The impact of smoke on walking speed

Transcription

1 FIRE AND MATERIALS Fire Mater. 2014; 38: Published online 18 December 2013 in Wiley Online Library (wileyonlinelibrary.com) The impact of smoke on walking speed Karl Fridolf*,, Kristin Andrée, Daniel Nilsson and Håkan Frantzich Department of Fire Safety Engineering and Systems Safety, Lund University, Lund, Sweden SUMMARY In fire safety engineering, information about the expected walking speed of occupants through smoke is often one factor that is of interest to the designer. However, despite the fact that research already in the 1970s demonstrated that people tend to evacuate through smoke, little research has been performed on the topic since, and evidently, there is a lack of data on walking speed in smoke. This has created a situation where fire safety engineering assessments of the required safe escape time may be intimately associated with high uncertainties, especially for buildings in which people can be expected to evacuate long distances through smoke, for example, underground transportation systems. In order to address the lack of data on movement through smoke, 133 data points on individual walking speed in smoke are presented in this paper. The data lie within an extinction coefficient range of m 1. In line with previous studies, it is demonstrated that the level of smoke density has a negative impact on the walking speed, whereas no significant effects of inclination, type of floor material, gender, age and height could be found in the data. In this paper, recommendations arealsoprovidedonhowdesignersshouldtreatthe data in their fire safety risk assessments, depending on the type of risk analysis method, that is, if the designer is performing a deterministic analysis or a quantitative risk analysis. It is argued that this information can be used to reduce the uncertainty in future risk analyses involving egress calculations. Copyright 2013 John Wiley & Sons, Ltd. Received 19 August 2013; Revised 5 November 2013; Accepted 18 November 2013 KEY WORDS: evacuation; human behaviour in fire; smoke; fire safety risk assessment; risk analysis; underground transportation systems; buildings; tunnels 1. INTRODUCTION The introduction of performance-based regulations in many countries has led to a worldwide development of fire safety engineering and methods to evaluate the fire safety design of a building with engineering analyses [1]. In, for example, quantitative analyses, the performance of the fire safety design of a building is evaluated by the analysis of a number of representable fire scenarios within that building [2, 3]. Typically, this analysis includes the assessment of the available safe escape time (ASET) and the required safe escape time (RSET) for each scenario, according to the egress time model [4]. This model gives the designer the possibility to either assess the ASET and RSET more or less independent of each other or to adopt a more complex approach of deriving the ASET and RSET, in which for example the accumulated dose of toxicants in the body is compared with the effective dose to cause irritation, incapacitation or death, that is, the fractional effective dose concept [5]. *Correspondence to: Karl Fridolf, Department of Fire Safety Engineering and Systems Safety, Lund University, Lund, Sweden. karl.fridolf@brand.lth.se In this paper, the term building also includes underground structures, such as tunnels and underground transportation systems. Copyright 2013 John Wiley & Sons, Ltd.

2 THE IMPACT OF SMOKE ON WALKING SPEED 745 Regardless of engineering approach, the expected walking speed of the occupants in a fire is of interest in the evaluation of life safety in a building fire [6]. Because of this, much research has been focused on measurements of the walking speed under normal conditions, in various building types and for a large number of people, see for example Fahy and Proulx [7]. In contrast, very little research focusing on walking speeds in smoke-filled environments has been carried out. This is despite the fact that research within the field of human behaviour in fire quite early on demonstrated that it is not uncommon for evacuees to evacuate through smoke in case of fire [8, 9]. Bryan [8] did, for example, demonstrate that 62.7% of the population in his large study had evacuated through smoke, of which 29.2% eventually had turned back. Similarly, Wood [9] stated that 59.6% of the population in his, also large, study had evacuated through smoke, of which 26% for some reason had turned back. The tendency to move through smoke during an evacuation has, furthermore, been confirmed in more recent studies [10, 11]. However, still today, available data sets on walking speeds in smoke-filled environments are scarce [12]. At the same time, fire safety engineering assessment of the RSET especially in underground transportation systems includes designing for evacuation in smoke [13 15]. The first, and probably the most referred, research study on walking speed in smoke was carried out by Jin [16, 17]. It involved a total of 10 male participants who were instructed to move through a 20-m-long corridor filled with either irritant smoke (produced by burning wood cribs) or nonirritant smoke (produced by burning kerosene), with extinction coefficients ranging from approximately 0.3 to 1.5 m 1. The study demonstrated a negative correlation between the walking speed and extinction coefficient, that is, the walking speed decreased as the smoke density increased and even more so when the smoke was an irritant. These results were also verified in a later study involving a total of 31 participants: 14 men, mainly students, and 17 women, mainly housewives [18]. Despite the fact that the two studies referred to earlier included rather few people and were carried out in the 1970s, few studies have since been carried out in order to validate the results. Exceptions include the studies by Wright, Cook and Webber [19] and Galea et al. [20]. Because of the limited data, the results presented by Jin and Yamada [16 18] are commonly used in egress calculations. This leads to an apparent risk of propagation of uncertainties in engineering analyses. The problem has been emphasized by developers of egress models, who have explicitly highlighted the lack of data on walking speed and human behaviour in smoke [21]. The existing data on walking speed in smoke can be represented in egress calculations in different ways [22]. The two main approaches are as follows: (1) to use a fractional reduction of people s unimpeded walking speed as a function of the smoke density (or visibility) and (2) to use an absolute reduction of the walking speed as a function of the smoke density (or visibility). Both these approaches require that existing data be used to derive a correlation describing the reduction of speed as a function of smoke density (or visibility). The development of these types of correlations has been performed in previous studies on the basis of varying data sets [22, 23]. However, the data on which the correlations are based are still very limited, and the descriptions often lack a discussion about the type of risk analysis method that is being considered. Performance-based fire safety engineering requires the designer to verify that the proposed fire safety design delivers a sufficient level of safety. This often involves performing a risk analysis, but the approach can vary in complexity from simple qualitative reasoning to a full quantitative risk analysis (QRA), depending on the objective of the analysis [24]. Two approaches that are often used are deterministic analysis and QRA. Deterministic analysis (also known as scenario analysis) involves the selection of a manageable number of scenarios that represent the worst credible cases. This means that the hazards are mainly described in terms of their consequences in the deterministic analyses [25]. QRA instead involves identification of the full range of possible scenarios, and the scenarios are then described in terms of their probability and consequence. The choice of risk analysis approach therefore dictates the type of correlation between walking speed and smoke density (or visibility) that should be used in the egress calculations. Should, for example, the derived correlations be based on the 95th percentile or on mean values of the existing data? The previous discussion demonstrates that there is not only an apparent lack of existing data on walking speed in smoke but also a lack of consensus on how designers should use the available data

