- Email: [email protected]
Construction of explanatory fire-loss model for buildings
ARTICLE IN PRESS Fire Safety Journal 44 (2009) 1046–1052
Contents lists available at ScienceDirect
Fire Safety Journal journal homepage: www.elsevier.com/locate/firesaf
Construction of explanatory fire-loss model for buildings Yuan-Shang Lin , Chih-Hsin Lin, Po-Chuan Huang Department and Graduate School of Fire Science, Central Police University, Kwei-San, Ta-Kang, Tauyuan, Taiwan 333, ROC
a r t i c l e in f o
a b s t r a c t
Article history: Received 9 January 2007 Received in revised form 28 January 2008 Accepted 20 July 2009 Available online 19 August 2009
This study focuses on investigating the affecting factors and constructing an explanatory model for fire losses. The research was carried out using a series of questionnaires and official reports. A total of 918 cases of residential building fires in Taiwan from January 1998 to February 2002 are examined. The contents of the investigations include attributes of occupants and building fire safety, time and spatial attributes of fire occurrence, fire development and egress, fire brigade interventions and the resulting fire losses. Using factor analysis, correlation analysis, regression analysis and path analysis, significant factors that have been identified, including degree of fire severity, dispatched fire-fighting forces, control time, degree of difficulty of fire egress, partition structure, time of fire occurrence, accessibility and conditions of fire-fighting and situations of escape routes are conceptualized and examined. The relative weights of importance among these factors on fire losses are analyzed as well. Connecting the identified significant factors by time sequence of fire occurrence and development, an explanatory model is proposed to explain fire loss for residential building. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Explanatory fire-loss model Degree of fire severity Control time Difficulty of fire egress Regression model Path analysis
1. Introduction More than twelve thousand fire incidents occur in Taiwan every year, which results in 290 deaths, 670 injured, in addition to 35 billion $NT of direct fire losses (including loss of property and building damage) [1]. Of these fire incidents, more than a half occur in residential buildings [1–2]. Accordingly, this study focuses on searching for the factors that affect fire losses in residential buildings. These are defined as the buildings where people live and are engaged in activities, etc. It is very difficult to use well-defined variables or factors to quantify the losses in residential building fires. However, various studies show that the possible factors that influence fire-related losses (life or financial losses) are multi-dimensional [3]. Some factors are still unknown. However, research on fire losses and practical experiences of the fire professions suggest that some of the following factors should be examined. There are attributes in relation to occupants that include knowledge, habits of fire prevention and fire management within the buildings [4–5]; attributes of building fire safety, for example building structure, location of escape routes, accessibility and potential situations of fire fighting and rescue and active as well as passive fire protection systems [6–7]; time and spatial attributes of fire occurrence, which include time of day, season, location of the fire within the building, the type of building occupancy, presence of iron-barred windows and numbers of fire exits in the Corresponding author. Tel.: +886 3 3282321x4796; fax: +886 3 3282321x4447.
E-mail address: [email protected] (Y.-S. Lin). 0379-7112/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.firesaf.2009.07.005
building; characteristics and severity of the fire development and evacuation difficulties [1,8–11]; fire brigade interventions such as travel distance, attendance time, control time, extinguishment time of fire and dispatched fire-fighting forces (fire services) [6,12–15]. All the above-mentioned possible influencing factors that may or may not be inter-related are considered as the explanatory variables in this study. The resulting fire loss is the dependent variable. Research on individual effect on fire losses for all the possible affecting factors may have been investigated elsewhere [4–8,11–16]. However, a systematic, simultaneous and comprehensive examination of all the above explanatory variables on fire losses is studied within this report. This research aims at investigating whether there is a particular set of characteristics related to occupants, building, fire growth and fire brigade interventions that could explain the fire losses. This study differentiates from the so-called scenario-based or engineeringbased fire-loss modeling by investigating fire loss from a socioeconomic point of view. If we can study the affecting factors that significantly contribute to fire losses, and subsequently quantify the relative effects by examining the real fire cases and propose good preventive suggestions, then fire safety research could be improved and fire loss could be brought under control.
2. Framework of explanatory model Fig. 1 illustrates the hypothetical framework of an explanatory fire-loss model, which shows possible relationships between the
ARTICLE IN PRESS Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
fire loss (dependent variable) and the explanatory variables (independent variables). The object of this framework is to determine whether such systematic relationships shown in Fig. 1 between the fire loss and a wide variety of habitual, structural, timing, fire development, evacuation and fire-fighting variables exist. Fire loss in this study is defined as the direct fire losses due to property losses and building damage, as determined by fire brigades. In order to obtain an overall understanding how to describe fire losses, all possible and time-dependent influencing factors should be considered in the proposed hypothetical model. The explanatory model is constructed in accordance with the time sequence, namely pre-fire, during the fire and post-fire (the resultant fire loss). All possible inter-related factors, concepts (scales) and variables of this study are shown in Fig. 1. From Fig. 1, this research assumes that the attributes of occupants and building fire safety, the time and spatial attributes of fire occurrence, the fire development and egress and the fire brigade interventions influence the fire losses in the case of a residential building fire. However, the relationship between these factors and the relative effects on the fire losses in Taiwan (or any other countries) could be examined by either empirical surveys or available fire statistics.
3. Research design 3.1. Questionnaire design In Taiwan, every fire incident attended by fire brigade is reported systematically in a standardized format designed by National Fire Agency for local fire departments. There is a substantial amount of good material on fire statistics in these reports. However, most fire statistics do not cover the actual performance of the fire brigade and the relationship between the fire-fighting operation and the resulting fire losses. It is reasonable to assume that residential buildings have similar fire-loss risks, since the occupants have similar living habits. In this small island, Pre-fire
fire brigades having similar fire-fighting techniques and building structures in the sample are much the same (most of them are reinforced concrete). It would be useful if behaviour characteristics regarding fire prevention, fire-source management relating to occupants and building attributes relating to fire safety and egress conditions could be obtained before a fire and then combined together with the fire department records. In an attempt to grasp the idea mentioned above, contexts of the investigations in this study try to include the data not only during the fire and after the fire but also before the fire. To establish an understanding in the relationship between these factors, all of them are determined through two questionnaires. The first questionnaire, which is designed for persons who lived in or owned fire-hit residential buildings, focuses on the habitual and structural variables relating to the occupants and the pre-fire characteristics. The other questionnaire is designed for the local fire department that attended the fire. The questionnaires are examined carefully over 9 sessions and then refined after a pre-test. This is to establish the pertinent factors that affect the outcome and present the results in such a way that the answers can be extracted without ambiguity. The questionnaires are distributed and recalled by the firemen, who were in charge of the general affairs of fire safety at the local fire substation. Firstly, the firemen ask for permission to interview the fire-hit residents and make sure that the persons (including the persons who were there during the fire or the family members, or the people who lived there and understood the fire process) who would be interviewed have the knowledge to answer the questionnaires. The second questionnaire elicits information regarding the fire department’s record, including the effects of fire fighting and life rescue during the fires. As aforementioned, the task of completing these two questionnaires to collect meaningful data does not seem easy. The authors are very grateful to firemen and the fire-hit families who kindly and bravely provided the time and help even though they have encountered the unwanted tragic event. The present study concerns 918 fire cases in residential buildings from 1998 to 2002.
During fire
Occupant Attributes
1047
Post-fire (the resulting fire loss)
Attributes of Time, Space and People
♦ Knowledge of ♦ Time Attributes
Fire Prevention ♦ Fire Management
♦ Spatial Attributes ♦ People Attributes
Attributes of Building Fire Safety
Fire Development and Egress ♦ Degree of Fire Severity
♦ Structures of Building and
♦ Degree of Difficulty of Fire Escape
Partition ♦ Situations of Escape
Fire Losses (Monetary Losses of Property and Building Damage)
Routes ♦ Accessibility and Conditions of Firefighting ♦ Fire Protection Systems
Fire Brigade Intervention ♦ Response Time ♦ Control Time ♦ Extinguishing Time ♦ Dispatched Fire Services
Fig. 1. Hypothetical framework of explanatory fire-loss model.
