of low complexity but high reliability. The EMS computes optimal assembly points and shelters and the related network of evacuation routes using GIS-based traffic, pedestrian and routing models including damaged assets or impassable areas. To test the proper operation performances of the EMS, we conducted a case study for the Gran Canaria wildfireThis research and APC was funded by the European Union’s H2020 research and innovation programme under grant agreement No. 832576 (ASSISTANCE project)

Innovations for smoke management in passenger trains

Spanish manufacturer Construcciones y Auxiliar de Ferrocarriles developed an innovative alternative for compartmentation, based on a smoke extraction system, to guarantee safe conditions during evacuation processes in a passenger unit. To demonstrate its performance in a train unit, a real-scale experimental programme, supported by the application of fire computer modelling, was applied in a new Construcciones y Auxiliar de Ferrocarriles' rolling stock. The new smoke exhaust system aims to extract the smoke generated during a fire in the passenger area by exhaust fans of the heating, ventilation, and air conditioning system, allowing the ingress of fresh exterior air in the lower part of the rear ends of the car. These key elements create an air flow that evacuates the smoke to prevent people from being exposed to it. Full-scale fire tests were developed in the train unit following the Australian standard AS 4391-1999. A fire of 140kW was used, and the smoke was generated by a clean smoke machine. Measurement points included six thermocouple trees, 10 gas flow velocity probes and two GoPro HD video cameras (for the estimation of the visibility). The system performance was successful with the tenability criteria, since the value of visibility at the non-fire car was greater than 30 m and the temperature was lower than 30°C during all the tests at a height of 1.7m above the floor. Experimental results were used to validate the computational model. The computational model results show a good accuracy compared with the tests

A simple direct method to obtain kinetic parameters for polymer thermal decomposition

In a fire, the polymer combustion occurs when gaseous fuels react with oxygen. The heating of a material could force the release of gaseous fuels during thermal decomposition and pyrolysis. The rate of pyrolysis to define the gaseous fuels is usually interpreted by means of the Arrhenius expression and a reaction model expression, which are characterized by an activation energy, a pre-exponential factor, and a reaction order value. Many methods are available for determining kinetic parameters from thermogravimetric experimental data. However, the most challenging issue is achieving an adequate balance between accuracy and simplicity. This work proposes a direct method for determining the kinetic parameters with only a thermogravimetric experiment at a single heating rate. The method was validated with six polymers, and the results were compared with those from similar procedures, such as the Lyon method and generalized direct method. The results achieved using the simpler approach of the proposed method show a high level of accuracy.Authors would like to thank to the Consejo de Seguridad Nuclear for the cooperation and co-financing the project “Metodologías avanzadas de análisis y simulación de escenarios de incendios en centrales nucleares” and to CAFESTO Project funded by the Spanish Ministry of Science, Innovation and Universities and the Spanish State Research Agency through public–private partnerships (Retos Colaboración 2017 call, ref RTC-2017-6066-8) co-funded by ERDF under the objective “Strengthening research, technological development and innovation

Intelligent emergency management system for railway transport

Nowadays, a major safety challenge in rail transport is to manage the incidents and emergencies in the most efficient possible way. The current contingency plans tend to be based on static procedures not taking into account how real-time conditions affect them. Consequently, the decision-making process may well suffer delays and the possibility of occurrence for human mistakes could raise since the required measures are expected to be carried out under important pressure. In this study, focused on commuter trains, railway safety is enhanced by a new intelligent emergency management system which aims to support the operator tasks in a realtime incident or emergency situation. This cyber-physical system is composed by two main modules: one on board the train, including sensors and GPS, and other integrated in the control centre addressing four computational models. Those models cover (1) the detection of different types of incidents/emergencies using the information received from on board sensors, (2) the calculation of the evacuation process (if necessary), (3) the selection, estimation of routes and communication with emergency services required for each event, and finally (4) a provision of actions to support the operator decisions. Communication between modules is provided by GPRS due to actual technology available in the pilot trains. This system has been implemented in an actual railway line in Cantabria (Santander-Cabezón de la Sal) and three practical demonstrations were defined based on several use cases, which were tested using a pilot facility incorporating all sensors and devices installed in those trains. Results demonstrated the benefits of the new system.The authors would like to thank the Ministry of Economy, Industry and Competitiveness (MINECO) for funding the SIGNAL project on the frame of the Subprogram RETOS-COLABORACIÓN 2016 call (Ref-RTC-2016-5474- 4), as well as the European Union through ERDF funding under the objective of Strengthening Research, Technological Development and Innovation and also to SETELSA company for their partnership, dedication and support for the developing of the project