3 746 K. FRIDOLF ET AL. in different types of fire safety risk analyses (i.e. if the designer performs a deterministic analysis or a QRA). The purpose of the present paper is, therefore, as follows: 1. to present the results from two evacuation experiments in terms of individual walking speeds through smoke for a mixed and rather large population sample and 2. to discuss how the data can be treated in different types of fire safety risk analyses. The overall objective of this paper is to provide information that can be used to reduce the uncertainty in future risk analyses that involve egress calculations. 2. METHOD The two experiments included in this paper were both performed in smoke-filled tunnels, more specifically, tunnels equipped to resemble a road tunnel [26, 27] and a rail tunnel [28, 29]. One of the objectives of the experiments was to collect data on walking speed in smoke, but aspects such as way finding and design of emergency exits were also studied. The participants walked one at a time through the tunnels. Hence, no group interaction was investigated. Prior to the studies, a regional ethics board reviewed the experimental procedure of each experiment. Important ethical aspects and considerations, such as how and which information were given to the participants, which precautions were taken to minimize both psychological and physical injuries, how informed consent was collected, how the data would be stored after the experiment, and other routines for, for example, follow-up, were described by the researchers and then reviewed by the ethical board. Both studies were consequently approved. The type of smoke and collection of movement data were similar in the two experiments. A combination of glycerol-based artificial smoke and acetic acid was used in the tunnels. The acetic acid was used to achieve an irritant effect and was boiled in pots on hot plates. Because of ethical reasons, the concentration was always kept below the Swedish Work Environment Authority s recommended concentration for short-time exposure, that is, 10 ppm for 15 min. A firefighter was always present in the tunnels and filmed the participants with a thermal imaging camera. The video recordings were used to derive the walking speed of each participant. This work involved the reproduction of the walking path of each participant in a CAD drawing of the respective tunnels. The walking speed was then derived for each participant by dividing the total distance walked in the tunnel by the total time spent in the tunnel. Consequently, all stops made by the participants were included in the calculations of the walking speed, and the walking speeds derived are presented in this paper as mean values during the entire evacuation. The smoke density in the two tunnels was measured using the same type of smoke measurement devices. The device, that is, a light obscuration metre, consisted of a laser light source (a laser diode emitting light with a mean wavelength of 670 nm) and a receiver (a photodiode with a peak sensitivity wavelength of 710 nm) that were fixed at a distance of 95 cm apart in a metal frame. The height of the device corresponded to the average position of the participants heads in both experiments. Measurements were made at two locations in both tunnels, and the smoke density was taken to be the average of the measurements made at the two different locations, which seldom differed by more than 10- to 15-cm visibility. The extinction coefficient, that is, the measure of the smoke density, was calculated using [30]: C s ¼ 1=L ð Þ*lnðI=I 0 Þ (1) where C s is the extinction coefficient (m 1 ), L is the light path length (m) (0.95 m in the experiments), I is the intensity of the light through the smoke at the receiver (mv) and I 0 is the intensity of the light at the source (mv). In both experiments, the participants had been given limited information of the experiment before taking part. However, because of ethical reasons, the participants were told that they were going to walk in an underground environment similar to a tunnel, in dense artificial smoke. They were also

