| 논문명 |
AutoML 기반의 이진, 다중 분류 모델 구축을 통한 건설 사고 발생 및 유형 예측 / Predicting Construction Safety Accidents and Types Using AutoML-based Binary and Multi-class Classification Models |
| 저자명 |
전정호(Jeon, JungHo) ; 최수연(Choi, SuYeon) ; 윤성배(Yun, SeongBae) ; 용선진(Yong, SunJin) ; 허영기(Huh, YoungKi) |
| 수록사항 |
대한건축학회논문집, Vol.40 No.9 (2024-09) |
| 주제어 |
건설안전;사고 예측;머신러닝;이진분류;다중분류;오토엠엘 ; Construction Safety;Accident Prediction;Machine Learning;Binary Classification;Multi-class Classification;AutoML |
| 요약1 |
AutoML을 사용해 CSI 데이터베이스 기반의 이진(사망, 부상) 및 다중 클래스(추락, 충돌 등) 분류 모델을 개발하는 것을 목적으로, 2019년부터 2024년 2월까지 수집된 235,665건의 사고 데이터를 분석해 18개의 주요 사고 요인을 추출하였다. 이진 분류 모델은 ET 알고리즘으로 95.9%의 정확도와 0.2771의 F1 점수를, 다중 클래스 분류 모델은 LGBM 알고리즘으로 57.4%의 정확도와 0.5503의 F1 점수를 기록하였다. 사고 객체는 두 모델 모두에서 가장 중요한 요인으로 나타났다. |
| 요약2 |
In 2022, the construction industry accounted for nearly half of all fatal accidents across sectors in South Korea. This study aims to develop
binary like fatality and injury and multi-class such as fall and struck-by classification models using AutoML to predict the occurrence and
types of construction accidents, based on data from the Construction Safety Integrated Management System (CSI) database. The dataset,
consisting of 235,665 accident cases from January 2019 to February 2024, includes 54 types of information, with 18 influential accident
factors identified. Preprocessed data were trained and tested using AutoML to determine optimal algorithms and influencing factors. Accuracy,
precision, recall, and F1 score metrics were used for validation. The binary classification model for predicting fatalities and injuries,
developed using the Extra Trees (ET) algorithm, achieved the highest accuracy of 95.9% and an F1 score of 0.2771. For predicting accident
types, the multi-class classification model using the LightGBM (LGBM) algorithm recorded the highest accuracy of 57.4% and an F1 score
of 0.5503. Feature importance analysis revealed that the accident object was the most critical factor in both models. This research is expected
to enhance safety management performance by efficiently identifying the likelihood and types of construction accidents. |