ARTICLE IN PRESS 1048
Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
3.2. Contents of questionnaires (research tools) The contents of questionnaires consist of independent and dependent variables. Independent (explanatory) variables, which include pre-fire and during-fire variables (or intervening variables), may be either a variable or a vector of variables. Table 1 shows the numbers and descriptions of items of independent variables (factors). The pre-fire independent variables include the attributes of occupants and building fire safety. Independent variables regarding during-fire contain time and spatial attributes of fire occurrence, fire development and egress, and fire brigade interventions. As mentioned above, fire loss in this study is defined as the direct monetary losses due to property and building damage, estimated by fire brigades and measured in $NT dollars. The frequency of fire loss in the current study is shown in Table 2. In order to grasp all the ideas of the explanatory factors as much as possible, not only the respondents of the questionnaires need to have the knowledge to be able to answer the questions as mentioned above but also the data measurement needs to be constructed with validity and reliability. Factor analysis based on orthogonal rotation methods with VARIMAX approach [17] is then used to develop factors (concepts, scales and sub-concepts). Several stages of analyzing processes (nine group meetings, pretest, validity and reliability analysis) are performed to justify why the questionnaires are pertinent and comprehensive and clear and precise.
3.3. Measurement of concept For the first concept, attributes of the occupants, eleven individual behaviours representing the attributes generally comTable 1 Items and contents of concepts (independent variables). Concepts Knowledge of fire prevention
Items and contents
6 items: Able to use fire extinguishers, take fire drill, alert to fire alarm, familiar to fire alarm, take firerelated courses and recognize the fire exit sign. Fire management 5 items: Close gas control valve, check electrical outlet, inspect gas valve, remove unused plug and turn off unused electrical sources. Structures of building and 2 items: Structure types of building and material of partition partition wall. Categorical data include concrete, iron, brick and wood members. Situations of escape routes 3 items: Design of escape route, clearance of escape route and stairs. Accessibility and conditions 5 items: Accessibility of fire engines, sufficiency of of fire-fighting fire-fighting water, easy to fire fighting from outside building, narrow situation of street or lane around the building and parking situation around the building. Fire protection systems 8 items: Situations of emergency lighting, exit sign, fire extinguishers, refuge appliances, broadcast facility, alarm systems, automatic fire suppression systems and efficiency of fire protection systems. Attributes of time, space 3 items: 1 item for fire occurrence time; 4 items for and people space including ignition location, ignition floor, situation of iron-barred window and numbers of exits;4 items for people including numbers of young children under 12, elderly people above 65, disabled people and continuous medicine-takers. Degree of fire severity 3 items: Situation of furnish burning, severe degree of first item ignited, amount of first item ignited. Degree of difficulty of fire 6 items: Difficult to escape, smoke spread, narrow escape stair, long travel distance, deficiency of fire exits and entrance being blocked due to smoke. Fire brigade interventions 4 items: Attendance time, control time, extinguishment time of fire and dispatched firefighting force.
Table 2 Frequency of fire losses ( 10,000) (in NT$). Fire losses
%
Below 1 1–10 (not included) 10–50 (not included) 50–100 (not included) 100–500 (not included) Above 500
26.2 36.6 23.8 8.8 3.9 0.7
mitted by occupants in Taiwan are included in the scale. Among the items measured are responses to questions about knowledge of fire prevention (6 items) and fire management (5 items). Respondents were asked whether they had the feelings or experiences as expressed in the 11 items in the past. Each item is scored on a four-point scale: (1) often, (2) sometimes, (3) rarely and (4) never. Then, all scores were re-coded (i.e., 4 ¼ often; 3 ¼ sometimes; 2 ¼ rarely and 1 ¼ never) necessary for the regression analysis. High scores on this scale will demonstrate frequent engagement in fire prevention acts. A composite score is computed by summing the fire-related prevention items. Hence, the latent concepts of occupant attributes are indexed by two independent empirical factors (sub-concepts) in the model. Similar approaches are applied to obtain the scores from other sub-concepts in this study. However, categorical types of sub-concept such as fire occurrence time, building structure, partition structure and the like are measured as dummy variables. The concept of attributes of building fire safety consists of four sub-concepts, including structure of the building and the partitions, location of escape routes, accessibility and conditions of fire fighting and fire protection systems. In the current study, 18-item symptoms are examined. Respondents were asked whether they had any feelings or experiences as expressed in the 18 items in the past. The higher the score, the more frequently a respondent feels the symptom and then a higher score demonstrates a much higher fire safety awareness. Attributes of time, space and people describe the time of fire occurrence and ignition locations, situations of iron-barred windows and numbers of exits (based on official records) and the number of young children under twelve, elderly people above 65, disabled people and continuous medicine-takers in the building during fires. Even though these attributes of time, space and people are categorized together, they are not formed as sub-concepts through factor analysis and they are analyzed separately in this research as well as other studies on life-risk analysis [1,18]. The concept entitled fire development and egress includes two sub-concepts, namely degree of fire severity (3 items) and degree of difficulty of fire escape (6 items). Three questions regarding the destructive effects of fires to the building are designed to measure the degree of fire severity. The degree of difficulty of escape in fire is measured not only by the adequacy and suitability of egress systems but also by the egress accessibility due to smoke effects. The sum of items then represents the individual sub-concept. The higher the score, the more severe a fire is in the degree of fire severity. The higher the score, the more difficulty a respondent or family member experienced in escaping during fires in measuring the degree of difficulty of escape from the fire. The fire fighting, response (attendance) time, control time (time difference between fire incident call received and fire under control by fire brigade), as well as extinguishing time measured in minutes and dispatched fire-fighting forces measured in persons as well as numbers of fire engines attending the fire fighting are assessed
ARTICLE IN PRESS Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
Table 3 Factor analysis and reliability analysis of knowledge of fire prevention. Variable
Factor loading
1. Be able to use fire extinguisher pre-fire 2. Take fire drill pre-fire 3. Be alert to fire alarm pre-fire 4. Be familiar to fire alarm pre-fire 5. Take fire-related courses pre-fire 6. Understand the signs of fire exit
0.649 0.710 0.815 0.826 0.686 0.716
Eigenvalue Cronbach a
3.518 0.814
Table 4 Summaries of factor analysis and reliability analysis for other sub-concepts. Factors (sub-concepts)
Knowledge of fire prevention Fire management Situations of escape routes Accessibility and conditions of fire fighting Fire protection systems Degree of fire severity Degree of difficulty of fire escape
Factor loading; eigenvalue
a
Cronbach
0.649–0.826; 3.254 0.641–0.826; 3.013 0.692–0.877; 2.002 0.602–0.833; 2.503
0.830 0.832 0.889 0.736
0.690–0.872; 5.257 0.729–0.838; 1.879 0.652–0.760; 2.882
0.923 0.687 0.780
from the official fire reports. Fire losses including monetary losses of property and building damage are also obtained from official records. Table 3 depicts the development of the sub-concept, namely knowledge of fire prevention. A reliable (a ¼ 0.830) six-item scale that assessed a variety of variables regarding fire prevention knowledge is shown in Table 3. Using the same technique (orthogonal rotation methods with VARIMAX) to develop the other sub-concepts, summary of analysis results for other subconcepts including the sub-concept entitled knowledge of fire prevention but excluding some sub-concepts due to categorical type or actual numbers is given in Table 4. A commonly used threshold value for acceptable reliability is 0.70. Although this is not an absolute standard, values below 0.70 have been deemed acceptable if the research is exploratory in nature [19]. More reliable measures (Cronbach a) provide the researcher with greater confidence that the individual factors are all consistent in their measurements. The results given in Table 4 seem reasonably acceptable since this research is original and exploratory in nature. However, all the developments of these concepts or sub-concepts may be improved in the future.
