ABSTRACT

Unsafe behavior is a critical risk factor in the construction industry, but it cannot be quantitatively evaluated by the traditional risk matrix (RM) method. Based on accident cases in the building construction industry in China, probabilistic reasoning from unsafe behaviors to accidents is explored using a Bayesian network (BN) to modify the RM method. The unsafe behaviors involved in 287 accident cases of four types (fall, collapse, struck-by and lifting) are used to construct a BN model. The parameters of the model are revised via Beta and Dirichlet distributions, and the conditional probabilities of the occurrence of accidents caused by unsafe behaviors are then obtained to determine the impact of unsafe behaviors on different types of accidents. A list of behavioral risk levels for the four accident types is generated by considering the initial probabilities and the conditional probabilities. The results show that the MRM method is feasible and effective, and unsafe behaviors that have a great impact on the four accident types are identified. The MRM method provides a method for quantitatively evaluating behavioral risk and enhances behavioral risk management on construction sites; thus, it is of theoretical and practical significance.