خطر مرگ انسان در آتش سوزی ساختمان : ابزار تصمیم گیری با استفاده از شبکه های بیزی

The Netherlands is the most densely populated country of the European Union, which makes space very expensive. This leads to increasing complexity of the cities’ layout and other public spaces, together with a large number of people involved. Authorities would like to know whether new and innovative building designs ensure an appropriate level of safety of people in case of fire, before the accident happens, and to be prepared for the so-called “low probability–high consequences” accidents. Therefore, they need a tool to help them estimate the extent of a fire in a building, given any combination of possible conditions and any unexpected course of events during an emergency. This paper discusses the possibility of using Bayesian belief nets for this task. Using this approach, the people in charge can take decisions at different stages of the design process of a building regarding the location, the structure, the loading of the building, the types of fire protection systems inside the building, as well as the characteristics of the fire brigade that fights the possible fire. In the current study, usefulness of the approach is investigated using a small example. This will show the feasibility of the approach for the Netherlands situation and give authorities involved confidence that building a large comprehensive model would fulfil their needs for a support tool in the planning process. The effort to gather real data therefore was restricted as demonstration of fitness for purpose was the primary objective.

The complexity of our society is continuously increasing. Advanced technology allows the accommodation of a large population with increasing demands on goods and mobility in the very small space that the Netherlands provide. Therefore, the available space is used at maximum. As an example, preparations are made to build a roof over several kilometres of a 10 lane highway – that carries dangerous goods – and to build offices and may be even houses on top of it. But this intense use of space does not go without a price. There is an increased potential for an accident to become a large-scale disaster. For example, an explosion of a track carrying dangerous goods on the highway mentioned above may end in a large number of causalities among the people living or working in the buildings on top of the highway. Although such an accident remains a rare event, its consequences can reach a large extent. Therefore, the authorities would like to know the consequences of such an accident and to prepare for intervention, before the accident happens. The people in charge of taking decisions in the design phase of such a complex project need a tool that helps them to choose among the alternative designs that one which ensures with a certain probability the smallest damage. Given the fact that solutions for the large demand on space are innovative designs, the outcome of a possible accident and in particular a fire in such a building cannot be estimated based on past experience and statistical data. Moreover, prescriptive codes cannot be applied to these innovative designs. Therefore, new methods to test the level of safety of people inside buildings are needed. These methods should take into consideration all uncertain conditions in which a future possible fire could take place and, therefore, should be based on computer simulations. There is a large range of models that simulate the evacuation of a building, from simple models that simulate only the movement of people within the building, to very complex models that attempt to incorporate human behaviour [1] and [2]. They are used in order to decide on the structure of the building, the position of the exits, the size of the doors, corridors, and staircases. They can estimate the time needed to evacuate the whole building or only some parts of the building. They help also to find bottlenecks of a building regarding evacuation (where people may be trapped, where queues can be formed, etc.). However, they cannot consider the conditions outside buildings, for example, how neighbourhood of buildings, weather conditions, or intervention of fire fighting services can influence evacuation, and, implicitly, the outcome of a fire in terms of number of deaths. As an example, one of the tunnels in the High Speed Railway Line is designed for quick evacuation of passengers from the tunnel but further investigations, with the tunnel already finished, show that there is not enough space at ground level to accommodate the fleeing crowd, let alone vehicles and equipment of emergency services. This example shows the need to include in models not only people evacuation, but also fire development and rescue services’ actions, taking into account the characteristics of people and structure, location, and the external factors. For this particular case, if all these factors are included into the Bayesian belief net (BBN) model, one may set values for the available safe place and may obtain that the number of people at risk is high, or that the probability to have a high-consequence fire is high. The goal of the model presented in this paper is to put together not only people and their behaviour during evacuation, but also fire fighters’ actions, structure of the building, and characteristics of the building and the environment, in an overall model. Model results are more useful in a comparative sense rather than in an absolute sense. Using this tool, more alternatives can be compared with each other, with the actual level of safety, the desired level of safety, or with existing codes and procedures. The model could be used to analyze the “what-if” scenarios, as well as the low probability–high-consequence scenarios. The model proposed in this paper is based on the Bayesian belief net approach, a probabilistic method that can accommodate the complexity of the system under analysis. In Section 2 of the paper, the approach chosen to reach the goal of the work is presented. This section is a short summary of general characteristics, advantages and disadvantages of this method, comparisons with methods used before, and a short presentation of attempts to use the BBN approach in the field of fire safety. The model for percentage of deaths in a fire is presented in Section 3. Three phases of building up the BBN model for fire safety are presented here. First, the process of building the graphical structure of the network and of quantifying it is succinctly described. The last part of the section presents some example of analysis and results that can be obtained using this method in order to give an idea about applicability of the BBN approach for the estimation of probability distribution of percentage of deaths in case of fire. The last section of the paper presents the conclusions and gives directions about the future work to be done.

In current society with a rapid rate of development, with new and modern architecture that involves more and more people, it seems to be very clear that there is need for a comprehensive model that can be used to estimate the extent of a possible fire in a building. The model should take into consideration all the factors involved in a complex system such as fire in a building, and especially interactions between them. The use of BBNs in general and in particular in the field of fire safety is developing rapidly mainly due to its capability to represent complex systems with multi-dependencies between variables. This paper shows the feasibility of the BBN approach to produce results that fulfil the need of those in authority in taking decisions about the acceptability of fire risk in a building. The main characteristic of this model is that it can be used in the planning phase of a new structure or of a new area of a city, without having historical data of past accidents in such a building. The model can be used to compare the behaviour of alternative designs in fires, in normal conditions, or in some extreme, unforeseen conditions. The results produced show not only the average risk, but also provide information about the probability of having some extreme consequences of fire. In this way, the model can be used to decide if the proposed design satisfies criteria of maximum level of risk. The BBN approach for the problem of fire safety proposed in this paper can be extended in several directions, as follows: first, it can be used in a cost-effectiveness analysis, or in a multi-criteria analysis, combining the criterion of low damage with other criteria, like low costs for example. On the other hand, a more complex structure of the network can be obtained, by adding more variables, or extending the model to some other types of buildings, with some other specific problems. However, it is known that by adding more details, the model becomes more particular for a problem and less applicable to other problems. Therefore, equilibrium between details and general applicability of the model has to be maintained. There are also some problems with the applicability of the BBN approach. The most difficult problem with the complex networks is the validation of both the structure of the network (as so-called “qualitative” validation) and quantitative results. The problem consists of the lack of complete and objective data needed for validation. However, validation studies for parts of the model can be performed, as well as sensitivity analysis of the model [33].