Non-linearity identification for construction workers' personality-safety behaviour predictive relationship using neural network and linear regression modelling
This work addresses safety management in construction by identifying workers prone to unsafe behaviors, though it is incremental as it applies existing neural network methods to a specific domain.
The study developed a neural network model to predict construction workers' safety behavior from personality traits, finding a nonlinear relationship with highly satisfactory prediction accuracy.
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.