4 THE IMPACT OF SMOKE ON WALKING SPEED 747 told that acetic acid would be used to create an irritant environment. Details of the purpose of the experiment were, however, not given before the experiment. Although the two experiments are very similar, there are some important differences that need to be noted. These differences are summarized in the following two sections. For more detailed information about the two experiments, the reader is referred to the original publications, in which the experimental methodology and the procedure during the experiments are described in greater detail [26 29] Evacuation of a smoke-filled road tunnel The road tunnel evacuation experiment [26, 27] was performed in a tunnel that is ordinarily used for training of firefighters. The tunnel is approximately 37 m long and 5 m wide, and the ceiling height is approximately 2.6 m (Figure 1). It is located at the Swedish Civil Contingencies Agency s training area in Revinge, Sweden. The ground consists of a solid, asphalt material. Pillars supporting the ceiling are located in the middle of the tunnel, and two emergency exits are placed along the left hand wall. During the experiment, one backlit emergency exit sign was placed above each emergency exit, and a third sign was mounted on the wall close to the end of the tunnel to represent a third emergency exit. In addition, other way-finding systems were tested at some of the exits, for example, orange flashing light and rows of travelling light sources. The tunnel was modified to resemble a real road tunnel. Six cars were placed inside the tunnel. All cars faced the same direction in order to resemble a tunnel tube with one-way traffic. The travel direction of the cars was the same as that of the participants: from the entrance towards the exit. Four of the six cars were placed close to the row of pillars, and two cars were placed against the wall at the end of the tunnel (Figure 1). The distance between the tunnel wall and the four cars closest to the entrance was approximately 0.6 m, which meant that participants could pass between the cars and the wall. Two speakers were placed inside the tunnel, and the sound of fans and fire was played continuously throughout the experiment. Two different lighting conditions were used in the experiment: with illumination and without illumination. The illumination consisted of five light fittings with fluorescent tubes along the right wall. The light intensity measured at floor level varied between approximately 2 and 21 Lx without smoke. When smoke was introduced into the tunnel, the light intensity was reduced and varied between approximately 0 and 8 Lx. For comparison with the rail tunnel evacuation experiment, only the condition with illumination on is included in this paper. The smoke density was measured at two locations in the tunnel. The first measurement device was placed on the roof of the first car at an approximate distance of 7 m from the entrance. The second measurement device was placed on top of the third car at a distance of approximately 23 m from the entrance. A total of 46 participants took part in the experiment, more specifically 30 men and 16 women. The age ranged between 18 and 29 years, with an average age of 22 years. Most participants were university students who had been recruited through information meetings during lectures. In total, 34 of the participants took part in the experiment with the illumination on. Figure 1. The tunnel used in the road tunnel evacuation experiment.

5 748 K. FRIDOLF ET AL. Figure 2. The tunnel used in the rail tunnel evacuation experiment, seen from above (upper part) and from the side (lower part). Measurements in metres Evacuation of a smoke-filled rail tunnel The rail tunnel evacuation experiment [28, 29] was performed in a tunnel, which previously had been used during the construction of a major road tunnel in Stockholm (the Southern Link). At the time of the experiment, the tunnel was not used for traffic, but occasionally, the Greater Stockholm Fire Brigade used it for firefighting exercises. The length of the tunnel is 300 m, but during the experiment, only the first 200 m was used. For the purpose of the experiment, an emergency exit was installed at a distance of 178 m from the entrance. The emergency exit was a mock-up door, which could not be used to exit the tunnel. A backlit emergency exit sign was mounted above the emergency exit and combined with other way-finding systems in different scenarios, for example, green flashing lights and a loudspeaker. This is described in greater detail in previous publications [28, 29]. During the experiment, the participants entered the tunnel through the tunnel entrance (Figure 2). The direction of travel was from the tunnel entrance to the emergency exit. The experiment was ended when the participant either reached the emergency exit located inside the tunnel or the end of the tunnel. As the emergency exit could not be used to exit the tunnel, the participants were led out of the tunnel by a firefighter after the experiment had ended. The tunnel can be divided into different sections with varying conditions in terms of inclination and floor surface material. The first 122 m of the tunnel has an incline/slope of 10%, and the remaining 76 m is flat (Figure 2). Generally, the floor surface material consists of smooth and compact gravel. However, one part measuring 32 m long and 1.5 m wide is covered with macadam mm in size. This part is located on the right side of the tunnel, about 150 m into the tunnel. The tunnel has a width of approximately 8 m, and a height of approximately 6.5 m (measured in the middle of the cross section). The illumination in the tunnel consisted of wall-mounted emergency lights, which were installed every 8 m on both sides of the tunnel wall. The lights were models of the emergency signage used in the Stockholm Metro at the time of the experiment, and they also included information on distances to the nearest exits. In smoke-free conditions, the lights produced a light intensity of approximately 1 Lx, measured at floor level at the midpoint between the two lights. The smoke density was measured at two locations in the tunnel. These locations are termed equipment 1 and 2 in Figure 2, and both measuring devices were placed in the centre of the tunnel at a height of about 1.5 m. Equipment 3 marks the location of the smoke machine that was used in the experiment. One hundred participants, namely 56 men and 44 women, took part in the experiment. Participants were recruited among the general public and among employees at the Traffic Administration Office in Stockholm. Ages ranged from 18 to 66 years, with an average age of 29 years Summary The two experiments are summarized in Table I, in order to facilitate a comparison between the two set-ups.

