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QRAM a Qualitative Occupational Safety Risk Assessment Model for the construction industry that incorporate uncertainties by the use of fuzzy sets Abel Pinto ⇑ Safe@Work, Independent Safety Research Group, Avenida do Mar, no. 30, 5 esq, 2825-476 Costa da Caparica, Portugal a r t i c l e i n f o Article history: Received 16 August 2013 Received in revised form 19 October 2013 Accepted 29 October 2013 Available online 27 November 2013 Keywords: Qualitative risk assessment Construction industry Occupational safety Fuzzy sets Qualitative model a b s t r a c t Occupational safety risk assessment is the core of safety practices. Is a complex process that requires the consideration of sundry parameters, which are often difficult to quantify. This paper presents the new developed fuzzy QRAM model, which intends to support construction companies in carrying out their responsibilities to reduce occupational safety risks. The innovative aspects of QRAM model is to embody assess of the safety climate and the safety barriers effectiveness as assessment dimensions and the use of fuzzy sets theory to enhance the use of imprecise and incomplete information, elicited by linguistic variables. The QRAM model was designed based on the best academic and empirical knowledge about safety risks on construction industry, biomechanical data and laws of physics, chemistry and engineering. The lack of credible and accurate data, resulting from the in-depth investigation of work accidents in construction industry was the greatest difficulty in carry-out this work. QRAM was, firstly evaluated by ‘‘peer’’ review, with 12 safety experts from Brazil (2), Bulgaria (1), Greece (3), Turkey (3) and Portugal (3) and, then, appraised by comparing QRAM with 2 other occupational safety risk assessment techniques. The safety experts evaluators concluded that: (a) QRAM is a versatile tool to assess occupational safety risk assessment on construction sites; ( the specific checklists for knowledge elicitation are a good aid and enhance the process objectivity, and © the use of linguistic variables is a better way to rate the risk factors thus to render the risk assessment process more objective and reliable. 1-s2.0-S092575351300252X-main.pdf

Occupational risk assessment in construction industry – Overview and reflection Abel Pinto a,⇑, Isabel L. Nunes a, Rita A. Ribeiro b a Universidade Nova Lisboa, Faculdade de Ciências e Tecnologia, Departamento de Engenharia Mecânica e Industrial, Campus de Caparica, 2829-516 Caparica, Portugal b Centro de Tecnologia e Sistemas, UNINOVA, Campus de Caparica, 2829-516 Caparica, Portugal a r t i c l e i n f o Article history: Received 6 October 2009 Received in revised form 23 November 2010 Accepted 8 January 2011 Available online 18 February 2011 Keywords: Occupational risk assessment Safety Construction industry Fuzzy sets a b s t r a c t The construction industry is plagued by occupational risky situations and poor working conditions. Occupational risk assessment (ORA) on workplace sites is the first and key step to achieve adequate safety levels, particularly to support decision-making in safety programs. Most construction safety efforts are applied informally under the premise that simply allocating more resources to safety management will improve safety on site. Moreover, there are many traditional methods to address ORA, but few have been adapted and validated for use in the construction industry, particularly in the design stage, for which traditional approaches do not give adequate answers. This paper presents a state-of-the-art on ORA traditional methods, for the construction industry, discussing their limitations and pointing advantages of using fuzzy sets approaches to deal with ill-defined situations. 1-s2.0-S0925753511000051-main.pdf

An occupational safety risk analysis method at construction sites using fuzzy sets G. Emre Gu¨ rcanli*, Ugur Mu¨ ngen Technical University of Istanbul, Civil Engineering Faculty, Construction Management Department, 34469 Maslak/Istanbul, Turkey a r t i c l e i n f o Article history: Received 18 February 2008 Received in revised form 26 September 2008 Accepted 15 October 2008 Available online 30 November 2008 Keywords: Construction safety Fuzzy sets Accident likelihood Accident severity Risk assessment a b s t r a c t The nature of construction work ensures that uncertainties are inherent in every condition; and on-site inspections generally use linguistic expressions rather than metrics to assess the risks of workers at a construction site. Additionally legal records, statistical data and documentation produced by companies are generally insufficient for determining risk. This fact increases the uncertainty of the job site atmosphere. This paper proposes a method for assessment of the risks that workers expose to at construction sites using a fuzzy rule-based safety analysis to deal with uncertain and insufficient data. Using this approach, historical accident data, subjective judgements of experts and the current safety level of a construction site can be combined. In the scope of this study, first 5239 occupational accidents in the construction industry are identified from 40,000 unclassified occupational accidents in all industries. Next, these 5239 construction accidents are investigated and classified in detail. Combining these data and the subjective judgement of safety experts, we derive three parameters namely the accident likelihood, current safety level and accident severity and they are utilized as input parameters for the fuzzy rule-based system. The method is then implemented on a tunnelling construction site and risk level for all type of accidents is derived. Relevance to the industry: The relevance of this study to industry is linked to the possibility of providing, through the use of proposed methodology, safety level scores for the construction sites that could result in work improvement and productivity. The application of the proposed method can reveal which safety items and factors are most important in improving workers safety, and therefore decide where to concentrate resources in order to improve the safety of the work environment. 1-s2.0-S0169814108001583-main.pdf