Application of machine learning to construction injury prediction
| Autor: | Matthew R. Hallowell, Antoine J.-P. Tixier, Dean Bowman, Balaji Rajagopalan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Empirical data Boosting (machine learning) business.industry 0211 other engineering and technologies Poison control 02 engineering and technology Building and Construction High skill Machine learning computer.software_genre Random forest Construction site safety Control and Systems Engineering 021105 building & construction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Injury prediction Categorical variable computer Civil and Structural Engineering |
| Zdroj: | Automation in Construction. 69:102-114 |
| ISSN: | 0926-5805 |
| DOI: | 10.1016/j.autcon.2016.05.016 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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