Use of Statistics for Qualitative Analysis of Fire Engineering Methods

Transcription

1 Use of Statistics for Qualitative Analysis of Fire Engineering Methods Jiří POKORNÝ, Vladimír VLČEK Fire Rescue Brigade of Moravian Silesian Region, Výškovická 40, Ostrava, Czech Republic Abstract: The article describes a part of the project, which has been solved during the security research in the Czech Republic and dealt with the issue of fire engineering. In the project were used relevant national and international statistical sources related to fires. Afterwards, the statistical data were selected and processed with an appropriate method into a form of so called qualitative analysis. The created charts dependencies make a part of certified methodology, which goal is to unify the fire engineering (expert) methods in the Czech Republic. 1. Introduction The project called Specific assessment of high-risk conditions for fire safety with the use of fire engineering methods (FIRESAFE), no. VG was solved in the framework of Security Research Programme in the Czech Republic from 2010 to The project took place from 2012 to The main goal of the project was to develop a methodology how to assess the risk of specific structures (eg. buildings higher than 60 meters, objects with high concentration of people) and technology. Partial goals of the project were to research the current level of fire engineering knowledge, research of related regulations, solutions and other tasks. 2. Qualitative analysis One part solved in the project is the area of so called qualitative analysis, which is a part of fire engineering evaluations (see Fig. 1). The purpose of the mentioned analysis is to define a selected number of fire scenarios that will be evaluated in a quantitative way. Fig. 1 The process of evaluation for fire engineering applications [1]

2 The probability of selected fire scenarios (eg. initiator, fire source, reaction of active fire appliances and their efficiency, probability of effective intervention with portable fire extinguishers) is evaluated when choosing the fire scenarios. The possible consequences (eg. deaths, injuries, property damage) are assessed as well. Prediction of probability is usually based on the use of relevant statistical data. There are usually very few relevant data typical for chosen events and their consequences today. 2. Analysis of Statistical Sources All possible foreign and national statistic sources were analysed during the project and useful statistical databases were identified (based on their range, nature and availability). During the project was also made a research of foreign statistical sources related to fires. The initial sources for searching statistical information were sources from organizations active in the field of fire safety, esp. International association of fire and rescue services (CTIF) [2], Federation of the European Union Fire Officer Associations (FEU) [3] a International Association for the Study of Insurance Economics [4]. Other statistical sources were data taken from countries similar to the Czech Republic, mainly from states in EU (eg. Germany, Italy, France, Austria, the Netherlands) and from chosen world powers (eg. USA and Russia). There were involved 31 countries. Summarized data were processed in the format Country Link Language Characteristics Note. [5], [6] The research of statistical sources was very complicated due to the fact that not all countries compile and publish enough detail statistic data or the sources were not properly prepared for a foreign visitor (eg. version available only in the native language of the country) which forced us to stop the work with foreign statistical sources. The biggest attention was paid to the national database identified as Statistical Monitoring of Events (SME) guided by General Directorate of Fire and Rescue Brigade of the Czech Republic in the Ministry of Interior. SME keep records of emergencies classified according to their characteristics into the following categories: fire, car accident, leak of hazardous chemical substance, technical accident, radiological accident, other emergencies, false alarm. [7] Only fire category is applicable in the project. There were identified approximately 50 figures and links suitable for the project (see Fig. 2). [5], [8]

3 They were mainly the following data: time of free growth of fire, time of localization, time of liquidation, direct damage, structural system, number of floors and height, evacuated and rescued people, injuries and deaths, mainly burning substances, intervention of citizens, function of electrical fire alarm system, automatic fire extinguishing system and equipment for smog and heat removal, start of fire according to day time. [5], [6], [8] The mentioned data were analyzed in connection to other items (eg. type of object, place of the origin). Fig. 2 Schematic diagram of relationships between the data monitored in Statistical Monitoring of Events and fire [5], [6] 3. Processing of Statistical Data The data about emergencies are recorded in Statistical Monitoring of Events (SME) since There occur some mistakes when inserting the data, which is mainly due to: too many figures (in hundreds of thousands), high number of people inserting the statistical figures (there are some variations in inserted figures which is influenced by their decision despite the fact that inserting the figures is coordinated, regulated by methodologies and members of fire brigade are carefully trained), errors in inserted figures, which is probably due to low attention and typing errors. [9]