4. Analysis and discussion
1049
degree of difficulty of escape from the fire, control time, extinguishing time and dispatched fire-fighting forces have positive correlations with fire loss. The higher the degree of fire severity, degree of difficulty of escape, fire control time, fire extinguishing time or dispatched fire-fighting forces for a fire, the greater will be the fire loss. However, among the significant explanatory variables, building structure and partition structure have negative correlations with fire loss. The negative correlations demonstrate that the lesser the fire resistance of building structure and partition structure, the greater will be the fire loss. Additionally, the correlation relationships between pre-fire variables and during-fire (intervening) variables are also obtained and shown in Table 5. All the statistically significant variables will be included while performing regression analysis in the next section. One might think that some of the corresponding correlation coefficients given in Table 5 are small (even though statistically significant). Possible reasons are the sample size, and that the measurement is ‘‘science-based’’ (i.e. social sciencebased) instead of ‘‘engineering-based’’ (i.e. fire load survey, fireresistance rating and opening factor) approach, etc. Therefore, further refinement of the investigation needs to be carried out.
4.2. Regression analysis Due to its duration in special fire cases, extinguishing time is less appropriate than control time, which is critical for measuring the fire-fighting operation and is not included in regression analysis. By similar reasoning, building structure is less appropriate than partition structure, i.e. a critical factor for measuring control time, which is not included in the regression analysis either. Based on five significant independent variables, the explanatory model for fire loss will be constructed by employing step-wise regression analysis. Taking the degree of fire severity, the degree of difficulty of escape, the control time, the dispatched fire-fighting forces, and the partition structure as the independent (explanatory) variables to predict the dependent variable, a regression model to explain fire loss is shown in Table 6. In addition, the regression results of taking some intervening variables, namely degree of fire severity, control time and dispatched fire-fighting forces as dependent variables, are also shown in Table 6. Including a large number of predictor variables in a regression model is never a good strategy, unless there are strong reasons to suggest that they should be included. As mentioned above, only the statistically significant correlated variables in Table 5 have been included in regression analysis. For the present data set, the best regression model for relating the independent variables to dependent variables is shown in Table 6 and can be written as the following regression equations. The definitions of variables are also shown in Table 6. Y ¼ 6:599 þ 4:945X2 þ 0:153X4 þ 0:175X5 X2 ¼ 1:531 þ 0:228X1 þ 0:285X3 0:200X6
4.1. Correlation analysis In order to examine the relationships between the independent variables and the dependent variable, a correlation analysis is performed. Fourteen possible influencing factors or variables shown in Fig. 1 or Table 1 are measured from the survey data. Seven out of fourteen variables listed in Table 5, namely degree of fire severity, degree of difficulty of fire escape, control time, extinguishing time, dispatched fire-fighting forces, building structure and partition structure, have statistically significant relationships on fire loss at the level of significance 0.05. Among the significant explanatory variables, degree of fire severity,
X4 ¼ 14:517 þ 1:989X2 0:864X7 X5 ¼ 33:842 þ 5:765X2 þ 0:620X4 1:263X8
In this study, independent variables such as degree of fire severity, control time and dispatched fire-fighting forces have significant effects on fire losses. From the estimated regression coefficients shown in Table 6, one can understand that among these significant variables, degree of fire severity has the most important effect due to the largest standardized regression coefficient in the model (b ¼ 0.299; po0.001). Fire losses can be reduced by lessening the degree of fire severity, control time and
ARTICLE IN PRESS 1050
Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
Table 5 Correlation coefficients between factors and fire losses. Factor
Fire losses
Degree of fire severity
Degree of difficulty of fire escape
Attendance time
Control time
Extinguishing time
Dispatched fire-fighting forces
Fire losses Degree of fire severity Degree of difficulty of fire escape Attendance time Control time Extinguishing time Dispatched fire-fighting forces Knowledge of fire prevention Fire management Situations of escape routes Accessibility and conditions of fire-fighting Fire protection systems Fire occurrence time Building structure Partition structure
1.000 0.398** 0.079* 0.036 0.282** 0.232** 0.367** 0.024 0.008 0.009 0.023
1.000 0.417** 0.032 0.194** 0.242** 0.342** 0.09 0.013 0.087* 0.072
1.000 0.040 0.078 0.064 0.199** 0.077* 0.028 0.187** 0.146**
1.000 0.003 0.016 0.062 0.012 0.052 0.063 0.023
1.000 0.593** 0.349** 0.050 0.005 0.083* 0.044
1.000 0.268** 0.052 0.025 0.070 0.010
1.000 0.054 0.007 0.065 0.167**
0.028 0.076* 0.026 0.027
0.049 0.065 0.008 0.064
0.056 0.100* 0.064 0.055
0.006 0.120** 0.115** 0.117**
0.021 0.127** 0.139** 0.136**
0.018 0.014 0.064 0.135** 0.166** 0.143** 0.159** 0.163**
**po0.01, *po0.05.
Table 6 Regression model for fire losses and some intervening variables. Fire losses (Y) Fire occurrence time (X1) Degree of fire severity (X2) Degree of difficulty of fire escape (X3) Control time (X4) Dispatched fire-fighting forces (X5) Partition structure (X6) Situations of escape routes (X7) Accessibility and conditions of fire fighting (X8) Constant R2
Degree of fire severity (X2)
Control time (X4)
Dispatched fire-fighting forces (X5)
0.228*(0.089) 4.945***(0.299)
1.989***(0.170)
5.765***(0.240)
0.285***(0.440) 0.153**(0.112) 0.175***(0.253)
0.620***(0.312) 0.200**(0.111) 0.864*(0.106)
6.559 0.246
1.531 0.220
14.517 0.043
1.263**(0.123) 33.842 0.203
Note: a. *po0.05, **po0.01, ***po0.001. b. Figures in parentheses are standardized regression coefficients.
dispatched fire-fighting forces. By improving the items of the subconcept entitled ‘‘degree of fire severity’’, that is by limiting the arrangement as well as the amount of furnishings and restricting the use of combustible material (see Table 1), the degree of fire severity in case of a fire can be greatly reduced. The length of fire control time and the number of dispatched fire-fighting forces can be reduced by more advanced techniques and effective fire-fighting training. Fire control time and dispatched fire-fighting forces only explain instead of predicting fire losses since they are usually recorded after the fire incident has occurred and fire losses have been incurred. In addition, it is worth noting that fire control time and dispatched fire-fighting forces are correlated with fire losses, but this does not imply causation, that longer control time or a higher number of fire-fighting forces will not cause a greater fire loss. On the contrary, it is possible that fires with larger losses usually take longer to control and normally require a larger number of fire-fighting units to attend. Even so, both the fire control time and the fire-fighting forces dispatched by fire brigades may provide meaningful information for practicing fire fighting. From Table 6 one can find that the degree of difficulty of escape from the fire influences (or explains) the degree of fire severity most, the degree of fire severity affects control time most and the control time influences the dispatched fire-fighting forces most due to the highest values of standardized regression coefficients in the model (b ¼ 0.440, po0.001; b ¼ 0.170, po0.001; b ¼ 0.312,
po0.001). From Table 6, one can also understand that fire occurrence at night time (22:00–08:00) and less fire resistance of partition structure have significant effects on the degree of fire severity; the poor situations of escape route significantly increase fire control time, and the poor fire-fighting conditions affect the fire-fighting forces significantly. In order to decrease the degree of fire severity, shorten the control time, and reduce the dispatched fire-fighting forces, the significant sub-concepts in Table 1 need to be improved. In this study, path analysis is performed to observe further the relationships between all significant independent variables (including intervening variables) and dependent variables. Fig. 2 shows the relationships and is proposed as the explanatory model for fire losses. From Fig. 2 one can understand that degree of fire severity, control time, and dispatched fire-fighting forces have direct effects on fire losses as mentioned above. Although partition structure, fire occurrence time and the degree of difficulty of escape have no significant or direct effects on fire losses, they have indirect effects on fire losses through the intervening variable (sub-concept) entitled degree of fire severity. The sub-concept entitled ‘‘accessibility and conditions of fire fighting’’ has no significant or direct effect on fire losses, but it has an indirect effect on fire losses via dispatched fire-fighting forces. The sub-concept entitled ‘‘situations of escape routes’’ does not have significant or direct effect on fire losses; however, it indirectly affects fire losses via fire control time. Degree of fire
ARTICLE IN PRESS Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
1051
Fire Occurrence Time (X1)
Partition Structure (X6)
-0.111
0.089 Degree of Fire Severity (X2)
Situations of Escape Routes (X7)
0.440
0.170
Degree of Difficulty of Fire Escape (X3)
Accessibility and Conditions of Firefighting (X8)
0.299 Fire Losses (Y) 0.112
-0.106
Control Time (X4)
0.312
0.240
0.253
-0.123 Dispatched Firefighting Forces (X5) Fig. 2. Proposed explanatory fire-loss model.
