Rough set theory for distilling construction safety measures

Chi Mint Tam, Thomas Tong and K. K. Chan

Construction Management and Economics, 2006, vol. 24, issue 11, 1199-1206

Abstract: There are numerous construction safety measures adopted by the local construction industry in Hong Kong. The purpose of this study is to distil the more significant measures from all these practices. To achieve this, the rough set theory, a data mining technique by applying the rule induction method, is proposed to distil the rules that determine the safety performance of construction firms. Rough sets represent a different mathematical approach to vagueness and uncertainty. It is based on the assumption that lowering the degree of precision in the data makes the data pattern more visible. Under such an assumption, the rough set theory can provide the ability of classifying vague and uncertain data. A practical example is used to illustrate its application to distil these safety measures and highlight those which are most effective and important in combating site accidents. There are three decision rules identified; the best one is companies with a comprehensive safety orientation programme and good safety award campaigns for senior management staff which give the lowest accident rate and the best safety performance. Safety management rules can be successfully reduced, facilitating contractors to direct their limited recourses effectively.

Keywords: Rough set theory; site operations; health and safety (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/01446190600879091 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:conmgt:v:24:y:2006:i:11:p:1199-1206

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RCME20

DOI: 10.1080/01446190600879091

Access Statistics for this article

Construction Management and Economics is currently edited by Will Hughes

More articles in Construction Management and Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().