Application of machine learning to construction injury prediction

The needs to ground construction safety-related decisions under uncertainty on knowledge extracted from objective, empirical data are pressing. Although construction research has considered machine learning (ML) for more than two decades, it had yet to be applied to safety concerns. We applied two s...

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Bibliographic Details
Published in:Automation in construction 2016-09, Vol.69, p.102-114
Main Authors: Tixier, Antoine J.-P., Hallowell, Matthew R., Rajagopalan, Balaji, Bowman, Dean
Format: Article
Language:English
Subjects:
Attribute
Boosting
Construction safety
Injury prevention
Machine learning
Predictive modeling
Random Forest
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Summary:The needs to ground construction safety-related decisions under uncertainty on knowledge extracted from objective, empirical data are pressing. Although construction research has considered machine learning (ML) for more than two decades, it had yet to be applied to safety concerns. We applied two state-of-the-art ML models, Random Forest (RF) and Stochastic Gradient Tree Boosting (SGTB), to a data set of carefully featured attributes and categorical safety outcomes, extracted from a large pool of textual construction injury reports via a highly accurate Natural Language Processing (NLP) tool developed by past research. The models can predict injury type, energy type, and body part with high skill (0.236
ISSN:0926-5805
DOI:10.1016/j.autcon.2016.05.016