# The LWF Blog

## Fire Engineering for Healthcare Premises – Risk Assessment – Part 30

April 12, 2021 12:18 pm

In LWF’s blog series for healthcare professionals, our aim is to give information on best practice of fire safety in hospitals and other healthcare premises. In part 29 of Fire Engineering for Healthcare Premises, LWF began to look at Risk Assessment. In part 30, we examine safety factors in Risk Assessment before discussing logic trees.

When building a fire safety strategy, it is common practice to include safety margins to ensure that the provided fire safety solution will suffice even if it has to stand against unknown variables. But how big a margin should be used? The appropriate level of safety margin can be ascertained by attention paid to various factors:

• The nature and type of fire safety solution. It may involve only a slight variation from Firecode, or it could be a highly individual and unorthodox fire safety strategy. Where the variation is only slight, only a small safety margin may be required. Where the variables are greater, the safety margin must reflect that.
• The method of analysis used.
• Input data for the analysis – reliability and source should be taken into account.
• Acceptance criteria.

A prescriptive fire safety margin is not appropriate. The multiple methods of analysis, input data and acceptance criteria do include an implicit safety margin, but the extent varies per method/data.

The appropriate nature of any safety margin used in fire safety engineering analysis must be ascertained on a case by case basis.

Where logic trees are used, it should be acknowledged that they cannot always examine all possible outcomes. A logic tree works best when the number of events and outcomes are strictly limited and this is often impossible in fire safety situations.

Uncertainties in the input parameters mean that the accuracy of absolute risk estimates is only about an order of magnitude.

A Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It is designed to calculate over and over, each time using a different set of random values from the probability functions. The Monte Carlo simulation provides a comprehensive view of what might happen, not only what could happen, but how likely the event is.

The simulation models input data in the form of probability distributions, such as those for fire load.

It includes complex interactions between the components of the model, which can make it impossible to predict the behaviour of the system. It’s also virtually impossible to validate the model for the whole system, only, for example, fire growth, smoke movement, evacuation etc.

Uncertainties in input parameters means that the accuracy of absolute risk estimates is only about an order of magnitude. Assessment using the quantification of relative risk is considered to be a more practical approach.