Abstract
Work safety control and analysis of accidents during construction performance are one of the most important issues of construction management. The paper focuses on post-accident absence as an element of occupational safety management. Somehow, the length of the post-accident absence can be treated as an indicator of building performance safety. The paper attempts to answer the question of whether it is possible to use boosted classifier ensembles to predict the post-accident absence length using a small set of historical observations, and which classification algorithm is the most promising to solve the prediction problem. It also proves that there is a dependence between the length of the post-accident absence and the cause of the accident or working conditions The choice of boosted algorithms is not accidental. Thanks to the use of aggregation methods it is possible to build classifiers that predict precisely and do not require any initial data treatment, which simplifies the prediction process significantly. The model of the prediction problem has been clarified. To identify the most promising classifier ensemble the prediction accuracy measures of selected classification algorithms were analyzed. The data used to build models was gathered on national (Polish) construction sites.
Highlights
An accident at work is defined in ESAW (European Statistics on Accidents at Work) methodology as a discrete occurrence during work which leads to physical or mental harm
Fatal accidents at work are those that lead to the death of the victim within one year, while non-fatal accidents at work collected within ESAW [1, 2] are those that imply at least four full calendar days of absence from work
The paper matches up with work safety modelling problems. It attempts to answer the question whether it is possible to use boosted decision trees to predict the post-accident absence length using historical observations, and which algorithm is the most promising to apply in the prediction problem
Summary
An accident at work is defined in ESAW (European Statistics on Accidents at Work) methodology as a discrete occurrence during work which leads to physical or mental harm. According to ILO [3] report, averagely, every 10 minutes one construction worker bears death during his work. A study on the above-mentioned papers and global research carried out by Saiful, Razwanul and Tarek [8], Mistikoglu et al [9], Chua and Goh [10], or Chan, Leung and Liu [11] indicate that there is a strong need to build models of advisory systems supporting H&S management in its various aspects. Such systems should be effective, easy to use, and predict precisely with the historical data. It attempts to answer the question whether it is possible to use boosted decision trees (a selected type of classifier ensembles) to predict the post-accident absence length using historical observations, and which algorithm is the most promising to apply in the prediction problem
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