Research has found that many occupational accidents are foreseeable, being the result of people's unsafe behaviour from a retrospective point of view. The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates. In recent years, researchers have found that people do have stable predispositions to engage in certain safety behavioural patterns which vary among individuals as a function of personality features . In this study, an innovative forecasting model, which employs machine learning algorithms, is developed to estimate construction workers' behavioural tendency based on the Big Five personality taxonomy . The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour. The developed model is found to be sufficient to have satisfactory accuracy in explaining and predicting workers' safety behaviour. This finding provides the empirical evidence to support the usefulness of personality traits as effective predictors of people's safety behaviour at work. In addition, this study could have practical implications. The machine learning model developed can help identify vulnerable workers who are more prone to undertake unsafe behaviours, which is proven to have good prediction accuracy and is thereby potentially useful for decision making and safety management on construction sites .
 The prediction of workers' safety behaviour can help identify vulnerable workers who intend to undertake unsafe behaviours and be useful in the design of management practices to minimise the occurrence of accidents . The latest literature has evidenced that there is within-population diversity that leads people's intended safety behaviours in the workplace, which are found to vary among individuals as a function of their personality traits . In this study, an innovative forecasting model, which employs neural network algorithms, is developed to numerically simulate the predictive relationship between construction workers' personality traits and their intended safety behaviour . The data-driven nature of neural network enabled a reliable estimate of the relationship, which allowed this research to find that a nonlinear effect exists in the relationship . This research has practical implications. The neural network developed is shown to have highly satisfactory prediction accuracy and is thereby potentially useful for assisting project decision-makers to assess how prone workers are to carry out unsafe behaviours in the workplace .
D
Research has found that many occupational accidents are foreseeable, being the result of people's unsafe behaviour from a retrospective point of view.
R
will enable the prior insights into each worker's behavioural tendency and will
can help identify vulnerable workers who intend to undertake unsafe behaviours and
R
prior to
to minimise
R
and contribute to the reduction of injury rates. In recent years, researchers have found that people do have stable predispositions to engage in certain safety behavioural patterns which
. The latest literature has evidenced that there is within-population diversity that leads people's intended safety behaviours in the workplace, which are found to
R
personality features
their personality traits
R
machine learning
neural network
R
estimate
numerically simulate the predictive relationship between
R
behavioural tendency based on the Big Five personality taxonomy
personality traits and their intended safety behaviour
R
machine learning technique
neural network
D
personality-safety behaviour
R
study to provide novel insight that nonlinearity may exist
research to find that a nonlinear effect exists
R
between construction workers' personality traits and safety behaviour. The developed model is found to be sufficient to have satisfactory accuracy in explaining and predicting workers' safety behaviour. This finding provides the empirical evidence to support the usefulness of personality traits as effective predictors of people's safety behaviour at work. In addition, this study could have
. This research has
R
machine learning model developed can help identify vulnerable workers who are more prone to undertake unsafe behaviours, which is proven to have good
neural network developed is shown to have highly satisfactory
R
decision making and safety management on construction sites
assisting project decision-makers to assess how prone workers are to carry out unsafe behaviours in the workplace