Akademik Veri Yönetim Sistemi
Journal of Construction Engineering, Management & Innovation (Online), cilt.6, sa.1, ss.1-15, 2023 (Hakemli Dergi)
Employees working on construction projects have a higher risk of experiencing
occupational accidents compared to workers in other sectors. Newly employed workers
might face an occupational accident shortly after they start working due to not being
able to notice risky environmental conditions. Despite existing studies in construction
safety literature focusing on several output variables such as fatality, accident type or
accident severity predictions, no studies have examined the short-term susceptibility of
construction workers to occupational injuries. This study aims to develop a model to
predict construction workers’ susceptibility to short-term occupational accidents using
interpretable machine learning (ML) methods. Hence, the primary research objective is
to identify construction workers who have high probability of experiencing an
occupational accident shortly after their employment. In this respect, a national dataset
of occupational accidents encountered in the construction industry in Turkey was
collected and subjected to various pre-processing elements (data cleaning, data scaling,
and data resampling) to prepare the data for prediction. At the processing step,
Stochastic Gradient Boosting (SGB) algorithm was applied for the classification purpose.
In the next step, Shapley Additive Explanations (SHAP) was used as an interpretable
artificial intelligence algorithm to explain how, to what extent, and in which direction the
input variables affect the prediction scheme, which is another distinguishing feature of
the present study compared to past studies in the subject matter. Results show that the
proposed SGB model is a powerful detector for the classification problem and salary of
workers, past accident in the company, and number of workers in the company were the
most influencing factors. Overall, this study contributes to practice by improving the
safety conditions of the newly employed workers as well as minimizing their accident
probability through intensified safety training. Given that contemporary safety
management applications demand a new set of data-driven inputs, proposed model is
expected to help industry professionals and safety managers apply more robust safety
risk mitigation and/or prevention measures.