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A fall (also referred to as a tumble) is the most common type of accident at steel construction (SC) sites. To reduce the risk of falls, current site safety management relies mainly on checklist evaluations. However, current on-site inspection is conducted under passive supervision, which fails to provide early warning to occupational accidents. To overcome the limitations of the traditional approach, this paper presents the development of a fall risk assessment model for SC projects by establishing a Bayesian network (BN) based on fault tree (FT) transformation. The model can enhance site safety management through an improved understanding of the probability of fall risks obtained from the analysis of the causes of falls and their relationships in the BN. In practice, based on the analysis of fall risks and safety factors, proper preventive safety management strategies can be established to reduce the occurrences of fall accidents at SC sites. [ABSTRACT FROM AUTHOR]

Abstract: Construction accident research involves the systematic sorting, classification, and encoding of comprehensive databases of injuries and fatalities. The present study explores the causes and distribution of occupational accidents in the Taiwan construction industry by analyzing such a database using the data mining method known as classification and regression tree (CART). Utilizing a database of 1542 accident cases during the period 2000–2009, the study seeks to establish potential cause-and-effect relationships regarding serious occupational accidents in the industry. The results of this study show that the occurrence rules for falls and collapses in both public and private project construction industries serve as key factors to predict the occurrence of occupational injuries. The results of the study provide a framework for improving the safety practices and training programs that are essential to protecting construction workers from occasional or unexpected accidents. [Copyright &y& Elsevier]

Abstract: Most models of the incidence of occupational accidents in the construction industry are composed of multiple factors. Although statistical techniques can be used to infer cause-and-effect relationships among these factors, the large number of factors involved and the complexity of the relationships among them make it difficult for managers to identify potential hazards in construction projects and thus develop effective safety procedures. This study addresses this problem by using the association-rule method of data mining in performing an analysis of 1347 accidents in the Taiwan construction industry during the period 2000–2007. The association-rule method enables potential cause-and-effect relationships to be identified among the many factors that play a role in occupational accidents in the construction industry. The study finds that such accidents tend to occur when certain combinations of hazards are present – especially working in high places without protective measures, loss of balance when in motion, failure to use protective equipment, insufficient experience, and injurious contact with unstable structures. These hazards are especially evident in small enterprises with less than 10 persons. The results can help management to formulate effective safety policies with regard to management shortcomings and staff training. [Copyright &y& Elsevier]