| Title |
Analysis of Occupational Injury and Feature Importance of Fall Accidents on the Construction Sites using Adaboost |
| Authors |
Choi, Jaehyun ; Ryu, HanGuk |
| DOI |
https://doi.org/10.5659/JAIK_SC.2019.35.11.155 |
| Keywords |
Construction Safety; Fall Accidents; Data Science; Machine Learning; Adaboost |
| Abstract |
The construction industry is the highest safety accident causing industry as 28.55% portion of all industries’ accidents in Korea. In particular,
falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major
disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea
Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for
Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning
algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of
occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We
extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be
applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the
network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and
types of accidents according to the site conditions from the real time data. |