Table 7 Effects of independent variable on fire losses. Independent variable
Direct effect
Indirect effects
Total effect
Ranking order
Fire occurrence time (X1) Degree of fire severity (X2) Degree of difficulty of fire escape (X3) Control time (X4) Dispatched fire-fighting forces (X5) Partition structure (X6) Situations of escape routes (X7) Accessibility and conditions of fire fighting (X8)
– 0.299 – 0.112 0.253 – – –
0.034 0.093 0.176 0.078 – 0.043 0.020 0.031
0.034 0.392 0.176 0.190 0.253 0.043 0.020 0.031
6 1 4 3 2 5 8 7
severity not only has significant or direct effect on fire losses but also has an indirect effect on fire losses through fire control time and dispatched fire-fighting forces. Table 7 depicts the direct and indirect effects of all significant independent variables on fire losses. Total effects and ranking orders of all significant independent variables on fire losses are also shown in Table 7. Degree of fire severity has the strongest effect on fire losses, followed by dispatched fire-fighting forces and then followed by fire control time in spite of either direct or indirect situation. Again, improving the items of sub-concept or independent variables (including intervening variables) can reduce dependent variables. One might think that degree of fire severity, control time, dispatched fire-fighting forces, accessibility and conditions of fire fighting, partition structure and other affecting factors conducted by the analysis above are almost surely correlated or significant to fire loss and the results are no great surprise. However, this work is based on a social science approach instead of an engineering one and has examined for the first time multi-dimensional factors including pre-fire and during-fire activities. These have been systematically investigated during the research process and relative effects as well as ranking orders among these significant determinants are achieved in this study. If the engineering-based research can proceed to this stage then specific items can be improved.
5. Conclusion This research identifies some significant determinants and constructs an explanatory model for fire losses regarding the availability as well as use of fire incidence questionnaires and fire reports. From this study some results may be summarized as follows:
a. Propose a social science-based method for fire safety research and then understand the fundamental factors that contribute fire losses from empirical surveys in fires within buildings. b. Construct a valid and reliable model to explain fire losses by taking into account the influencing factors not only prefire (e.g. accessibility and conditions of fire fighting and partition structure) but also during a fire (e.g. degree of fire severity, fire control time, dispatched fire-fighting forces and degree of difficulty of escape from the fire) for residential buildings. c. Degree of fire severity has the greatest effect on the fire losses, followed by dispatched fire-fighting forces and then fire control time, degree of difficulty of escape, partition structure, fire occurrence time, accessibility and conditions of fire fighting, situations of escape routes, etc.
ARTICLE IN PRESS 1052
Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
d. Provide useful and meaningful information for fire prevention, fire practice and fire-loss control. Fire losses can be greatly reduced if occupants, building managers and firefighters can take into consideration the statistically significant risk factors found in this study, while planning or practicing their programmes or activities. This research provides a very good starting point for developing an explanatory model in statistical fire-loss analysis even though some measurements of concepts (or items) regarding questionnaire need to be improved and refined. Undoubtedly, comprehensive fire investigation can lead to understanding the occupant and building attributes and improve pre-fire as well as during-fire activities that will enable better control over the fire growth, fire spread and the resulting fire losses. In order to establish a reliable model, more scientific data on the influencing factors (e.g. fire load and duration of fire resistance of the room of origin) need to be collected in the future. We can expect a gradual advancement in statistical modeling for fire losses as more background information is assembled and organized. Examinations of various relationships between the affecting factors and fire losses by introducing other multivariate analysis such as canonical correlation analysis and LISREL (Linear Structure Relation) will be the focus of future research.
Acknowledgements The authors are very thankful to the firemen and the fireaffected families who kindly and bravely provided the time and help to fill out the questionnaires. This work was funded in part by grant NSC89-2415-H-015-001-SSS, NSC90-2415-H-015-001-SSS and 96-2415-H-015-002-MY2 from the National Science Council, Taiwan, ROC.
References [1] Lin Yuan-Shang, Investigation and Analysis of Fire Losses, first ed., Central Police University Publishing Inc., Tauyuan, Taiwan, 2004 p2. [2] Please visit http://www.nfa.gov.tw/main9.html. [3] L. Yuan-Shang, L. Ho-Shu, Statistical models for fire risk assessment, Journal of Central Police University, Tauyuan, Taiwan 36 (2000) 415–434. [4] Federal Emergency Management Agency, Profile of the urban fire problem in the United States, Technical Report Series, 1999. [5] Federal Emergency Management Agency, Socioeconomic Factors and the Incidence of Fire, Federal United States Fire Administration, National Fire Data Center, Washington, DC, 1997. [6] J. Hausner, W. Walker, A. Swersey, An analysis of the deployment of firefighting resources in Yonkers, R-1566/2-HUD/CY, The New York City Rand Institute, New York, 1974. [7] G. Ramachandran, The Economics of Fire Protection, E&FN SPON, An Imprint of Routledge, London and NY, 1998 p. 67. [8] G. Ramachandran, Statistical methods in risk evaluation, Fire Safety Journal 2 (1979/1980) 125–145. [9] W. Belles Donald, Interior finish, in: Fire Protection Handbook, 18th ed., NFPA, 1997, p. 80 (Section 7). [10] Stollard Paul, Abrahams John, Fire form First Principles, E&FN SPON, 1999, pp. 35, 57, 58. [11] L. Yuan-Shang, L. Ho-Shu, Effects of building firesafety characteristics, egress difficulty and other attributes on life risk, Journal of Central Police University, Tauyuan, Taiwan 37 (2000) 445–476. [12] H.E. Corman, J. Ignall, K.L. Rider, A. Srevenson, Fire casualties and their relation to fire company distance and demographic factor, Fire Technology 12 (3) (1976) 193–203. [13] E.K. Ignall, L. Rider, R. Urbach, Fire Severity and Response Initial Findings, R2013, Rand Institute, New York, 1978, pp. 40–43. [14] Stefan Sardqvist, Goran Holmstedt, Correlation between firefighting operation and fire area: analysis of statistics, Fire Technology 36 (2000) 109–121. [15] Lin Yuan-Shang, Huang Chien-Hua, Effects of fire fighting on fire losses, Journal of Police Science, Tauyuan, Taiwan 33 (2) (2002) 189–204. [16] D.J. Rasbash, G. Ramachandran, B. Kandola, J.M. Watts Jr., M. Law, Evaluation of Fire Safety, John Wiley & Sons, England, 2004. [17] Wang Pao-Ging, SPSS for windows and research on behavior science, Hsin-Li Publishing Ltd., Taipei, Taiwan, 1999 p. 701. [18] Lin Yuan-Shang, Life risk analysis in residential building fires, Journal of Fire Sciences 22 (Nov. 2004) 491–504. [19] F.H. Joseph Jr., R.E. Adnderson, R.L. Tatham, W.C. Black, Structural equation modeling, multivariate data analysis with readings, fourth ed., Prentice-Hall Inc., New Jersey, USA, 1995 p. 641.
