Scenario-based automated data preprocessing to predict severity of construction accidents

Occupational accidents are common in the construction industry, therefore developing prediction models to detect high severe accidents would be useful. However, existing studies are limited and usually focus on selecting the most appropriate machine learning method rather than identifying the most e...

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Bibliographic Details
Published in:Automation in construction 2022-08, Vol.140, p.104351, Article 104351
Main Authors: Koc, Kerim, Gurgun, Asli Pelin
Format: Article
Language:English
Subjects:
Accident risk assessment
Accident severity
Artificial intelligence
Automated pre-processing
Construction industry
Construction site accidents
eXtreme gradient boosting (XGBoost)
Industrial development
Machine learning
Missing data
Occupational health and safety (OHS)
Outliers (statistics)
Oversampling
Performance prediction
Permutations
Prediction models
Preprocessing
Resampling
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Summary:Occupational accidents are common in the construction industry, therefore developing prediction models to detect high severe accidents would be useful. However, existing studies are limited and usually focus on selecting the most appropriate machine learning method rather than identifying the most effective preprocessing pipeline before the prediction. In this study, a scenario-basis automated preprocessing model that identifies the best scenario is developed to predict the severity of construction accidents. The results show that the scenario combination of not removing missing data, not applying data binning, considering outliers, applying Min-Max-Scaler and one-hot encoding, and data resampling with random oversampling yielded the highest prediction performance with 0.6092 of F1-score. Permutation importance of XGBoost analysis indicates that year, cause material, age, past accidents, experience, and salary are the most influential attributes. This study contributes to society/practice through a model preventing high-severe accidents and theory/technology with novel preprocessing model to perform more reliable predictions. •A scenario-basis automated preprocessing model is proposed.•Performances of preprocessing scenarios are tested with XGBoost.•Severity of construction accidents is predicted.•A construction safety management framework is developed.•The framework is used in construction sites to prevent high severe accidents.
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2022.104351