6 THE IMPACT OF SMOKE ON WALKING SPEED 749 Table I. A quick comparison between the two experiments included in the paper. Road tunnel Rail tunnel Dimensions (L W H) (m) Ground material Solid, asphalt material In general smooth, compact gravel; in one section, a macadam material with a size of mm Inclination No inclination Inclination corresponding to approximately 10% in one section Obstacles Cars No obstacles Lighting conditions 2 21 Lx without smoke, Approximately 1 Lx with smoke 0 8 Lx with smoke Participants 46, of which 36 are included in the analysis; 30 men and 16 women, with ages ranging from 18 to 29 years 100; 56 men and 44 women, with ages ranging from 18 to 66 years 3. RESULTS In the present paper, data on the individual walking speeds from the two experiments are presented, in terms of both the measured extinction coefficient and visibility. Note that the walking speeds are mean values of the whole evacuation for each participant. The visibility, V (m), is estimated from the extinction coefficient, C s (m 1 ), using the following equation [30]: V ¼ 2=C s (2) In the literature, a value between 2 and 4 is suggested to represent the visibility for a reflecting sign. The minimum value of 2 may be applicable for any other object [29]. In total, 133 data points were derived from the two evacuation experiments: 34 data points from the road tunnel evacuation experiment [26, 27] and 99 data points from the rail tunnel evacuation experiment [28, 29]. Because of a recording error during one of the evacuations in the rail tunnel evacuation experiment, one participant was excluded in the analysis. The average walking speed of each participant is presented as a function of the extinction coefficient (derived from Eq. 1) in Figures 3 and 4 and as a function of the visibility (derived from Eq. 2) in Figures 5 and 6. Also included in the figures are linearly fitted curves, which are the result of linear least square regression analyses, performed in MATLAB R2012b ( ) 64-bit (maci64) for Mac below X, based on the pooled data from the two experiments. The 1.6 Fridolf et al. (2013) Frantzich and Nilsson (2003; 2004) 1.4 Walking speed [m/s] Extinction coefficient [1/m] Figure 3. The average walking speed of each participant as a function of the extinction coefficient, together with the linear regression line and the prediction bounds for the fitted curve (95% confidence level).

7 750 K. FRIDOLF ET AL. 1.6 Fridolf et al. (2013) Frantzich and Nilsson (2003; 2004) Walking speed [m/s] Extinction coefficient [1/m] Figure 4. The average walking speed of each participant as a function of the extinction coefficient, together with the linear regression line and the prediction bounds for a new observation (50%, 75% and 95% confidence levels). regression models, including the coefficients (with 95% confidence bounds) and goodness of fits, are presented. Figures 3 and 5 include the data points from the two experiments, the regression line and the lower/upper confidence bounds for the fitted function (with a 95% level of certainty), that is, the prediction bounds for the fitted curve. The width of the interval can be illustrated to answer the following question: With which speed will a person within the population on average be walking at the given smoke density (extinction coefficient/ visibility)? In other words, with a 95% level of certainty, the average walking speed for a person can be expected to be somewhere within the lower and upper confidence bounds illustrated in Figures 3 and 5. Figures 4 and 6 include the data points, the regression line and the lower/upper confidence bounds for a new observation (with 50%, 75% and 95% levels of certainty), that is, the prediction bounds for a new observation. The width of the intervals can be illustrated to answer the following question: With which speed will a randomly selected person within the population be walking at the given smoke density (extinction coefficient/visibility)? In other words, when randomly selecting a new person, the walking speed can (with a 50%, 75% or 95% level of certainty) be expected to be somewhere within the lower and upper confidence bounds illustrated in Figures 4 and Fridolf et al. (2013) Frantzich and Nilsson (2003; 2004) 1.4 Walking speed [m/s] Visibility [m] Figure 5. The average walking speed of each participant as a function of the visibility, together with the linear regression line and the prediction bounds for the fitted curve (95% confidence level).