Contents lists available at ScienceDirect
Fire Safety Journal journal homepage: www.elsevier.com/locate/firesaf
Construction of explanatory fire-loss model for buildings Yuan-Shang Lin , Chih-Hsin Lin, Po-Chuan Huang Department and Graduate School of Fire Science, Central Police University, Kwei-San, Ta-Kang, Tauyuan, Taiwan 333, ROC
a r t i c l e in f o
a b s t r a c t
Article history: Received 9 January 2007 Received in revised form 28 January 2008 Accepted 20 July 2009 Available online 19 August 2009
This study focuses on investigating the affecting factors and constructing an explanatory model for fire losses. The research was carried out using a series of questionnaires and official reports. A total of 918 cases of residential building fires in Taiwan from January 1998 to February 2002 are examined. The contents of the investigations include attributes of occupants and building fire safety, time and spatial attributes of fire occurrence, fire development and egress, fire brigade interventions and the resulting fire losses. Using factor analysis, correlation analysis, regression analysis and path analysis, significant factors that have been identified, including degree of fire severity, dispatched fire-fighting forces, control time, degree of difficulty of fire egress, partition structure, time of fire occurrence, accessibility and conditions of fire-fighting and situations of escape routes are conceptualized and examined. The relative weights of importance among these factors on fire losses are analyzed as well. Connecting the identified significant factors by time sequence of fire occurrence and development, an explanatory model is proposed to explain fire loss for residential building. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Explanatory fire-loss model Degree of fire severity Control time Difficulty of fire egress Regression model Path analysis
1. Introduction More than twelve thousand fire incidents occur in Taiwan every year, which results in 290 deaths, 670 injured, in addition to 35 billion $NT of direct fire losses (including loss of property and building damage) [1]. Of these fire incidents, more than a half occur in residential buildings [1–2]. Accordingly, this study focuses on searching for the factors that affect fire losses in residential buildings. These are defined as the buildings where people live and are engaged in activities, etc. It is very difficult to use well-defined variables or factors to quantify the losses in residential building fires. However, various studies show that the possible factors that influence fire-related losses (life or financial losses) are multi-dimensional [3]. Some factors are still unknown. However, research on fire losses and practical experiences of the fire professions suggest that some of the following factors should be examined. There are attributes in relation to occupants that include knowledge, habits of fire prevention and fire management within the buildings [4–5]; attributes of building fire safety, for example building structure, location of escape routes, accessibility and potential situations of fire fighting and rescue and active as well as passive fire protection systems [6–7]; time and spatial attributes of fire occurrence, which include time of day, season, location of the fire within the building, the type of building occupancy, presence of iron-barred windows and numbers of fire exits in the Corresponding author. Tel.: +886 3 3282321x4796; fax: +886 3 3282321x4447.
E-mail address: [email protected] (Y.-S. Lin). 0379-7112/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.firesaf.2009.07.005
building; characteristics and severity of the fire development and evacuation difficulties [1,8–11]; fire brigade interventions such as travel distance, attendance time, control time, extinguishment time of fire and dispatched fire-fighting forces (fire services) [6,12–15]. All the above-mentioned possible influencing factors that may or may not be inter-related are considered as the explanatory variables in this study. The resulting fire loss is the dependent variable. Research on individual effect on fire losses for all the possible affecting factors may have been investigated elsewhere [4–8,11–16]. However, a systematic, simultaneous and comprehensive examination of all the above explanatory variables on fire losses is studied within this report. This research aims at investigating whether there is a particular set of characteristics related to occupants, building, fire growth and fire brigade interventions that could explain the fire losses. This study differentiates from the so-called scenario-based or engineeringbased fire-loss modeling by investigating fire loss from a socioeconomic point of view. If we can study the affecting factors that significantly contribute to fire losses, and subsequently quantify the relative effects by examining the real fire cases and propose good preventive suggestions, then fire safety research could be improved and fire loss could be brought under control.
2. Framework of explanatory model Fig. 1 illustrates the hypothetical framework of an explanatory fire-loss model, which shows possible relationships between the
ARTICLE IN PRESS Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
fire loss (dependent variable) and the explanatory variables (independent variables). The object of this framework is to determine whether such systematic relationships shown in Fig. 1 between the fire loss and a wide variety of habitual, structural, timing, fire development, evacuation and fire-fighting variables exist. Fire loss in this study is defined as the direct fire losses due to property losses and building damage, as determined by fire brigades. In order to obtain an overall understanding how to describe fire losses, all possible and time-dependent influencing factors should be considered in the proposed hypothetical model. The explanatory model is constructed in accordance with the time sequence, namely pre-fire, during the fire and post-fire (the resultant fire loss). All possible inter-related factors, concepts (scales) and variables of this study are shown in Fig. 1. From Fig. 1, this research assumes that the attributes of occupants and building fire safety, the time and spatial attributes of fire occurrence, the fire development and egress and the fire brigade interventions influence the fire losses in the case of a residential building fire. However, the relationship between these factors and the relative effects on the fire losses in Taiwan (or any other countries) could be examined by either empirical surveys or available fire statistics.
3. Research design 3.1. Questionnaire design In Taiwan, every fire incident attended by fire brigade is reported systematically in a standardized format designed by National Fire Agency for local fire departments. There is a substantial amount of good material on fire statistics in these reports. However, most fire statistics do not cover the actual performance of the fire brigade and the relationship between the fire-fighting operation and the resulting fire losses. It is reasonable to assume that residential buildings have similar fire-loss risks, since the occupants have similar living habits. In this small island, Pre-fire
fire brigades having similar fire-fighting techniques and building structures in the sample are much the same (most of them are reinforced concrete). It would be useful if behaviour characteristics regarding fire prevention, fire-source management relating to occupants and building attributes relating to fire safety and egress conditions could be obtained before a fire and then combined together with the fire department records. In an attempt to grasp the idea mentioned above, contexts of the investigations in this study try to include the data not only during the fire and after the fire but also before the fire. To establish an understanding in the relationship between these factors, all of them are determined through two questionnaires. The first questionnaire, which is designed for persons who lived in or owned fire-hit residential buildings, focuses on the habitual and structural variables relating to the occupants and the pre-fire characteristics. The other questionnaire is designed for the local fire department that attended the fire. The questionnaires are examined carefully over 9 sessions and then refined after a pre-test. This is to establish the pertinent factors that affect the outcome and present the results in such a way that the answers can be extracted without ambiguity. The questionnaires are distributed and recalled by the firemen, who were in charge of the general affairs of fire safety at the local fire substation. Firstly, the firemen ask for permission to interview the fire-hit residents and make sure that the persons (including the persons who were there during the fire or the family members, or the people who lived there and understood the fire process) who would be interviewed have the knowledge to answer the questionnaires. The second questionnaire elicits information regarding the fire department’s record, including the effects of fire fighting and life rescue during the fires. As aforementioned, the task of completing these two questionnaires to collect meaningful data does not seem easy. The authors are very grateful to firemen and the fire-hit families who kindly and bravely provided the time and help even though they have encountered the unwanted tragic event. The present study concerns 918 fire cases in residential buildings from 1998 to 2002.
During fire
Occupant Attributes
1047
Post-fire (the resulting fire loss)
Attributes of Time, Space and People
♦ Knowledge of ♦ Time Attributes
Fire Prevention ♦ Fire Management
♦ Spatial Attributes ♦ People Attributes
Attributes of Building Fire Safety
Fire Development and Egress ♦ Degree of Fire Severity
♦ Structures of Building and
♦ Degree of Difficulty of Fire Escape
Partition ♦ Situations of Escape
Fire Losses (Monetary Losses of Property and Building Damage)
Routes ♦ Accessibility and Conditions of Firefighting ♦ Fire Protection Systems
Fire Brigade Intervention ♦ Response Time ♦ Control Time ♦ Extinguishing Time ♦ Dispatched Fire Services
Fig. 1. Hypothetical framework of explanatory fire-loss model.
