| Title |
Development of Machine Learning-based Construction Accident Prediction Model Using Structured and Unstructured Data of Construction Sites |
| Authors |
조민건(Cho, Mingeon) ; 이동환(Lee, Donghwan) ; 박주영(Park, Jooyoung) ; 박승희(Park, Seunghee) |
| DOI |
https://doi.org/10.12652/Ksce.2022.42.1.0127 |
| Keywords |
건설재해; 예측모델; 기계학습; BERT; Decision Forest Construction accident; Prediction model; Machine learning; BERT; Decision forest |
| Abstract |
Recently, policies and research to prevent increasing construction accidents have been actively conducted in the domestic construction industry. In previous studies, the prediction model developed to prevent construction accidents mainly used only structured data, so various characteristics of construction sites are not sufficiently considered. Therefore, in this study, we developed a machine learning-based construction accident prediction model that enables the characteristics of construction sites to be considered sufficiently by using both structured and text-type unstructured data. In this study, 6,826 cases of construction accident data were collected from the Construction Safety Management Integrated Information (CSI) for machine learning. The Decision forest algorithm and the BERT language model were used to train structured and unstructured data respectively. As a result of analysis using both types of data, it was confirmed that the prediction accuracy was 95.41 %, which is improved by about 20 % compared to the case of using only structured data. Conclusively, the performance of the predictive model was effectively improved by using the unstructured data together, and construction accidents can be expected to be reduced through more accurate prediction. |