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«FICHA TÉCNICA Título Segurança e Higiene Ocupacionais - SHO 2012 - Livro de Resumos Autores/Editores Arezes, P., Baptista, J.S., Barroso, M.P., ...»

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From all pointed limitations of ORA methods, it seems that the use of Fuzzy Set Theory (FST) (Zadeh, 1965) may help produce more realistic representations and solutions, as shown by different authors (Herrera and Viedma, 2000; Nunes, 2003; Mure et al., 2006; Liu et al., 2004). FST presents a natural way of modelling the intrinsic vagueness and imprecision of everyday concepts by providing a very precise approach for dealing with uncertainty which grows out of the complexity of human behaviour. It also allows the inclusion of human creativity and intuition, which is an essential ingredient for successful ORA (Ru and Eloff, 1996). This statement lies in the recognition that while the role of facts is to provide an answer to expected value (because facts are the same for everyone), the subjective process will produce as many answers as there are people involved. In fact, even when the probabilities are know (in occupational safety it is made always by oversimplifications), analysts should take into account utility (Bernstein, 1996) aspects, such as, (in occupational safety context) risk perception, usefulness or workers satisfaction, which are fuzzy concepts. ORA analysts should try to elicit these aspects because answer to it can be important in aspects like acceptance of ORA results or workers motivation.

FST can address, more easily, the perceptions of all stakeholder groups, facilitating constructive discussion and elucidating the points of ORA results disagreement.

However, FST and probability theory are not contending or incompatible mathematical frameworks. They are different ways of expressing uncertainty.

Probability theory measures how likely one proposition is to be correct and FST measures the degree of correctness to which the proposition is correct.

Zadeh (1978) argues that fuzzy logic is different in character from probability, and is not a replacement for it. He fuzzified probability to fuzzy probability and also generalized it to what is called possibility theory.

A large number of different ORA traditional methods, qualitative and quantitative, are detailed in relevant literature (Ringdahl, 2001; Hollnagel, 2007). Fuzzy methods are presented and defended by other authors such as Nunes, 2003; Liu et al., 2004; Mure et al., 2006, Markowski et al., 2009.

We developed QRAM, a Qualitative Occupational Risk Assessment, using FST and with a model usefulness in mind, that is, simple enough to highlight the relevant characteristics of the real system.

Occupational Safety and HygieneInternational Symposium on


Qualitative Occupational Risk Assessment model (QRAM) is defined by four dimensions: Safety Climate ( SC ), Severity Factors ( S ) Possibility Factors ( OAP ) and Safety Barriers ( B ), used to estimate the risk of the following accident modes: a) Falls, b) Contact with electricity, c) Struck by moving vehicle (including heavy equipment), d) Injured by falling/dropped/collapsing object/person/wall/vehicle/crane which falls under gravity (including building or structure collapse and slipping hand held tool), e) Cave-ins (while or after excavation), f) Hit by rolling/sliding object or person (including stuck against object or equipment and caught in or compressed by equipment or objects), g) Contact with machinery moving parts (including injured by hand held tools operated by oneself), h)Lost buoyancy in water and,

i) Fire and Explosion.

To define the four dimensions we follow some rational on the risk concept, namely: 1) Aven and Renn (2009), risk is the uncertainty about the severity of the consequences of an activity with respect to something that humans value and, 2) EU (2000), the probability and severity of an adverse effect/event occurring to man or the environment following exposure, under defined conditions, to a risk source(s).

So, the risk expresses a combination of (Christensen et al, 2003):

Probability (possibility when using FST) of an unwanted outcome (a work accident, in a safety context);


Extent of consequence/effect under given specific circumstances (i. E. existence of safety barriers).

Some explanations for the high accident rates in construction have included organizational factors (Landeweer et al.,

1990) such as: 1) management style, 2) companies safety policy, 3) personal characteristics (like age and experience), 4) knowledge, 5) risk perception, and 6) motivation. The Health and Safety Executive (HSE, 2003) found that human behaviour is the factor that contributes about 80% to work accidents in the construction industry. This allows us to conclude that the causes of most work accidents at construction sites are related to safety factors, so we have included this dimension in our model.