ARTICLE IN PRESS 1048
Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
3.2. Contents of questionnaires (research tools) The contents of questionnaires consist of independent and dependent variables. Independent (explanatory) variables, which include pre-fire and during-fire variables (or intervening variables), may be either a variable or a vector of variables. Table 1 shows the numbers and descriptions of items of independent variables (factors). The pre-fire independent variables include the attributes of occupants and building fire safety. Independent variables regarding during-fire contain time and spatial attributes of fire occurrence, fire development and egress, and fire brigade interventions. As mentioned above, fire loss in this study is defined as the direct monetary losses due to property and building damage, estimated by fire brigades and measured in $NT dollars. The frequency of fire loss in the current study is shown in Table 2. In order to grasp all the ideas of the explanatory factors as much as possible, not only the respondents of the questionnaires need to have the knowledge to be able to answer the questions as mentioned above but also the data measurement needs to be constructed with validity and reliability. Factor analysis based on orthogonal rotation methods with VARIMAX approach [17] is then used to develop factors (concepts, scales and sub-concepts). Several stages of analyzing processes (nine group meetings, pretest, validity and reliability analysis) are performed to justify why the questionnaires are pertinent and comprehensive and clear and precise.
3.3. Measurement of concept For the first concept, attributes of the occupants, eleven individual behaviours representing the attributes generally comTable 1 Items and contents of concepts (independent variables). Concepts Knowledge of fire prevention
Items and contents
6 items: Able to use fire extinguishers, take fire drill, alert to fire alarm, familiar to fire alarm, take firerelated courses and recognize the fire exit sign. Fire management 5 items: Close gas control valve, check electrical outlet, inspect gas valve, remove unused plug and turn off unused electrical sources. Structures of building and 2 items: Structure types of building and material of partition partition wall. Categorical data include concrete, iron, brick and wood members. Situations of escape routes 3 items: Design of escape route, clearance of escape route and stairs. Accessibility and conditions 5 items: Accessibility of fire engines, sufficiency of of fire-fighting fire-fighting water, easy to fire fighting from outside building, narrow situation of street or lane around the building and parking situation around the building. Fire protection systems 8 items: Situations of emergency lighting, exit sign, fire extinguishers, refuge appliances, broadcast facility, alarm systems, automatic fire suppression systems and efficiency of fire protection systems. Attributes of time, space 3 items: 1 item for fire occurrence time; 4 items for and people space including ignition location, ignition floor, situation of iron-barred window and numbers of exits;4 items for people including numbers of young children under 12, elderly people above 65, disabled people and continuous medicine-takers. Degree of fire severity 3 items: Situation of furnish burning, severe degree of first item ignited, amount of first item ignited. Degree of difficulty of fire 6 items: Difficult to escape, smoke spread, narrow escape stair, long travel distance, deficiency of fire exits and entrance being blocked due to smoke. Fire brigade interventions 4 items: Attendance time, control time, extinguishment time of fire and dispatched firefighting force.
Table 2 Frequency of fire losses ( 10,000) (in NT$). Fire losses
%
Below 1 1–10 (not included) 10–50 (not included) 50–100 (not included) 100–500 (not included) Above 500
26.2 36.6 23.8 8.8 3.9 0.7
mitted by occupants in Taiwan are included in the scale. Among the items measured are responses to questions about knowledge of fire prevention (6 items) and fire management (5 items). Respondents were asked whether they had the feelings or experiences as expressed in the 11 items in the past. Each item is scored on a four-point scale: (1) often, (2) sometimes, (3) rarely and (4) never. Then, all scores were re-coded (i.e., 4 ¼ often; 3 ¼ sometimes; 2 ¼ rarely and 1 ¼ never) necessary for the regression analysis. High scores on this scale will demonstrate frequent engagement in fire prevention acts. A composite score is computed by summing the fire-related prevention items. Hence, the latent concepts of occupant attributes are indexed by two independent empirical factors (sub-concepts) in the model. Similar approaches are applied to obtain the scores from other sub-concepts in this study. However, categorical types of sub-concept such as fire occurrence time, building structure, partition structure and the like are measured as dummy variables. The concept of attributes of building fire safety consists of four sub-concepts, including structure of the building and the partitions, location of escape routes, accessibility and conditions of fire fighting and fire protection systems. In the current study, 18-item symptoms are examined. Respondents were asked whether they had any feelings or experiences as expressed in the 18 items in the past. The higher the score, the more frequently a respondent feels the symptom and then a higher score demonstrates a much higher fire safety awareness. Attributes of time, space and people describe the time of fire occurrence and ignition locations, situations of iron-barred windows and numbers of exits (based on official records) and the number of young children under twelve, elderly people above 65, disabled people and continuous medicine-takers in the building during fires. Even though these attributes of time, space and people are categorized together, they are not formed as sub-concepts through factor analysis and they are analyzed separately in this research as well as other studies on life-risk analysis [1,18]. The concept entitled fire development and egress includes two sub-concepts, namely degree of fire severity (3 items) and degree of difficulty of fire escape (6 items). Three questions regarding the destructive effects of fires to the building are designed to measure the degree of fire severity. The degree of difficulty of escape in fire is measured not only by the adequacy and suitability of egress systems but also by the egress accessibility due to smoke effects. The sum of items then represents the individual sub-concept. The higher the score, the more severe a fire is in the degree of fire severity. The higher the score, the more difficulty a respondent or family member experienced in escaping during fires in measuring the degree of difficulty of escape from the fire. The fire fighting, response (attendance) time, control time (time difference between fire incident call received and fire under control by fire brigade), as well as extinguishing time measured in minutes and dispatched fire-fighting forces measured in persons as well as numbers of fire engines attending the fire fighting are assessed
ARTICLE IN PRESS Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
Table 3 Factor analysis and reliability analysis of knowledge of fire prevention. Variable
Factor loading
1. Be able to use fire extinguisher pre-fire 2. Take fire drill pre-fire 3. Be alert to fire alarm pre-fire 4. Be familiar to fire alarm pre-fire 5. Take fire-related courses pre-fire 6. Understand the signs of fire exit
0.649 0.710 0.815 0.826 0.686 0.716
Eigenvalue Cronbach a
3.518 0.814
Table 4 Summaries of factor analysis and reliability analysis for other sub-concepts. Factors (sub-concepts)
Knowledge of fire prevention Fire management Situations of escape routes Accessibility and conditions of fire fighting Fire protection systems Degree of fire severity Degree of difficulty of fire escape
Factor loading; eigenvalue
a
Cronbach
0.649–0.826; 3.254 0.641–0.826; 3.013 0.692–0.877; 2.002 0.602–0.833; 2.503
0.830 0.832 0.889 0.736
0.690–0.872; 5.257 0.729–0.838; 1.879 0.652–0.760; 2.882
0.923 0.687 0.780
from the official fire reports. Fire losses including monetary losses of property and building damage are also obtained from official records. Table 3 depicts the development of the sub-concept, namely knowledge of fire prevention. A reliable (a ¼ 0.830) six-item scale that assessed a variety of variables regarding fire prevention knowledge is shown in Table 3. Using the same technique (orthogonal rotation methods with VARIMAX) to develop the other sub-concepts, summary of analysis results for other subconcepts including the sub-concept entitled knowledge of fire prevention but excluding some sub-concepts due to categorical type or actual numbers is given in Table 4. A commonly used threshold value for acceptable reliability is 0.70. Although this is not an absolute standard, values below 0.70 have been deemed acceptable if the research is exploratory in nature [19]. More reliable measures (Cronbach a) provide the researcher with greater confidence that the individual factors are all consistent in their measurements. The results given in Table 4 seem reasonably acceptable since this research is original and exploratory in nature. However, all the developments of these concepts or sub-concepts may be improved in the future.
