title: Pattern extraction for high-risk accidents in the construction industry: a data-mining approach.
abstract: Accidents involving falls and falling objects (group I) are highly frequent accidents in the construction industry. While being hit by a vehicle, electric shock, collapse in the excavation and fire or explosion accidents (group II) are much less frequent, they make up a considerable proportion of severe accidents. In this study, multiple-correspondence analysis, decision tree, ensembles of decision tree and association rules methods are employed to analyse a database of construction accidents throughout Iran between 2007 and 2011. The findings indicate that in group I, there is a significant correspondence among these variables: time of accident, place of accident, body part affected, final consequence of accident and lost workdays. Moreover, the frequency of accidents in the night shift is less than others, and the frequency of injury to the head, back, spine and limbs are more. In group II, the variables time of accident and body part affected are mostly related and the frequency of accidents among married and older workers is more than single and young workers. There was a higher frequency in the evening, night shifts and weekends. The results of this study are totally in line with the previous research.
authors.affiliation: a Civil and Environmental Engineering Department , Amirkabir University of Technology , Tehran , Iran.
authors.forename: Mehran
authors.initials: M
authors.lastname: Amiri
authors.affiliation: a Civil and Environmental Engineering Department , Amirkabir University of Technology , Tehran , Iran.
authors.forename: Abdollah
authors.initials: A
authors.lastname: Ardeshir
authors.affiliation: b Department of Industrial Engineering and Management Systems , Amirkabir University of Technology , Tehran , Iran.
authors.forename: Mohammad Hossein
authors.initials: MH
authors.lastname: Fazel Zarandi
authors.affiliation: c Computer Engineering Department , Sharif University of Technology , Tehran , Iran.
authors.forename: Elahe
authors.initials: E
authors.lastname: Soltanaghaei
mesh_terms: D000062:Accidents, Occupational / Q000517:prevention & control / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D060389:Construction Industry / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D013997:Time Factors; D014947:Wounds and Injuries / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control; D055815:Young Adult
keywords: association rules; construction safety; ensembles of decision tree; high-risk accidents; multiple correspondence analysis; pattern extraction