Real-time evacuation route selection method for complex buildings

This paper proposes a novel real-time decision methodology for the selection of optimal evacuation routes for buildings. A summary of the mathematical formulation, the solution algorithm and the computer model are presented. The optimization algorithm is based on the stochastic evacuation model predictions by considering emergency data such as the location of the hazard. The method is applied to an industrial building. The stochastic evacuation model is compared with the commercial evacuation model STEPS. In addition, the computational model is applied to several emergency scenarios to evaluate its validity.The authors would like to acknowledge the Spanish Ministry of Economy and Competitiveness for the DEFENDER Project grant, Ref: BIA2016-64866-R, co-financed by ERDS funds

A method to assess the accuracy of pseudo-random number sampling methods from evacuation datasets

We propose a method for assessing the accuracy of pseudo-random number sampling methods for evacuation modelling purposes. It consists of a systematic comparison between experimental and generated distributions. The calculated weighted relative error (Ew_rel) is based on the statistical parameters as central moments (mean, standard deviation, skewness and kurtosis) to shape the distribution. The case study involves the Box?Muller transform, the Kernel-Epanechnikov, the Kernel-Gaussian and the Piecewise linear generating samples from eight evacuation datasets fitted against normal, lognormal and uniform distributions. Keeping in mind that the Bos Muller method has two potential sources of error (i.e. distribution fitting and sampling), this method produces plausible results when generating samples from the three types of distributions (Ew_rel 0.80). Results suggest that the Piecewise linear is the most accurate method (Ew_rel = 0.01 normal; Ew_rel = 0.04 lognormal; Ew_rel = 0.009 uniform). This method has the advantage of sampling directly from empirical datasets i.e. no previous distribution fitting is needed. While the proposed method is used here for evacuation modelling, it can be extended to other fire safety engineering applications.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the DEFENDER Project Grant, Ref.: BIA2015-64866-R, co-financed by ERDS funds

LLDPE kinetic properties estimation combining thermogravimetry and differential scanning calorimetry as optimization targets

Thermal analysis techniques play a crucial role to characterize solid-phase thermal decomposition, since it provides information about how mass is lost (thermal gravimetric analysis) and energy released [differential scanning calorimetry (DSC)]. However, most of the input thermal parameters and kinetic properties to be used in fire computer modelling cannot be obtained directly from those tests. Early works looked forward achieving those parameters employing indirect fitting methods, which enable the user to obtain a set of parameters capable of simulating accurately the mass loss curve (TG) or its derivative (DTG). This work aims to study the possibility of adding the energy released as a new target in the process, applying the analysis to linear low-density polyethylene. Results obtained in the present work reveal the major challenge of getting a set of parameters that can also fit DSC curve. The level of accuracy of the fitting to TG curve is higher than to DSC curve. This fact increases the value of the errors when both curves are used as targets to approach. As a result, this paper includes an alternative to consider the effects of the DSC curve.The authors would like to thank to the Consejo de Seguridad Nuclear for the cooperation and co-financing the project ‘‘Simulation of fires in nuclear power plants’’ and to CAFESTO Project funded by the Spanish Ministry of Science, Innovation and Universities and the Spanish State Research Agency through public–private partnerships (Retos Colaboración 2017 call, ref RTC-2017-6066-8) co-funded by ERDF under the objective ‘‘Strengthening research, technological development and innovation’’