4. Analysis and discussion
1049
degree of difficulty of escape from the fire, control time, extinguishing time and dispatched fire-fighting forces have positive correlations with fire loss. The higher the degree of fire severity, degree of difficulty of escape, fire control time, fire extinguishing time or dispatched fire-fighting forces for a fire, the greater will be the fire loss. However, among the significant explanatory variables, building structure and partition structure have negative correlations with fire loss. The negative correlations demonstrate that the lesser the fire resistance of building structure and partition structure, the greater will be the fire loss. Additionally, the correlation relationships between pre-fire variables and during-fire (intervening) variables are also obtained and shown in Table 5. All the statistically significant variables will be included while performing regression analysis in the next section. One might think that some of the corresponding correlation coefficients given in Table 5 are small (even though statistically significant). Possible reasons are the sample size, and that the measurement is ‘‘science-based’’ (i.e. social sciencebased) instead of ‘‘engineering-based’’ (i.e. fire load survey, fireresistance rating and opening factor) approach, etc. Therefore, further refinement of the investigation needs to be carried out.
4.2. Regression analysis Due to its duration in special fire cases, extinguishing time is less appropriate than control time, which is critical for measuring the fire-fighting operation and is not included in regression analysis. By similar reasoning, building structure is less appropriate than partition structure, i.e. a critical factor for measuring control time, which is not included in the regression analysis either. Based on five significant independent variables, the explanatory model for fire loss will be constructed by employing step-wise regression analysis. Taking the degree of fire severity, the degree of difficulty of escape, the control time, the dispatched fire-fighting forces, and the partition structure as the independent (explanatory) variables to predict the dependent variable, a regression model to explain fire loss is shown in Table 6. In addition, the regression results of taking some intervening variables, namely degree of fire severity, control time and dispatched fire-fighting forces as dependent variables, are also shown in Table 6. Including a large number of predictor variables in a regression model is never a good strategy, unless there are strong reasons to suggest that they should be included. As mentioned above, only the statistically significant correlated variables in Table 5 have been included in regression analysis. For the present data set, the best regression model for relating the independent variables to dependent variables is shown in Table 6 and can be written as the following regression equations. The definitions of variables are also shown in Table 6. Y ¼ 6:599 þ 4:945X2 þ 0:153X4 þ 0:175X5 X2 ¼ 1:531 þ 0:228X1 þ 0:285X3 0:200X6
4.1. Correlation analysis In order to examine the relationships between the independent variables and the dependent variable, a correlation analysis is performed. Fourteen possible influencing factors or variables shown in Fig. 1 or Table 1 are measured from the survey data. Seven out of fourteen variables listed in Table 5, namely degree of fire severity, degree of difficulty of fire escape, control time, extinguishing time, dispatched fire-fighting forces, building structure and partition structure, have statistically significant relationships on fire loss at the level of significance 0.05. Among the significant explanatory variables, degree of fire severity,
X4 ¼ 14:517 þ 1:989X2 0:864X7 X5 ¼ 33:842 þ 5:765X2 þ 0:620X4 1:263X8
In this study, independent variables such as degree of fire severity, control time and dispatched fire-fighting forces have significant effects on fire losses. From the estimated regression coefficients shown in Table 6, one can understand that among these significant variables, degree of fire severity has the most important effect due to the largest standardized regression coefficient in the model (b ¼ 0.299; po0.001). Fire losses can be reduced by lessening the degree of fire severity, control time and
ARTICLE IN PRESS 1050
Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
Table 5 Correlation coefficients between factors and fire losses. Factor
Fire losses
Degree of fire severity
Degree of difficulty of fire escape
Attendance time
Control time
Extinguishing time
Dispatched fire-fighting forces
Fire losses Degree of fire severity Degree of difficulty of fire escape Attendance time Control time Extinguishing time Dispatched fire-fighting forces Knowledge of fire prevention Fire management Situations of escape routes Accessibility and conditions of fire-fighting Fire protection systems Fire occurrence time Building structure Partition structure
1.000 0.398** 0.079* 0.036 0.282** 0.232** 0.367** 0.024 0.008 0.009 0.023
1.000 0.417** 0.032 0.194** 0.242** 0.342** 0.09 0.013 0.087* 0.072
1.000 0.040 0.078 0.064 0.199** 0.077* 0.028 0.187** 0.146**
1.000 0.003 0.016 0.062 0.012 0.052 0.063 0.023
1.000 0.593** 0.349** 0.050 0.005 0.083* 0.044
1.000 0.268** 0.052 0.025 0.070 0.010
1.000 0.054 0.007 0.065 0.167**
0.028 0.076* 0.026 0.027
0.049 0.065 0.008 0.064
0.056 0.100* 0.064 0.055
0.006 0.120** 0.115** 0.117**
0.021 0.127** 0.139** 0.136**
0.018 0.014 0.064 0.135** 0.166** 0.143** 0.159** 0.163**
**po0.01, *po0.05.
Table 6 Regression model for fire losses and some intervening variables. Fire losses (Y) Fire occurrence time (X1) Degree of fire severity (X2) Degree of difficulty of fire escape (X3) Control time (X4) Dispatched fire-fighting forces (X5) Partition structure (X6) Situations of escape routes (X7) Accessibility and conditions of fire fighting (X8) Constant R2
Degree of fire severity (X2)
Control time (X4)
Dispatched fire-fighting forces (X5)
0.228*(0.089) 4.945***(0.299)
1.989***(0.170)
5.765***(0.240)
0.285***(0.440) 0.153**(0.112) 0.175***(0.253)
0.620***(0.312) 0.200**(0.111) 0.864*(0.106)
6.559 0.246
1.531 0.220
14.517 0.043
1.263**(0.123) 33.842 0.203
Note: a. *po0.05, **po0.01, ***po0.001. b. Figures in parentheses are standardized regression coefficients.
dispatched fire-fighting forces. By improving the items of the subconcept entitled ‘‘degree of fire severity’’, that is by limiting the arrangement as well as the amount of furnishings and restricting the use of combustible material (see Table 1), the degree of fire severity in case of a fire can be greatly reduced. The length of fire control time and the number of dispatched fire-fighting forces can be reduced by more advanced techniques and effective fire-fighting training. Fire control time and dispatched fire-fighting forces only explain instead of predicting fire losses since they are usually recorded after the fire incident has occurred and fire losses have been incurred. In addition, it is worth noting that fire control time and dispatched fire-fighting forces are correlated with fire losses, but this does not imply causation, that longer control time or a higher number of fire-fighting forces will not cause a greater fire loss. On the contrary, it is possible that fires with larger losses usually take longer to control and normally require a larger number of fire-fighting units to attend. Even so, both the fire control time and the fire-fighting forces dispatched by fire brigades may provide meaningful information for practicing fire fighting. From Table 6 one can find that the degree of difficulty of escape from the fire influences (or explains) the degree of fire severity most, the degree of fire severity affects control time most and the control time influences the dispatched fire-fighting forces most due to the highest values of standardized regression coefficients in the model (b ¼ 0.440, po0.001; b ¼ 0.170, po0.001; b ¼ 0.312,
po0.001). From Table 6, one can also understand that fire occurrence at night time (22:00–08:00) and less fire resistance of partition structure have significant effects on the degree of fire severity; the poor situations of escape route significantly increase fire control time, and the poor fire-fighting conditions affect the fire-fighting forces significantly. In order to decrease the degree of fire severity, shorten the control time, and reduce the dispatched fire-fighting forces, the significant sub-concepts in Table 1 need to be improved. In this study, path analysis is performed to observe further the relationships between all significant independent variables (including intervening variables) and dependent variables. Fig. 2 shows the relationships and is proposed as the explanatory model for fire losses. From Fig. 2 one can understand that degree of fire severity, control time, and dispatched fire-fighting forces have direct effects on fire losses as mentioned above. Although partition structure, fire occurrence time and the degree of difficulty of escape have no significant or direct effects on fire losses, they have indirect effects on fire losses through the intervening variable (sub-concept) entitled degree of fire severity. The sub-concept entitled ‘‘accessibility and conditions of fire fighting’’ has no significant or direct effect on fire losses, but it has an indirect effect on fire losses via dispatched fire-fighting forces. The sub-concept entitled ‘‘situations of escape routes’’ does not have significant or direct effect on fire losses; however, it indirectly affects fire losses via fire control time. Degree of fire
ARTICLE IN PRESS Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
1051
Fire Occurrence Time (X1)
Partition Structure (X6)
-0.111
0.089 Degree of Fire Severity (X2)
Situations of Escape Routes (X7)
0.440
0.170
Degree of Difficulty of Fire Escape (X3)
Accessibility and Conditions of Firefighting (X8)
0.299 Fire Losses (Y) 0.112
-0.106
Control Time (X4)
0.312
0.240
0.253
-0.123 Dispatched Firefighting Forces (X5) Fig. 2. Proposed explanatory fire-loss model.