8 THE IMPACT OF SMOKE ON WALKING SPEED Fridolf et al. (2013) Frantzich and Nilsson (2003; 2004) Walking speed [m/s] Visibility [m] Figure 6. The average walking speed of each participant as a function of the visibility, together with the linear regression line and the prediction bounds for a new observation (50%, 75% and 95% confidence levels). The linear regression model illustrated earlier, based on the measured extinction coefficient, is defined by the function f(x)= p1*x + p2, where the coefficients (with 95% confidence bounds) are p1= ( , 0.113) and p2 = (1.088, 1.267). The value of R 2 = , that is, approximately 41% of the variation in walking speed among the 133 participants, is, with this model, explained by the extinction coefficient. The linear regression model illustrated earlier, based on the estimated visibility, is defined by the function f(x) =p1*x + p2, where the coefficients (with 95% confidence bounds) are p1 = (0.4373, ) and p2 = (0.1864, ). The value of R 2 = , that is, approximately 36% of the variation in walking speed among the 133 participants, is, with this model, explained by the visibility. An alternative to the previous presentation is to present the walking speed as a function of the extinction coefficient (or the visibility) within defined intervals. It may, for example, be argued that the data do not reveal a linear trend and should therefore not be treated in a linear regression analysis. Furthermore, the residuals, for example, the distance from a data point to the fitted curve, seem to be greater in the rail tunnel evacuation experiment [28, 29] when compared with the road tunnel evacuation experiment [26, 27]. As an alternative, values of the mean and standard deviation of the walking speed are presented for defined intervals in Table II and illustrated as box plots in Figure 7. The width of the intervals is based on the visibility interval of the experiments, for example, approximately m (e.g. Figure 5). The entire interval was divided into five smaller intervals of 0.27 m each, mainly for illustrative purposes, but the number of subintervals may be increased or decreased, as long as the number of observations within each interval allows a comparison between them. Also included in Table II are the results of one-tailed, independent-samples t-tests (α = 0.05), of the interval means, in which the mean value of the current interval was compared with the mean of the next interval. The null and alternative hypotheses are defined as H 0 μ i = μ i+1 H 1 μ i > μ i+1 where μ i is the mean walking speed in the current interval and μ i+1 is the mean walking speed in the next interval. As can be seen in Table II, the mean walking speed in intervals 2 and 4 were significantly higher than the mean walking speed in intervals 3 and 5, respectively. The boxes in Figure 7 should be interpreted in the following way: The line inside each box is the median of the average walking speeds by the participants included in the specific interval, that is, the 50th percentile. Thus, half of the participants in the interval walked slower, and half of the participants faster, for that particular smoke density.

9 752 K. FRIDOLF ET AL. The tops and bottoms of each box are the 25th and 75th percentiles of the average walking speeds by the participants included in the specific interval. Thus, 25% of the participants in the interval walked slower than the 25th percentile, and 25% of the participants walked faster than the 75th percentile. The lines extending above and below each box are the T-bars, that is, the whiskers, and represent the minimum and maximum average walking speeds by the participants included in the specific interval (excluding any extreme values, i.e. outliers). The whiskers extend 1.5 times the height of the box or to the minimum/maximum value if there is no data point in the sample that is in that range. The + are the outliers, that is, average walking speeds more than 1.5 times the interquartile range away from the top or bottom of each box. By studying Figure 7, it becomes clear that the variation is somewhat smaller in intervals 1 and 5 than in intervals 2 4. As the data points in these two intervals are smaller than in the other three, it cannot be explained as an effect of more data points. Instead, a possible explanation could be that in relatively unimpeded environments, as well as in dense smoke with low visibility, people tend to walk at similar speeds. However, at intermediate smoke densities, the variation may be larger among individuals. In previous studies, the walking speed in smoke has been demonstrated to reduce as the visibility decreases, and the results presented earlier conform to these observations. This seems natural and Table II. A representation of the mean walking speed in defined intervals of the extinction coefficient/ visibility, and the results of one-tailed, independent-samples t-tests (α = 0.05) of the interval means between the intervals. Interval Walking speed [m/s] t-test No. Extinction coefficient (m 1 ) Visibility (m) n Mean Standard deviation t df Significance 1 ( ] ( ] ( ] ( ] ( ] ( ] ( ] ( ] ( ] ( ] The symbol ( indicates that the interval endpoint is to be excluded from the set and ] that the interval endpoint is to be included Walking speed [m/s] Interval 1 Interval 2 Interval 3 Interval 4 Interval 5 Figure 7. Box plots illustrating the recorded walking speed within the defined intervals (as described in Table II).

