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건설현장의 공사사전정보를 활용한 사망재해 예측 모델 개발

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Alternative Title
Development of Prediction Models for Fatal Accidents using Proactive Information in Construction Sites
Abstract
In Korea, more than half of work-related fatalities have occurred on construction sites. To reduce such occupational accidents, safety inspection by government agencies is essential in construction sites that present a high risk of serious accidents. To address this issue, this study developed risk prediction models of serious accidents in construction sites using five machine learning methods: support vector machine, random forest, XGBoost, LightGBM, and AutoML. To this end, 15 proactive information (e.g., number of stories and period of construction) that are usually available prior to construction were considered and two over-sampling techniques (SMOTE and ADASYN) were used to address the problem of classimbalanced data. The results showed that all machine learning methods achieved 0.876~0.941 in the F1-score with the adoption of over-sampling techniques. LightGBM with ADASYN yielded the best prediction performance in both the F1-score (0.941) and the area under the ROC curve (0.941). The prediction models revealed four major features: number of stories, period of construction, excavation depth, and height. The prediction models developed in this study can be useful both for government agencies in prioritizing construction sites for safety inspection and for construction companies in establishing pre-construction preventive measures.
Author(s)
최승주김진현정기효
Issued Date
2021
Type
Article
Keyword
occupational safetyconstructionsafety inspectionmachine learningimbalanced data
DOI
https://doi.org/10.14346/JKOSOS.2021.36.3.31
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8909
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_9800281&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,%EA%B1%B4%EC%84%A4%ED%98%84%EC%9E%A5%EC%9D%98%20%EA%B3%B5%EC%82%AC%EC%82%AC%EC%A0%84%EC%A0%95%EB%B3%B4%EB%A5%BC%20%ED%99%9C%EC%9A%A9%ED%95%9C%20%EC%82%AC%EB%A7%9D%EC%9E%AC%ED%95%B4%20%EC%98%88%EC%B8%A1%20%EB%AA%A8%EB%8D%B8%20%EA%B0%9C%EB%B0%9C&offset=0&pcAvailability=true
Publisher
한국안전학회지
Location
대한민국
Language
한국어
ISSN
1738-3803
Citation Volume
36
Citation Number
3
Citation Start Page
31
Citation End Page
39
Appears in Collections:
Engineering > Industrial Management Engineering
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