Table 7 Effects of independent variable on fire losses. Independent variable
Direct effect
Indirect effects
Total effect
Ranking order
Fire occurrence time (X1) Degree of fire severity (X2) Degree of difficulty of fire escape (X3) Control time (X4) Dispatched fire-fighting forces (X5) Partition structure (X6) Situations of escape routes (X7) Accessibility and conditions of fire fighting (X8)
– 0.299 – 0.112 0.253 – – –
0.034 0.093 0.176 0.078 – 0.043 0.020 0.031
0.034 0.392 0.176 0.190 0.253 0.043 0.020 0.031
6 1 4 3 2 5 8 7
severity not only has significant or direct effect on fire losses but also has an indirect effect on fire losses through fire control time and dispatched fire-fighting forces. Table 7 depicts the direct and indirect effects of all significant independent variables on fire losses. Total effects and ranking orders of all significant independent variables on fire losses are also shown in Table 7. Degree of fire severity has the strongest effect on fire losses, followed by dispatched fire-fighting forces and then followed by fire control time in spite of either direct or indirect situation. Again, improving the items of sub-concept or independent variables (including intervening variables) can reduce dependent variables. One might think that degree of fire severity, control time, dispatched fire-fighting forces, accessibility and conditions of fire fighting, partition structure and other affecting factors conducted by the analysis above are almost surely correlated or significant to fire loss and the results are no great surprise. However, this work is based on a social science approach instead of an engineering one and has examined for the first time multi-dimensional factors including pre-fire and during-fire activities. These have been systematically investigated during the research process and relative effects as well as ranking orders among these significant determinants are achieved in this study. If the engineering-based research can proceed to this stage then specific items can be improved.
5. Conclusion This research identifies some significant determinants and constructs an explanatory model for fire losses regarding the availability as well as use of fire incidence questionnaires and fire reports. From this study some results may be summarized as follows:
a. Propose a social science-based method for fire safety research and then understand the fundamental factors that contribute fire losses from empirical surveys in fires within buildings. b. Construct a valid and reliable model to explain fire losses by taking into account the influencing factors not only prefire (e.g. accessibility and conditions of fire fighting and partition structure) but also during a fire (e.g. degree of fire severity, fire control time, dispatched fire-fighting forces and degree of difficulty of escape from the fire) for residential buildings. c. Degree of fire severity has the greatest effect on the fire losses, followed by dispatched fire-fighting forces and then fire control time, degree of difficulty of escape, partition structure, fire occurrence time, accessibility and conditions of fire fighting, situations of escape routes, etc.
ARTICLE IN PRESS 1052
Y.-S. Lin et al. / Fire Safety Journal 44 (2009) 1046–1052
d. Provide useful and meaningful information for fire prevention, fire practice and fire-loss control. Fire losses can be greatly reduced if occupants, building managers and firefighters can take into consideration the statistically significant risk factors found in this study, while planning or practicing their programmes or activities. This research provides a very good starting point for developing an explanatory model in statistical fire-loss analysis even though some measurements of concepts (or items) regarding questionnaire need to be improved and refined. Undoubtedly, comprehensive fire investigation can lead to understanding the occupant and building attributes and improve pre-fire as well as during-fire activities that will enable better control over the fire growth, fire spread and the resulting fire losses. In order to establish a reliable model, more scientific data on the influencing factors (e.g. fire load and duration of fire resistance of the room of origin) need to be collected in the future. We can expect a gradual advancement in statistical modeling for fire losses as more background information is assembled and organized. Examinations of various relationships between the affecting factors and fire losses by introducing other multivariate analysis such as canonical correlation analysis and LISREL (Linear Structure Relation) will be the focus of future research.
Acknowledgements The authors are very thankful to the firemen and the fireaffected families who kindly and bravely provided the time and help to fill out the questionnaires. This work was funded in part by grant NSC89-2415-H-015-001-SSS, NSC90-2415-H-015-001-SSS and 96-2415-H-015-002-MY2 from the National Science Council, Taiwan, ROC.
References [1] Lin Yuan-Shang, Investigation and Analysis of Fire Losses, first ed., Central Police University Publishing Inc., Tauyuan, Taiwan, 2004 p2. [2] Please visit http://www.nfa.gov.tw/main9.html. [3] L. Yuan-Shang, L. Ho-Shu, Statistical models for fire risk assessment, Journal of Central Police University, Tauyuan, Taiwan 36 (2000) 415–434. [4] Federal Emergency Management Agency, Profile of the urban fire problem in the United States, Technical Report Series, 1999. [5] Federal Emergency Management Agency, Socioeconomic Factors and the Incidence of Fire, Federal United States Fire Administration, National Fire Data Center, Washington, DC, 1997. [6] J. Hausner, W. Walker, A. Swersey, An analysis of the deployment of firefighting resources in Yonkers, R-1566/2-HUD/CY, The New York City Rand Institute, New York, 1974. [7] G. Ramachandran, The Economics of Fire Protection, E&FN SPON, An Imprint of Routledge, London and NY, 1998 p. 67. [8] G. Ramachandran, Statistical methods in risk evaluation, Fire Safety Journal 2 (1979/1980) 125–145. [9] W. Belles Donald, Interior finish, in: Fire Protection Handbook, 18th ed., NFPA, 1997, p. 80 (Section 7). [10] Stollard Paul, Abrahams John, Fire form First Principles, E&FN SPON, 1999, pp. 35, 57, 58. [11] L. Yuan-Shang, L. Ho-Shu, Effects of building firesafety characteristics, egress difficulty and other attributes on life risk, Journal of Central Police University, Tauyuan, Taiwan 37 (2000) 445–476. [12] H.E. Corman, J. Ignall, K.L. Rider, A. Srevenson, Fire casualties and their relation to fire company distance and demographic factor, Fire Technology 12 (3) (1976) 193–203. [13] E.K. Ignall, L. Rider, R. Urbach, Fire Severity and Response Initial Findings, R2013, Rand Institute, New York, 1978, pp. 40–43. [14] Stefan Sardqvist, Goran Holmstedt, Correlation between firefighting operation and fire area: analysis of statistics, Fire Technology 36 (2000) 109–121. [15] Lin Yuan-Shang, Huang Chien-Hua, Effects of fire fighting on fire losses, Journal of Police Science, Tauyuan, Taiwan 33 (2) (2002) 189–204. [16] D.J. Rasbash, G. Ramachandran, B. Kandola, J.M. Watts Jr., M. Law, Evaluation of Fire Safety, John Wiley & Sons, England, 2004. [17] Wang Pao-Ging, SPSS for windows and research on behavior science, Hsin-Li Publishing Ltd., Taipei, Taiwan, 1999 p. 701. [18] Lin Yuan-Shang, Life risk analysis in residential building fires, Journal of Fire Sciences 22 (Nov. 2004) 491–504. [19] F.H. Joseph Jr., R.E. Adnderson, R.L. Tatham, W.C. Black, Structural equation modeling, multivariate data analysis with readings, fourth ed., Prentice-Hall Inc., New Jersey, USA, 1995 p. 641.