10 THE IMPACT OF SMOKE ON WALKING SPEED 753 can be explained by the fact that the participants lose their orientation and are afraid to walk into an obstacle, which is not visible to them. Both experiments revealed that in order to cope with the situation in the dark and smoke-filled tunnels, a great majority of the participants used and walked along the tunnel walls to facilitate orientation [26 29]. In addition, the analysis of the walking speeds indicates that people walk faster if they follow a wall. It was also noted that the participants in both experiments used their perception of touch to a greater extent compared with people that are walking in normal smoke-free conditions. In the preceding presentation, the walking speed is presented as mean values of the whole evacuation for each participant. Still, it must be emphasized that the walking speed may have varied in the different parts of the tunnels because of a number of reasons. For example, learning effects and/or fatigue may have affected the walking speed at different locations in the tunnels. In addition, the slope with a 10% inclination in the rail tunnel evacuation experiment, as well as the coarse macadam material, may also have affected the walking speed. However, when studying the individual walking speeds in the rail tunnel evacuation experiment, no significant differences can be identified in terms of walking speed related to the distance walked in the tunnel [28, 29]. Neither can a significant difference in walking speed be distinguished when the individual walking speeds are measured, divided per the different sections in the rail tunnel evacuation experiment: (1) 10% inclination; (2) flat with no inclination; and (3) coarse macadam with no inclination, which is illustrated in Figure 8 [29]. In a normal situation, the walking speed could be expected to vary at these different locations. But as people are subject to thick (and to some extent irritating) smoke, in an unfamiliar environment, it is hypothesized that they are already walking so slow that the effect from an inclination or a coarser floor material will have a very limited effect on the walking speed Impact of other variables Independent of whether the walking speed is described as a function of the extinction coefficient or as a function of the visibility, the value of R 2 (0.41 and 0.36, respectively) indicates that other variables are correlated with the walking speed in the experiments. These variables could, if identified, increase the robustness of the regression model by further describing the variation in walking speed among the participants. In order to improve the model, the independent variables must, however, reveal a relationship with the dependent variable (the walking speed). A number of demographic variables related to the participants were examined in an attempt to improve the model: (1) gender; (2) age; and (3) height. The first of these variables is a binary discrete variable, that is, a categorical variable that can only hold one of two values. The latter two are continuous variables, that is, variables that can hold any value. Note that information about all 2.00 Movement speed [m/s] Part of the tunnel 3 Figure 8. The individual walking speeds of the participants in the rail tunnel evacuation experiment in the different parts of the tunnel. Reproduced from [29] with permission.

11 754 K. FRIDOLF ET AL. Gender Height Age Speed Speed Age Height Gender Figure 9. A scatterplot matrix illustrating the relationships (or lack of) between the individual walking speeds of the participants (speed), their age (age), height (height) and gender (gender). variables was collected in the rail tunnel evacuation experiment; however, only information about the participants gender and age were collected in the road tunnel evacuation experiment. As a first step, the average walking speed of each participant was individually plotted against the independent variables selected for the analysis in a scatterplot matrix. The result is demonstrated in Figure 9. Considering the relationship between the walking speed (termed speed) and the three new independent variables (termed age, height and gender), the linear relationship appears to be weak; in all cases, R 2 is less than Actually, the only clear relationship that appears is the one between gender and height; in general, the male participants in the two experiments appear to have been somewhat taller than the female participants. Not only are the relationships between the three new independent variables and the walking speed weak, they are also not demonstrating a significant relationship with the walking speed when included in the regression models presented earlier (where the independent variable is solely the extinction coefficient or the visibility). This is revealed in a t-test (α = 0.05) for each of the independent variables in the model. The only independent variable demonstrating a significant relationship to the walking speed is the already included extinction coefficient (or the visibility, depending on which of the models is being considered). In other words, the robustness and the explanatory value of the original regression models are not improved by including the participants gender, age or height as descriptors. This may, however, be the case in a normal situation (i.e. that a person s height affects the walking speed). But as mentioned before, in an unfamiliar, dark, smoke-filled and irritating environment, it could be that the participants were already walking so slow that the negative effect, for example, of age, did not limit the walking speed further. This may also apply to, for example, disability. 4. DISCUSSION In the present paper, data points on walking speed in smoke from two full-scale evacuation experiments have been derived and presented together. In addition, two alternative techniques to present the correlation between walking speed and extinction coefficient (or visibility) have been

