**4. Application example**

34 Fuzzy Inference System – Theory and Applications

given fuzzy risk level the linguistic term is selected from the fuzzy set with higher membership degree. For instance, a risk level of 0.5 has a membership degree of 0.2 to

The selected qualifier is used for building a sentence in natural language that presents the

For instance, a result from a risk assessment can be "The risk of electrical shock is very

The system can also offer explanations about the results presented. This is done using a backward chain inference process that identifies, ranks and presents the attributes that have high values (above a specified threshold) and that more significantly contributed to the

Where the detailed explanations are sorted in decreasing order of the respective attributes

The advice phase is performed after the conclusion of the risk assessment, and offers recommendations about safety measures adequate to control the risk for situations where the risk level is Medium or higher. The recommendations can be generic and specific. Generic recommendations refer to advice (i.e., legislation, guidelines, best practice) relating to a type of risk in general (e.g., risk of falls from height); while specific recommendations refer to advice that addresses a specific type of attribute that contributes to the risk (e.g.,

The selection of the specific recommendations is performed using a backward chaining inference process based on the risk assessment fuzzy rules. This process identifies and ranks the key attributes that contributed to the risk assessment result (i.e., the attributes with high

"Medium" and 0.8 to "High", consequently the qualifier to use will be "High".

computed level of risk. The explanations use the following generic format:

"*The risk of [descriptor of risk] is [qualifier] because*:

The [*attribute1*] is [*qualifier*] (fuzzy value) The [*attribute2*] is [*qualifier*] (fuzzy value)

The [*attributen*] is [*qualifier*] (fuzzy value)"

result to the user, using the generic format:

**3.3.4 Explanation process** 

high".

…

fuzzy value.

*…* 

**3.3.5 Advice phase** 

collective protection installed in site).

*Generic Recommendation1* 

*Generic Recommendationn"* 

The generic recommendations use the following format:

*"Regarding the risk of [descriptor of risk] consider the following advice* 

membership values), and provides recommendations in this order.

The specific recommendations use the following format:

*The risk of [descriptor of risk] is [qualifier]* 

In this section it will be demonstrated the use of the RA\_X fuzzy model in support of risk management. The example presented analyzes a construction work activity, which is pouring concrete into the forms of the structure of a building. Since the activity is performed on a platform located several meters in the air, the risk analysis presented regards the risk of "falls from height".


Applications of Fuzzy Logic in Risk Assessment – The RA\_X Case 37

offered is deemed as inadequate and the more relevant potentiating factors are the inadequate type of floor and safety training. The output of this assessment is done using a

As mentioned before the system can offer explanations about the results presented. The

Regarding advice the RA\_X can offer generic and specific recommendations that can be customized to the regional specificity of the users. Generic recommendations to the risk of falls from height include multimedia documents or internet links to, for instance, regulations, guidelines, best practices or software tools (e.g., European Directive 2001/45/EC (EU, 2001), European norms for protection against falls from heights (CEN, 2008), OSHA's Guidelines for the Prevention of Falls (OSHA, 1998), (OSHA, 2010c), OSHA Construction eTool (OSHA, 2010a), HSE's Interactive Guide (HSE, 2010)). Specific recommendations include the same type of references, but addressing the individual issues that emerged as contributing significantly to the risk. In the present example, the recommendations would address themes like improving personnel protection (e.g., (BSI, 2005)), collective protection (e.g. (NASC, 2008)), type of working floor (e.g. (OSHA, 2010b))

Fuzzy Logics has been used to handle uncertainty in human-centred systems (e.g., ergonomics, safety, occupational stress) analysis, as a way to deal with complex, imprecise,

This chapter presented the main features of the RA\_X FMADM model, which was developed to implement a fuzzy expert system for supporting risk management activities. In the current stage a prototype was implemented for test and validation purposes. The support of a proactive risk management is achieved by assessing potential factors that contribute for occupational accident occurrence and by guiding on the adoption of safety

The RA\_X is meant to be a flexible and easy to use system, which can process both objective and subjective input data and provide risk assessment and advice for a broad variety of occupational activities. The results are offered using natural language. The system also provides means to perform trend analysis supporting the follow-up and monitoring of risks

Following a quite simple Knowledge Engineering process, the Knowledge Base of the RA\_X expert system can be updated to incorporate new risks, broadening the scope of application, and can be customized to different national realities accommodating, for instance, to

explanation regarding this risk assessment would adopt the following format:

sentence in natural language, such as:

or safety training (e.g. (HSE, 2008)).

**5. Conclusion** 

measures.

in work situations.

uncertain and vague data.

"The risk of falls from height is High"

"The risk of falls from height is High because: The Height is very inadequate (1)

The Harness/Life line is inadequate (0.75) The Type of floor/Tidiness is inadequate (0.75) The Safety training is inadequate (0.75)"

As explained before, the risk assessment is based on attributes related with three categories of main factors (hazard, safety control factors and potentiating factors). Table 2 illustrates the main factors and examples of corresponding attributes for assessing the risk of "falls from height". For example, "Work at height" is the Hazard and "Height" is the attribute required for this analysis. The list of attributes is used during the data collection phase to ask for the relevant input data for the risk analysis. If the user doesn't provide data to some attribute the model considers that this attribute is in such a state that is not contributing to the risk.

Table 3 synthesizes the application of the RA\_X model. The collected input data is shown in column "Raw Data".


Table 3. RA\_X application example in the assessment of the risk "falls from height" for an activity of pouring concrete

In this case the Height was obtained by measurement and the other data are opinions. The fuzzification of the data was done using the membership function presented in Figure 4 for the Height, and the Linguistic Variable "inadequacy" (Figure 5) for the remaining subjective data (refer to subsection 3.2), and the results of the fuzzification process are presented in column "Fuzzy Attribute Value". The results of the partial fuzzy inference processes are shown in column "Aggregated Values" (refer to subsection 3.3). Finally, the fuzzy risk level and the corresponding crisp level, obtained by defuzzification (see Figure 6) are presented in column "Fuzzy Risk Level (Crisp Risk Level)" (refer to subsection 3.3.3).

In short, the risk assessment based on the RA\_X model is that there is a high risk of falls from height for an activity where the workers operate at a height of 4 m, the best protection offered is deemed as inadequate and the more relevant potentiating factors are the inadequate type of floor and safety training. The output of this assessment is done using a sentence in natural language, such as:

"The risk of falls from height is High"

As mentioned before the system can offer explanations about the results presented. The explanation regarding this risk assessment would adopt the following format:

"The risk of falls from height is High because:

The Height is very inadequate (1) The Harness/Life line is inadequate (0.75) The Type of floor/Tidiness is inadequate (0.75) The Safety training is inadequate (0.75)"

Regarding advice the RA\_X can offer generic and specific recommendations that can be customized to the regional specificity of the users. Generic recommendations to the risk of falls from height include multimedia documents or internet links to, for instance, regulations, guidelines, best practices or software tools (e.g., European Directive 2001/45/EC (EU, 2001), European norms for protection against falls from heights (CEN, 2008), OSHA's Guidelines for the Prevention of Falls (OSHA, 1998), (OSHA, 2010c), OSHA Construction eTool (OSHA, 2010a), HSE's Interactive Guide (HSE, 2010)). Specific recommendations include the same type of references, but addressing the individual issues that emerged as contributing significantly to the risk. In the present example, the recommendations would address themes like improving personnel protection (e.g., (BSI, 2005)), collective protection (e.g. (NASC, 2008)), type of working floor (e.g. (OSHA, 2010b)) or safety training (e.g. (HSE, 2008)).