4 The above described influences can cause so called outlying and extreme values, which can affect (distort) the evaluation of selected statistical files in a negative way. As s suitable mean value of compared figures was chosen median. Outliers and extreme deviations were eliminated by the use of quartiles and methods of internal walls. The mentioned methods are described for example by NEUBAUER and Coll. [10] Other commonly used statistical indicators showing the mean value of statistical file, eg. average, weighted average or modus proved to be less appropriate. Similarly, other methods for removing deviations, such as Grubbs and Dean-Dixon s Q test, were not used. Quartiles are modified quantiles and divide the statistical file into four equal parts. There are the following limitations: lower quartile called x 0,25 (25 % of the values are below this quartile, 75 % are above it), middle quartile called median x 0,50 (50 % of the values are below this quartile, 50 % are above it), upper quartile called x 0,75 (75 % of the values are below this quartile, 25 % are above it). [10] Quartile graph, so called boxplot, shows outliers and extreme values with the use of quartiles. The basis of the graph is rectangle, so called box. Its bottom edge is a lower quartile and its upper edge is an upper quartile. Inside the rectangle are 50% of all values of the statistical file. Quartile graph also shows two categories of values with significant deviations from others. Extreme values are the statistical units with more the 3 times higher difference between the upper and lower quartile (box lengths). Outliers are the statistical units with more the 1.5 times higher difference between the upper and lower quartile (box lengths). Principles of quartile method are used in a method of elimination of deviations according to internal walls. The internal walls are determined by quartiles for upper and lower internal wall. The upper internal wall can be determined by the following equation [10]: =, + 1,5,, (1) UIW is upper internal wall (-) The lower internal wall can be determined by the following equation [1010]: =, 1,5,, (2) LIW is lower internal wall (-) Values outside the range of internal walls are most probably outliers.

5 An example of the mean value calculation (median) of fire area of roofs and attics in Moravian Silesian Region between the years 2006 and 2011 is shown in Fig x 0,50 = Fire area (m 2 ) x 0,25 = 2 x 0,50 = 10 x 0,75 = 67 O = 164, Number of fires in attics and roofs. Legend: Red - median calculation in basic statistical file Blue - median calculation in a selected statistical file (excluding deviations) Fig. 3 Schematic diagram of the mean value for basic and selective statistical file [11] 4. Chart Dependency The chart dependency was created from processed figures in period from 2006 to The most important include: fires in regard to premise of origin, fires in regard to area of origin, fires in regard to time of origin, fires in regard to cause of origin, fire causes in regard to premise oforigin, fire initiators in regard to premise of origin, period of free fire development sorted by regions, fire area according to place of origin, spread of fire in a building when located in regard to premise, direct damage caused by fire in regard to place of origin, direct damage on 1 m 2 fire area in regard to premise of origin, danger to people in regard to area of origin, average time of fighting the fire. [12], [13]

6 An example of dependency for area of origin danger to people (f (area dan)) is shown in tab. 1. Area of Origin Tab. 1 Dependency for area and danger to people Number Number Number Number Number of of of of of Death Injured Rescued Evacuated Endangered People People People People People per Fire per Fire per Fire per Fire per Fire (10-3 ) (10-3 ) (10-3 ) (10-3 ) (10-3 ) Percentage of Fires with Endangered People (%) dining rooms, canteens, restaurants ,2 offices, receptions in buildings ,3 kitchen with pantry, etc ,1 An example of dependency for premise of origin damage caused by fire (f (prem dam)) is shown in tab. 2. Tab. 2 Dependency for type of premise and damage to fire area Type of Premise Average Damage Depending on the Area of Fire (Euro/m 2 ) Average Damage Depending on The Area of Fire/ Fires with Damages (Euro/m 2 ) cinemas theatres department stores An example of dependency for place of origin fire area (f (place area)) is shown in tab. 3. Tab. 3 Dependency for place of origin and fire area Place of Origin Average Fire Area (m 2 ) Maximum Fire Area (m 2 ) Percentage of Fire with an Area Larger than 1 m 2 (%) Production area incl. separate auxiliary working place Library and Archive Medical Service Summary The article deals with partial results of FIRESAFE project, which was solved in the frame of security research program in the Czech Republic. An attention was paid to national and international statistical sources related to fires and can be applied in the context of qualitative analysis of fire engineering assessment. The extent and form of foreign available statistical figures proved to be problematic from many reasons. The attention was further focused to national statistical source in the Czech Republic (database of Statistical Monitoring of Events by Fire Rescue Brigade of the Czech Republic). Collection of statistical dependencies (chart dependency), that are part of certified method for fire engineering processes, was made with the use of appropriate mathematical tools.