Each dimension includes a number of parameters to characterize the dimension. Dimensions are aggregate by the fuzzyor operator to rank the accident mode risk (severity dimension development can be found in DOI:


There may be various levels of detail and analysis will depend on what want to do with the results and operational

factors, including:

Type and relevance of available data;

Degree of accuracy required to the obtained results;

Available resources.

The formal model for QRAM is for each accident mode depicted in Table 1.

–  –  –

Data collection is performed by observation of reality, interviews with workers, foreman and engineers and consultation of site documents (working procedures, reports of work accident investigation....). The collected data shall be transformed using the defined fuzzy sets and (in each dimension) will be aggregated with specialized fuzzy operators, to obtain a ranking of risks in the construction site.

The four dimensions results will be aggregated by an average operator to rank the work accidents risk.

Calculation of an absolute value to rank risks is unnecessary and can distract safety practitioners from the main aim of ORA process: to decide about occupational risk acceptability and recommend adequate measures for risk control and management.

So, ORA methods should have a tool/metric by which the results of a risk analysis can be translated into recommendations on the risks tolerability and respective improve actions.

QRAM uses the ALARP (as low a level as reasonably practicable) framework as a guide to ranking risks and achieving a satisfactory outcome for the practical management of risks. The ALARP approach requires site safety managers to demonstrate that: (1) the site is fit for its intended purposes, (2) the risks associated with its functioning are sufficiently low and (3) sufficient safety and emergency measures have been instituted (or are proposed).

QRAM results presented risks by 3 regions: acceptable (below 0,3), ALARP (between 0,3 and 0,7, including this values) and unacceptable (above 0,7). These regions are determined by analysis of the QRAM features and expert opinions.

QRAM is a model designed to analyze “real” sites so it need real data obtained “in situ” such as heights, weights (of tools and materials), supervision adequacy, work procedures adequacy, workers attitudes towards safety, management attitudes towards safety, safety work environment or safety barriers effectiveness.

Information sources include observation of reality (attitudes, behaviour, communication flows, traffic flows, housekeeping), interviews with workers, foreman and engineers, consultation of site documents (safety plans, working procedures, training records, reports of work accident investigation, safety meeting minutes, safety inspections reports, safety audit reports, instruction manuals of machinery and equipment) and physical measures (heights, weights…).

3. CONCLUSIONS Due to the lack, inadequate and imprecise data, the use of probabilistic ORA models at constructions sites requires analysts to make harsh estimates based on their experience and perceptions. This is reflected in the analyst-to-analyst variability of results. Significant analyst-to-analyst variability might well be perceived by engineers, foreman, workers and managers to indicate an inexact and immature analysis team not capable to providing accurate predictions of risk.

Any decisions based on misleading results may lead to non-effective safety actions.

Construction needs ORA models that can allow the use real data and where information should all be elicitated and treated in an easily understandable manner to assure that safety practitioners understand the results entirely.

FST proved to be a good framework for ORA especially because it allows the use of ill-defined data and the use of empirical knowledge to deal with fuzzy (real) concepts.

In risky industries, like construction, ORA results should be subject of mandatory quality assurance by critical review (of procedures, practices, data, etc…), conducted by labour safety inspectors.

4. ACKNOWLEDGMENTS This work was funded by the Portuguese Foundation for Science and Technology, Scholarship No. SFRH/BD/39610/ 2007.

5. REFERENCES Aven, T. and Renn, O. 2009. On risk defined as an event where the outcome is uncertain. Journal of Risk Research, 47 1–11 Bernstein, P. L. 1996. Against the gods: the remarkable story of risk. Jonh Wiley & Sons. New York.

Christensen, F. M., Andersen. O., Duijm, N. J. & Harremoës, P. (2003). Risk terminology-a platform for common understanding and better communication. Journal of Hazardous Materials, A103, 181–203.

Faber, M.H., Stewart, M.G., 2003. Risk assessment for civil engineering facilities: critical overview and discussion. Reliability Engineering and System Safety 80, 173–184.

Herrera, F., Viedma, E.H., 2000. Linguistic decision analysis: steps for solving decision Hollnagel, E. 2008. Risk + barriers = safety? Safety Science, 46, 221–229.

Hollnagel, E., 2007. Barriers and Accident Prevention. Ashgate Publishing, Hampshire.

HSE -Health and Safety Executive. Causal factors in construction accidents. Research Report 156. HSE, London.

