Developing a Risk Assessment Model for construction safety

• Published in: International journal of project management Vol. 28; no. 6; pp. 593 - 600
• Main Authors: Fung, Ivan W.H., Tam, Vivian W.Y., Lo, Tommy Y., Lu, Lori L.H.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2010; Elsevier Science Ltd
• Subjects: Accident prevention; Accidents; Assessments; Construction; Construction accidents & safety; Construction industry; Decision support system; Mathematical models; Occupational safety; Prioritization; Random access memory; Reliability; Risk assessment; Safety; Studies; Work trade; Work trade; Prioritization; Construction; Safety; Decision support system; Risk assessment
• ISSN: 0263-7863; 1873-4634
• DOI: 10.1016/j.ijproman.2009.09.006

Problems on occupational health and safety are commonly occurred in the construction industry, including falling of materials or person from height, stepping on objects and injured by hand tools. Key of dealing with project safety and risk assessment in construction industry is important, thus, way on doing assessment and liability of assessment are necessary for all professionals. However, it is found that those professionals prone to heavily rely on their own experiences and knowledge on decision making on risk assessment, which lack of a systematic approach and lack of ways to check the reliability of the decisions. This paper investigates the need for people involved in construction to take systematic and effective risk assessments for different trades. 14 common types of trades, accidents, and accident causes are explored, in addition to suitable risk assessment methods and criteria. A Risk Assessment Model (RAM) is then developed for assessing risk levels as various project stages with various work trades. A case study is used to prove the workability and reliability of the developed RAM in the practical industry. The developed RAM is found to be benefit for predicting high-risk construction activities and thus preventing accidents occurred based on a set of historical accident data.