Non-linearity identification for construction workers' personality-safety behaviour predictive relationship using neural network and linear regression modelling
Abstract
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.
Cite
@article{arxiv.1912.05944,
title = {Non-linearity identification for construction workers' personality-safety behaviour predictive relationship using neural network and linear regression modelling},
author = {Yifan Gao and Vicente A. Gonzalez and Tak Wing Yiu and Guillermo Cabrera-Guerrerod},
journal= {arXiv preprint arXiv:1912.05944},
year = {2020}
}
Comments
The manuscript is currently undergoing a major revision as some contents in its current form are not scientifically rigorous and can be misleading to potential readers. Thus, we apply for withdrawal of the manuscript