Landeweer, J.A., Urlings, I.J.M., Dejong, A.H.J., Nijhuis, F.J.N. and Bouter, L.M., 1990. Risk taking tendency among construction workers. Journal of Occupational Accidents, 11, 3, 183-196.

Kentel, E., Aral, M.M., 2004. Probabilistic-fuzzy health risk modeling. Stochastic Environmental Research and Risk Assessment 18, 324–338.

Liu, J., Yang, J., Wang, J., Sii, H., Wang, Y., 2004. Fuzzy rule-based evidential reasoning approach for safety analysis.

International Journal of General Systems 33 (2–3), 183–204.

Markowski, A., Mannan, Bigoszewska, A., 2009. Fuzzy logic for process safety analysis. Journal of Loss Prevention in the Process Industries 22, 695–702.

Mure, S., Demichela, M., Piccinini, N., 2006. Assessment of the risk of occupational accidents using a fuzzy approach.

Cognition, Technology & Work 8, 103–112.

Nunes, I.L., 2003. Modelo De Sistema Pericial Difuso Para Apoio À Análise Ergonómica De Postos De Trabalho. [Fuzzy Expert System Model to Support Workstation Ergonomic Analysis]. PhD Dissertation, Universidade Nova de Lisboa/Faculdade de Ciências e Tecnologia, Dep Eng Mecânica e Industrial.

Pinto, Abel, Nunes I. L. and Ribeiro, R. A, 2011. Occupational risk assessment in construction industry – Overview and reflection. Safety Science, 49, 616–624.

Ringdahl, L.H., 2001. Safety Analysis Principles and Practice in Occupational Safety, second ed. Taylor & Francis, London.

Ru, W.G., Eloff, J.H.P., 1996. Risk analysis modelling with the use of fuzzy logic. Computers and Security 15 (3), 239–248.

Zadeh, L. A., 1978. Fuzzy Sets as the Basis for a Theory of Possibility. Fuzzy Sets and Systems 1, 3-28.

Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8, 338–353.

Occupational Safety and HygieneInternational Symposium on

Análise de Planos de Segurança e Saúde Analysis of Health and Safety plans Pinto, Dianaa, Reis, Cristinab a Universidade de Trás-os-Montes e Alto Douro, Vila Real, email: dianasrpinto@gmail.com ; b Universidade de Trás-osMontes e Alto Douro, Vila Real, e-mail: crisreis@utad.pt

1. INTRODUÇÃO Devido ao número preocupante de acidentes que ocorre na área da construção, e assentando nos princípios gerais de prevenção, no contexto das Directivas Europeias, surge o Decreto-lei n.º 155/95, revogado pelo Decreto-lei n.º 273/2003, de 29 de Outubro de 2003, que prevê uma linha de responsabilidades entre todos os intervenientes, “clarificando as funções do coordenador de segurança em projecto e em obra”, bem como as “obrigações de cada interveniente no acto de construção” (Gonelha e Saldanha 2006) e a elaboração de um plano de segurança e saúde, em obras sujeitas a projecto e que impliquem riscos especiais, ou a comunicação prévia (Decreto-lei 273/2003), passando “a considerar o empreendimento construtivo na sua globalidade, desde a concepção à sua execução e posterior utilização, intervindo vários sujeitos neste processo, desde o autor de projecto até às diversas cadeias de subcontratação”. (Gonelha e Saldanha 2006) O objectivo principal da elaboração do Plano de Segurança e Saúde é estabelecer linhas orientadoras para a gestão da segurança e saúde no trabalho. (Decreto-lei 273/2003). Este documento deve identificar e avaliar os riscos em matéria de segurança e saúde, nas diversas fases do empreendimento, e preconizar as respectivas medidas preventivas a adoptar.

Deve ser iniciado na fase de projecto e deve ser complementado para a fase de execução de obra e adaptado, caso necessário, durante a mesma., A avaliação de riscos com vista à prevenção deve, portanto, iniciar-se com o inicio do projecto, contemplando, desde esta fase, os trabalhos a executar, os riscos associados e respectivas medidas preventivas. Desta forma, é atribuída responsabilidade ao projectista que deverá ter em conta, aquando da execução do projecto, os princípios gerias de prevenção, dado que as suas opções condicionam a diversidade dos riscos (DL 273/2003).

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