12 THE IMPACT OF SMOKE ON WALKING SPEED 755 included, that is, as single data points in a scatterplot together with linearly fitted curves and as box plots for defined intervals. Yet nothing has been said about how to treat the data for fire safety design purposes. In this section, a discussion is, therefore, presented on how walking speed in smoke may be treated in egress calculations. As mentioned in the introduction to this paper, the use of the data on walking speed in smoke can be expected in, for example, performance-based fire safety design. However, when the designer uses the data, consideration must be given to how the data should be interpreted. A performance-based design solution verification can use a range of methods, depending on how the designer chooses to manage the inherent uncertainty in the design. A typical way is for a designer to represent the possible outcomes of a fire in a building with a set of representative scenarios. In this way, the representative scenarios cover most of the possible scenarios in the building; that is, they are chosen, or derived, in a conservative manner, with the intention that the probability of obtaining a situation worse than the representative scenario shall be small. In the introduction to this paper, this method is termed deterministic analysis or scenario analysis. As an example, related to visibility, the designer should in the deterministic analysis choose a relationship between walking speed and smoke density, so that the result is considered conservative [31], that is, follows the lower points in Figures 3 6 (or the lower confidence bound of the fitted curve for a new observation). The reason for selecting the points in the lower part of the figures is to consider the uncertainty in walking speed and to base the design on the most vulnerable people assumed to be present in the room of consideration and subject for design. If the designer knows exactly what occupancy type can be expected, and to design for, and at the same time have access to data on the speed visibility relation for the population, that relation can (and should) be used directly. Unfortunately, the designer is most likely to lack information on the occupancy s speed visibility relation, as the population seldom is known. Therefore, the designer has to look at the data available and assume that a lower percentile of the data available is a good enough representation of the expected speed visibility relation for the more vulnerable persons in the population. If, on the other hand, the verification is performed using a QRA method, another strategy should be assumed when using the data. A QRA represents a likely description of the consequences with consideration of the frequency for each scenario. The QRA is usually linked to an event tree approach with a large number of scenarios, which in turn is the way to manage the uncertainty. The uncertainty in speed visibility can then be managed as separate scenarios in the event tree. Depending on the needed accuracy, the number of scenarios representing the uncertainty in speed visibility has to be determined. Thus, the basic data are the same, but depending on how uncertainties are managed, either a lower percentile or the average value in combination with the variability that are chosen for the analysis is selected for deterministic analysis and QRA, respectively. Often, computer models are used to facilitate egress calculations in performance-based fire safety design. If a computer model is used, the population of agents could (in terms of walking speed in smoke) be defined to follow a specified correlation within the bounds illustrated in Figures 3 6, in order to enable modelling of the average decrease in speed as a function of smoke density. If needed for the analysis, that is, in a QRA method, a normal distribution of individual speeds at any specific smoke density could be used around the average line in order to manage the inherent uncertainty at the chosen level. By using, for example, a Monte Carlo method, the agents deviations from the average line could be randomly selected in a number of simulations in order to map the probability of different outcomes. As the deterministic analysis approach by far is the most common in performance-based fire safety design, the designer has to get some guidance on how conservative the calculation needs to be. As a first approximation, as has been indicated earlier in the discussion, a lower percentile can be used. In Figure 7, a summary of the data from the experiments is illustrated in terms of box plots, where the data are separated in five intervals. The reason for presenting the data in intervals, and not just as a single mean line with the confidence bounds, is that the number of data points is unevenly distributed along the visibility range for the experiments presented in this paper. A better prediction is provided with a lower degree of uncertainty when the data are presented in intervals, which in turn provides the designer with a not-too-conservative solution. The first approximation for the designer can, therefore, be the lower confidence bound, which represents the walking speed that is

13 756 K. FRIDOLF ET AL. exceeded in 97.5% of all cases in the experiments. For design purposes, it is assumed, as an approximation, that the walking speed shall be constant within each visibility interval. The next question is what happens outside the range of the analysis? It can be argued that movement in heavy smoke is similar to movement in complete darkness. The reason for this is that movement patterns are similar, which was observed in the experiments. This means that in situations with a very high smoke density, a walking speed threshold seem to exist, and for design purposes, a level around 0.2 m s 1 may be used. In the other end of the scale, that is, when the visibility is higher than 1.7 m, it may be argued that visibility does not affect the walking speed. Note, however, that the suggested values stem from observations under the specific condition of a lighted tunnel with or without obstructions and that the variability between individuals is large (Figures 3 6), which implies that some persons find also this level of smoke density difficult to move in. Furthermore, previous studies in real fire smoke have indicated that some people in some building scenarios might slow their walking speed significantly already at visibilities between 2 and 3 m and walk as if in darkness at anything below 1-m visibility [16 18]. This must be considered in the design, for example, defining unimpeded walking speed in a conservative manner even for low-smoke-density situations. In some occasions, the designer may be interested in group interactions and evacuation of groups in case of fire. From the two experiments presented in this paper, it is, however, difficult to use the data for these design cases, as the data have been collected by observations of individual evacuations. One strategy could be to derive n number of speed visibility correlations (where n corresponds to the number of group members), from the data that have been presented in this paper, and to select the slowest person s walking speed as a design movement speed for the group. This assumes that the group will stick together during the entire evacuation. However, the derivation of the walking speed for each individual must still be conditional of the chosen analysis method. Just selecting an average walking speed, on the basis of the data that have been presented earlier, to represent the group s movement may not always be appropriate. Another interesting aspect to consider is which of the following two main approaches is most appropriate in terms of representation of an individual s walking speed in smoke: (1) a fractional reduction of unimpeded walking speed or (2) an absolute reduction of the walking speed. The first approach, that is, the fractional reduction, is based on the assumption that the reduction of walking speed as a function of smoke density (or visibility) is related to the unimpeded walking speed. This approach implies that there is a relationship between unimpeded walking speed and the ability to walk through smoke. However, it seems likely that unimpeded walking speed is mainly related to physical characteristics, for example, people s height or mobility, whereas the ability to move through smoke is related to a great extent to perception, that is, vision, or emotional aspects, such as fear of tripping. Although there might be a slight correlation between these factors (as an example, age may influence both mobility and perception), they are not necessarily directly coupled. Consequently, it seems more reasonable that the second approach, that is, an absolute reduction of the walking speed, is the more valid one. According to this approach, people move at their unimpeded walking speed until the smoke becomes too dense, and they are forced to slow down. In this case, the curve describing the speed reduction can be chosen independent of the unimpeded waking speed. This, in turn, does not imply a direct relationship between unimpeded walking speed and the reduction. However, it should be said that because of the limited available data, it is difficult to directly determine which of the two mentioned approaches is most suitable. It is, therefore, recommended that new experiments be performed. In these experiments, the unimpeded walking speed of each participant should first be measured for clear conditions. This activity should then be followed by a test of each participant s walking speed at different smoke densities. It is only when this type of data is available that it will be possible to determine which approach is most accurate. It must be emphasized that all data regarding visibility and walking speed in this paper are related to smoke with a mild irritant additive. The acetic acid used to simulate irritant smoke is most likely not as irritant as fire smoke, at least not in the visibility range relevant for this paper. However, because of ethical considerations in the experiments, irritant levels higher than those used were not considered. According to the participants in the rail tunnel evacuation experiment [29], the concentration levels used were not enough to create an irritant environment, and there is consequently an uncertainty on whether or not the scenario was any worse than if glycerol had been used alone to produce the

