Suchergebnisse

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.

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 Innovation

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.

Previous research has provided useful information to qualitatively interpret fundaments of social influence during evacuation. However, this performance needs to be quantified that it can be understood and considered in life safety calculations. We apply a quantitative method for assessing the behavioural cohesion among evacuees i.e. whether individuals respond and move together, as a group. Three evacuation scenarios are analysed: 1) a multimodal station, 2) a sports centre and 3) a library. Results suggested that proximity (visual/verbal contact) could be 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. Overall, these results indicate that the proposed method can be used as a priori assessment to determine the presence of evacuation groups in fire safety engineering analyses. ; 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.

This work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of principal component analysis. We validate the method and show its efficiency against false positives and negatives using several metrics: accuracy, precision, recall, and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management. ; Authors would like to acknowledge the discussions with Marcos Pantaleón from APIA XXI, Ambher Monitoring Systems and Banobras S.N.C. This work has received funding from the European's Union Horizon 2020 research and innovation program under the grant agreement No 690660 (RAGTIME Project) and No 769373 (FORESEE Project). This paper reflects only the author's views. The European Commission and INEA are not responsible for any use that may be made of the information contained therein. David Pardo has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS), the European POCTEFA 2014-2020 Project PIXIL (EFA362/19) by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program, the Project of the Spanish Ministry of Science and Innovation with reference PID2019-108111RBI00 (FEDER/AEI), the BCAM "Severo Ochoa" accreditation of excellence (SEV-2017-0718), and the Basque Government through the BERC 2018-2021 program, the two Elkartek projects 3KIA (KK-2020/00049) and MATHEO (KK-2019-00085), the grant "Artificial Intelligence in BCAM number EXP. 2019/00432", and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education.