7 Acknowledgement This article arose with the support of the Ministry of Interior in Czech Republic no. VG called Specific assessment of high-risk conditions for fire safety with the use of fire engineering methods. Literature: [1] ISO/TR Fire safety engineering - Part 1: Application of fire performance concepts to design objectives. Geneva: International Organization for Standardization, [2] CTIF. In International association of fire and rescue service. [online] [cit ]. Dostupné z WWW: < [3] FEU. In Federation of the European Union Fire Officer Associations. [online] [cit ]. Dostupné z WWW: < [4] THE GENEVA ASSOCIATION. In International Association for the Study of Insurance Economics. [online] [cit ]. Dostupné z WWW: < [5] KUČERA, P., POKORNÝ, J. a kol. Projekt Specifické posouzení vysoce rizikových podmínek požární bezpečnosti svyužitím postupů požárního inženýrství. Kód projektu VG Roční zpráva k projektu za rok Ostrava: VŠB-TU Ostrava, FBI a ČAHD, 2012, 151 s. [6] KUČERA, P., POKORNÝ, J. a kol. Požární inženýrství v souvislostech I. Ostrava: Sdružení požárního a bezpečnostního inženýrství vydání, 152 s. ISBN [7] Software Krajské statistické sledování událostí, ver Kladno: RCS Kladno s.r.o., [8] POKORNÝ, Jiří - NANEK, Martin - PLISKA, Martin - ŠLACHTA, Zdeněk. Statistické údaje jako jeden ze zdrojů požárně inženýrských hodnocení. In Sborník přednášek XXII. ročníku mezinárodní konference Požární ochrana Ostrava: VŠB -TUO, FBI, SPBI ve spolupráci s ČAHD, s , ISBN: , ISSN: [9] POKORNÝ, Jiří - NANEK, Martin - PLISKA, Martin - ŠLACHTA, Zdeněk. Zpracování statistických údajů využitelných pro požárně inženýrské aplikace. In Sborník přednášek XXIII. ročníku mezinárodní konference Požární ochrana Ostrava: Sdružení požárního a bezpečnostního inženýrství, s ISBN , ISSN [10] NEUBAUER, J., SEDLAČÍK, M., KŘÍŽ, O.: Základy statistiky. 1. vydání. Praha: Grada Publishing, a.s., 2012, 240 s., ISBN [11] KUČERA, P., POKORNÝ, J. a kol. Specifické posouzení vysoce rizikových podmínek požární bezpečnosti s využitím postupů požárního inženýrství. Kód projektu VG Roční zpráva k projektu za rok Ostrava: Sdružení požárního a bezpečnostního inženýrství, 2013, 142 s. [12] KUČERA, P., POKORNÝ, J. a kol. Požární inženýrství v souvislostech II. Ostrava: Sdružení požárního a bezpečnostního inženýrství. 2014, s ISBN [13] KUČERA, P., POKORNÝ, J. a kol. Metodika pro specifické posouzení vysoce rizikových podmínek požární bezpečnosti s využitím postupů požárního inženýrství, Příloha č. 8.9 Statistická data o požárech. Výstup projektu Specifické posouzení vysoce rizikových podmínek požární bezpečnosti svyužitím postupů požárního inženýrství. Kód projektu VG Ostrava: 2014, 64 s.