14 THE IMPACT OF SMOKE ON WALKING SPEED 757 smoke. There is, therefore, a question of whether or not the results from the experiments are representative for fire smoke. As mentioned in the introduction to this paper, there are other research results available, for example, the data by Jin et al. [16 18], Wright, Cook and Webber [19], and Galea et al. [20] and also these investigations have limitations regarding the possibility to use the data for design. The data by Jin et al. are used in many applications, as they are one of the first quantitative results linking visibility to walking speed. Jin et al. have presented data on both irritant and non-irritant smoke, and their data are, therefore, considered very useful. However, the data presented in the papers imply that the variability between individuals is large, and the data may be considered limited as the number of subjects is rather low and also within a very small extinction coefficient/visibility interval [16 18]. The same goes for the previously performed experiments; there is a limitation using the new data from the experiments presented in this paper, but by knowing about this and acknowledging this fact, it is argued that the data are still useful also for design purposes. There are a number of reasons why only data from two evacuation experiments were included in the analysis that has been presented in the present paper. Obviously, other data sets do exist. Firstly, the selection of the data sets was motivated by the experimental methodology similarities, as mentioned earlier. Secondly, the other data sets mentioned previously in this paper have either of the following characteristics: (1) have a relatively unclear presentation of the experimental methodology, the data collection and/or the analysis; (2) include experimental differences; or (3) are presented in a different way. One reason could simply be that the main purpose of some of the previous studies has not been to primarily study the impact of smoke on walking speed, but, for example, the performance of technical installations in smoke. Independent of reason, it was deemed difficult to combine other data sets with the data included in this paper. It must still be noted that the included data are a combination of results from two different evacuation experiments performed at different times. In addition, the experiments were performed in different tunnels with different lengths and environmental settings. Although similar in methodology, there is an obvious risk of hidden uncertainties, especially when generalizing the data in, for example, an equation derived from a regression analysis. The main problem is that uncertainties related to each experiment become hidden in one equation. However, by being transparent with the source of each data point, it is argued that the combination of the data leads to an increased generalizability of the results. It all does, however, come down to the fire safety designer, who needs to consider the limitations related to the data representation when using it. An alternative approach could simply be to use the data from the two experiments separately or to include more data sets to represent the correlation between walking speed and visibility. An important aspect of this paper is the quality of the data, and how it can and should be used. Often, this is discussed in terms of internal and external validity, that is, if cause and effect relationships have been correctly described (internal), and to what extent the data and relationships can be used (external) [32]. Following the terminology suggested by Nilsson [32], both experiments included in the present paper should be categorized as laboratory experiments, that is, experiments performed in a controlled environment. On the other hand, although the experiments did not employ real tunnels used for traffic, it is argued that both experiments included in the present paper were performed in environments that the participants could encounter in real life during fire. Consequently, because the data stem from realistic environments with representative participants, it is argued that the external validity is relatively high. Some factors that decrease the external validity are, however, that the experiments were not completely unannounced. Because of ethical reasons, the participants had received some information before the experiment (although not about the purpose). Furthermore, a firefighter was always present during the evacuations, and the participants performed the evacuations individually. The latter makes it difficult to say anything about group interactions, and similar affiliative behaviours that may arise during a real evacuation. When stating that the external validity is high, underground transportation systems are especially being considered as the experiments were performed in similar environments. As traditional buildings differ from the experimental environments, it is not unlikely that people will behave differently (e.g. because the environment is more familiar), and consequently, the external validity may be lower when considering these structures. On the other hand, traditional buildings are, as opposed to underground transportation systems [13 15], seldom designed for people having to evacuate in smoke [24,33].