최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기KSCE Journal of Civil and Environmental Engineering Research = 대한토목학회논문집, v.42 no.1, 2022년, pp.127 - 134
조민건 (성균관대학교 미래도시융합공학과) , 이동환 (성균관대학교 미래도시융합공학과) , 박주영 (성균관대학교 건설환경시스템공학과) , 박승희 (성균관대학교 건설환경공학부)
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 const...
Beautiful Soup (2020). Beautiful soup documentation, Available at: https://www.crummy.com/software/BeautifulSoup/bs4/doc/ (Accessed: June 25, 2020).
Cho, J. H. (2012). "A study on the causes analysis and preventive measures by disaster types in construction fields." Journal of the Korea Safety Management & Science, Vol. 14, No. 1, pp. 7-13.
Cho, Y. R., Kim, Y. C. and Shin, Y. S. (2017). "Prediction model of construction safety accidents using decision tree technique." Journal of the Korea Institute of Building Construction, Vol 17, No. 3, pp. 295-303 (in Korean).
Choi, S. J., Kim, J. H. and Jung, K. H. (2021). "Development of prediction models for fatal accidents using proactive information in construction sites." Journal of the Korean Society of Safety, Vol. 36, No. 3, pp. 31-39 (in Korean).
Choi, S. Y. (2020). Comparison analysis of deaths in construction industry in OECD countries, Construction & Economy Research Institute of Korea, pp. 13 (in Korean).
Cortes, C. and Vapnik, V. (1995). "Support-vector networks." Machine Learning, Vol. 20, pp. 273-297.
Devlin, J., Chang, M. W., Lee, K. and Toutanova, K. (2019). "BERT: Pre-training of deep bidirectional transformers for language understanding." arXiv:1810.04805v2, pp. 1-16.
Fisher, A., Rudin, C. and Dominici, F. (2019). "All models are wrong, but many are useful: learning a variable's importance by studying an entire class of prediction models simultaneously." arXiv:1801.01489v5, pp. 1-81.
Ha, M. S. and Ahn, H. C. (2019). "A machine learning-based vocational training dropout prediction model considering structured and unstructured data." Journal of the Korea Contents Association, Vol. 19, No. 1, pp. 1-15.
Hoskins, J. C. and Himmelblau, D. M. (1992). "Process control via artificial neural networks and reinforcement learning." Computers & Chemical Engineering, Vol. 16, No. 4, pp. 241-251.
Kim, B. S. (2008). "The appropriation and the use scheme of safety control cost for reducing severity rate of injury on construction." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 28, No. 3D, pp. 383-390 (in Korean).
Kim, Y. C., Yoo, W. S. and Shin, Y. S. (2017). "Application of artificial neural networks to prediction of construction safety accidents." Journal of the Korean Society of Hazard Mitigation, Vol. 17, No. 1, pp. 7-14 (in Korean).
Korea Labor Institute (KLI) (2013). Construction industry accident status analysis and policy direction, pp. 31 (in Korean).
Korea Occupational Safety and Health Agency (KOSHA) (2019). 2019 Large accident report book, pp. 9 (in Korean).
Lee, C. H., Lee, Y. J. and Lee, D. H. (2020). "A study of fine tuning pre-trained korean BERT for question answering performance development." Journal of Information Technology Services, Vol. 19, No. 5, pp. 83-91 (in Korean).
Lee, S. G. (2018). "A study on the trends of construction safety accident in unstructured text using topic modeling." Journal of the Korea Academia-Industrial Cooperation Society, Vol. 19, No. 10, pp. 176-182 (in Korean).
Lim, W. J., Kee, J. H., Seong, J. H. and Park, J. Y. (2019). "Development of accident cause analysis model for construction site." Journal of the Korean Society of Safety, Vol. 34, No. 1, pp. 45-52 (in Korean).
Ministry of Employment and Labor (MOEL) (2020). 2019 Industrial accident analysis of current situation, pp. 32 (in Korean).
Park, K. C. and Kim, H. K. (2021). "Analysis of seasonal importance of construction hazards using text mining." KSCE Journal of Civil and Environmental Engineering Research, KSCE, Vol. 41, No. 3, pp. 305-316 (in Korean).
Raschka, S. (2018). "Model evaluation, model selection, and algorithm selection in machine learning." arXiv:1801.01489v5, pp. 1-45.
Rokach, L. (2016). "Decision forest: Twenty years of research." Information Fusion, Vol. 27, pp. 111-125.
Shanker, M., Hu, M. Y. and Hung, M. S. (1996). "Effect of data standardization on neural network training." The International Journal of Management Science, Vol. 24, No. 4, pp. 385-397.
Sokolova, M. and Lapalme, G. (2009). "A systematic analysis of performance measures for classification tasks." Information Processing and Management, Vol. 45, No. 4, pp. 427-437.
Sperandei, S. (2014). "Understanding logistic regression analysis." Biochemia Medica, Vol. 24, No. 1, pp. 12-18.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. and Polosukhin, I. (2017). "Attention is all you need." arXiv:1706.03762v5, pp. 1-15.
Woo, D. C., Moon, H. S., Kwon, S. B. and Cho, Y. H. (2019). "A deep learning application for automated feature extraction in transaction-based machine learning." Journal of Information Technology Service, Vol. 18, No. 2, pp. 143-159.
Yu, Y. J., Kim, T. H., Son, K. Y., Lee, K. H. and Kim, J. M. (2016). "Analysis of primary internal and external risk factors according to the accident causes in construction site." Journal of the Korea Institute of Building Construction, Vol. 16, No. 6, pp. 519-527 (in Korean).
Zhang, F., Fleyeh, H., Wang, X. and Lu, M. (2019). "Construction site accident analysis using text mining and natural language processing techniques." Automation in Construction, Vol. 99, pp. 238-248.
Zhang, H. (2004). The optimality of naive bayes, American Association for Artificial Intelligence, USA, pp. 1-6.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.