Effect of Exit Door Width on Flow Rate in Occupant-based Fire Evacuation Scenarios in Dormitories

Student dormitories are intensely used buildings that meet the resting, accommodation and living needs of students. It is necessary to ensure the safety of students and to eliminate possible risks in dormitories as intensive use areas. Fires pose a great risk in dormitories and may cause serious casualties and injuries. The reduction of casualties and injuries can be achieved by analyzing occupant behaviour during fires according to the building use scenarios. In this paper, a type of dormitory that provides two alternative exits is explored. The building use scenarios of the dormitory were investigated by making on-site observations. Students’ use of sleeping units, dining units and partial sleeping/dining units and fire exit routes were determined. Pathfinder computer program was used to analyze the fire evacuation performance. This program was defined in accordance with occupant behaviour and different fire evacuation times were suggested depending on the building use scenarios. At the end of the study, based on the evacuation times, the flow rate at the exit doors according to the location of the occupants was analyzed. In the fire escape routes, as the upper floors are reached from the lower floors, the occupant flow rate decreases at the exit doors and the flow rates continue to be stable as the number of occupants is saturated according to the door width. The decrease in the number of occupants in the dining unit decreases the flow rate at the exit doors. It is important that various assembly units in dormitories, such as the dining unit, are designed on floors that can directly provide evacuation to a safe area. The results obtained are suitable for all dormitories, residences, hotels and other similar buildings. Keywords: building use scenario, dormitory, evacuation, flow rate, exit widths

431. SARS-CoV-2 Environmental Contamination in Hospitalized COVID-19 Patients’ Rooms

Background The correlation between SARS-CoV-2 RNA and infectious viral contamination of the hospital environment is poorly understood. Methods housed in a dedicated COVID-19 unit at an academic medical center. Environmental samples were taken within 24 hours of the first positive SARS-CoV-2 test (day 1) and again on days 3, 6, 10 and 14. Patients were excluded if samples were not obtained on days 1 and 3. Surface samples were obtained with flocked swabs pre-moistened with viral transport media from seven locations inside (bedrail, sink, medical prep area, room computer, exit door handle) and outside the room (nursing station computer). RNA extractions and RT-PCR were completed on all samples. RT-PCR positive samples were used to inoculate Vero E6 cells for 7 days and monitored for cytopathic effect (CPE). If CPE was observed, RT-PCR was used to confirm the presence of SARS-CoV-2. Results We enrolled 14 patients (Table 1, Patient Characteristics) between October 2020 and May 2021. A total of 243 individual samples were obtained – 97 on day 1, 98 on day 3, 34 on day 6, and 14 on day 10. Overall, 18 (7.4%) samples were positive via RT-PCR – 9 from bedrails (12.9%), 4 from sinks (11.4%), 4 from room computers (11.4%) and 1 from the exit door handle (2.9%). Notably, all medical prep and nursing station computer samples were negative (Figure 1). Of the 18 positive samples, 5 were from day 1, 10 on day 3, 1 on day 6 and 2 on day 10. Only one sample, obtained from the bedrails of a symptomatic patient with diarrhea and a fever on day 3, was culture-positive (Figure 2). Table 1. Patient Characteristics Figure 1. Proportion of RT-PCR Positive Samples by Sample Day and Location Figure 2. Cell cultures of negative control (left) and CPE positive sample (right) Conclusion Overall, the amount of environmental contamination of viable SARS-CoV-2 virus in rooms housing COVID-19 infected patients was low. As expected, more samples were considered contaminated via RT-PCR compared to cell culture, supporting the conclusion that the discovery of genetic material in the environment is not an indicator of contamination with live infectious virus. More studies including RT-PCR and viral cell culture assays are needed to determine the significance of discovering SARS-CoV-2 RNA versus infectious virus in the clinical environment. Disclosures David J. Weber, MD, MPH, PDI (Consultant)

Numerical Analysis of Smoke Spreading in a Medium-High Building under Different Ventilation Conditions

Smoke from fires in residential buildings represents the greatest threat to the life and health of inhabitants and firefighters at the scene of an accident. Therefore, the aim of this study was to reconstruct a numerical model for the estimation of smoke spread in a medium-high building under different ventilation conditions. Here, the three-dimensional geometry of a designated medium-high building was reconstructed and an exit door in the basement was specified as a smoke inlet; a window in the upper part was marked as outlet; and an entrance door, which allowed the outside air to enter the building after opening, was designated as an inlet door. The initial simulation, in which no air could enter the building, predicted the time taken for the staircase to become filled with smoke. In a second simulation, the entrance door was a fresh air inlet. The results showed that, for the analyzed building, rapid use of the mechanical ventilation can shorten the time of operations and improve their safety.

Anticipatory behaviour as an indicator of the welfare of dairy calves in different housing environments

Anticipatory behaviour occurs in the period before a reward or other positive event is presented and has been interpreted as an indicator of the welfare and emotional state of animals. The use of this indicator has received limited attention in dairy calves. Therefore, we investigated how anticipatory behaviour is affected by housing environment and reward quality, and if anticipatory behaviour changes when reward quality changes unexpectedly. Sixteen pairs of calves were assigned to treatments in a 2 x 2 factorial design (two housing environment and two reward quality combinations). Housing was either basic (2 m2/calf, river stone surface) or enriched (5 m2/calf, woodchip, and enrichment items), and the reward was access to either an additional basic or enriched pen. Calves were conditioned to anticipate reward pen access; anticipatory behaviour toward receiving the reward pen was measured. Signaling reward access increased the frequency of transitions between behaviours and duration of touching and looking at the signal and exit door. Basic-housed calves showed more anticipatory behaviour (increased frequency of transitions between behaviours) and decreased latency to access the reward compared to enriched-housed calves, but the reward pen quality had no effect on anticipatory behaviour. When the reward pen quality changed from enriched to basic unexpectedly, resulting in sudden reward loss, basic-housed calves decreased, while enriched-housed calves increased, anticipatory behaviour. However, there was no change in anticipatory behaviour during reward gain (change from basic to enriched reward pen). Our findings align with previous work showing that animals in basic housing show more anticipation for a reward, and demonstrate suppressed behavioural response when experiencing reward loss, suggesting greater sensitivity to reward. Sensitivity to reward has associations with mood state; thus, calves in basic environments may experience a more negative emotional state, and thus reduced welfare, compared to calves in enriched environments.

An Improved Simulation Model for Pedestrian Crowd Evacuation

<p>This paper works on one of the most recent pedestrian crowd evacuation models, i.e., “a simulation model for pedestrian crowd evacuation based on various AI techniques”, developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.</p>

An Improved Simulation Model for Pedestrian Crowd Evacuation

<p>This paper works on one of the most recent pedestrian crowd evacuation models, i.e., “a simulation model for pedestrian crowd evacuation based on various AI techniques”, developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.</p>

An Improved Simulation Model for Pedestrian Crowd Evacuation

This paper works on one of the most recent pedestrian crowd evacuation models—i.e., “a simulation model for pedestrian crowd evacuation based on various AI techniques”—which was developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it into the model. This method enables the developed model to find a more appropriate evacuation area design regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model’s output—i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.