Article ID Journal Published Year Pages File Type
1096584 International Journal of Industrial Ergonomics 2009 17 Pages PDF
Abstract

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 industryThe 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.

Related Topics
Physical Sciences and Engineering Engineering Industrial and Manufacturing Engineering
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