EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 251 (2018) MATEC Web Conf., 251 (2018) 06020 References
Open Access
Issue
MATEC Web Conf.
Volume 251, 2018
VI International Scientific Conference “Integration, Partnership and Innovation in Construction Science and Education” (IPICSE-2018)
Article Number 06020
Number of page(s) 8
Section Risk Management in Construction
DOI https://doi.org/10.1051/matecconf/201825106020
Published online 14 December 2018
  1. U.S. Energy Information Administration. How large are U.S. coal reserves? (2018) https://www.eia.gov/coal/reserves/ [Google Scholar]
  2. U.S. Bureau of Labor Statistics. Injuries, illnesses, and fatalities in the coal mining industry. (2010) https://www.bls.gov/iif/oshwc/osh/os/osar0012.htm [Google Scholar]
  3. U.S. Mine Safety and Health Administration. Mine safety and health at a glance. (2017) https://www.msha.gov/data-reports/statistics/mine-safety-and-health-glance [Google Scholar]
  4. U.S. Mine Safety and Health Administration. Coal employment and production. (2017) https://dol-msha-peir-mshagov-prod.s3.amazonaws.com/s3fs-public/Data_Reports/DEC_15_2016_Historical_MIWQ_Employment_and_Production.pdf [Google Scholar]
  5. U.S. Bureau of Labor Statistics. Coal mining injuries, illnesses, and fatalities fact sheet. (2010) https://www.bls.gov/iif/oshwc/osh/os/osar0012.pdf [Google Scholar]
  6. B. Nowrouzi, M. Rojkova, J. Casole, B. Nowrouzi-Kia. A bibliometric review of the most cited literature related to mining injuries. Int. J. Min. Reclam. Env. 31, 276–285 (2016) doi:10.1080/17480930.2016.1138850 [CrossRef] [Google Scholar]
  7. S Inzalkar, J Sharma. A survey on text mining techniques and application. Int. J. Res. In Sci. & Eng. 24, 1–14 (2015) [Google Scholar]
  8. D. Brown. Text mining the contributors to rail accidents. IEEE T. Intell. Transp. Sys. 17, 346–355 (2016). doi:10.1109/tits.2015.2472580 [CrossRef] [Google Scholar]
  9. T. Bunn, S. Slavova, L. Hall. Narrative text analysis of Kentucky tractor fatality reports. Accident Anal. Prev. 40, 419–425 (2008) doi:10.1016/j.aap.2007.07.010 [CrossRef] [Google Scholar]
  10. J. Dement, H. Liscomb, L. Li, C. Epling, T. Desai. Nail gun injuries among construction workers. Appl. Occup. Environ. Hyg. 18, 374–383 (2010) doi:10.1080/10473220301365 [CrossRef] [Google Scholar]
  11. K. McKensie, D. Scott, M. Campbell, R. McClure. The use of narrative text for injury surveillance research: A systematic review. Accident Anal. Prev. 42, 354–363 (2010) doi:10.1016/j.aap.2009.09.020 [CrossRef] [Google Scholar]
  12. K. Biswas, R. Zipf. Root causes of groundfall related incidents in U.S. mining industry. 22nd Int. Conf. on Ground Contr. in Min. (2003) https://www.cdc.gov/niosh/mining/userfiles/works/pdfs/rcogr.pdf [Google Scholar]
  13. A. Samuel. Some studies in machine learning using the game of checkers. IBM J. Res. Dev., 3, 211–229 (1959) [Google Scholar]
  14. D. Blei. Probabilistic topic models. Commun. ACM. 55, 77–84 (2012) doi:10.1145/2133806.2133826 [CrossRef] [Google Scholar]
  15. T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). (Springer, 2009) [Google Scholar]
  16. S. Debortoli, O. Müller, I. Junglas, J. Brocke. Text mining for information systems researchers: An annotated topic modeling tutorial. Comm. Assoc. Infor. Sys. 39, 7 (2016) doi:10.17705/1cais.03907 [Google Scholar]
  17. S. Kotsiantis, I. Supervised machine learning: A review of classification techniques. Informatica. 31, 249–268 (2007) https://datajobs.com/data-science-repo/Supervised-Learning-[SB-Kotsiantis].pdf [Google Scholar]
  18. U.S. Mine Safety and Health Administration. Accident Injuries Data Set (issued annually). https://arlweb.msha.gov/OpenGovernmentData/DataSets/Accidents.zip [Google Scholar]
  19. Section 103 of the Federal Mine Safety and Health Amendments Act of 1977, Public Law 95–164. https://arlweb.msha.gov/regs/30cfr/ [Google Scholar]
  20. Mine Accident, Injury, and Illness Report MSHA Form 7000-1. https://arlweb.msha.gov/forms/70001INB.HTM [Google Scholar]
  21. C. Chae, D. Passmore. MSHA data analyzed for topic modeling. (2018). https://osf.io/ab8rk/ [Google Scholar]
  22. L. Kurgan, P. Musilek. A survey of knowledge discovery and data mining process models. Knowl. Eng. Rev. 21, 1–24 (2008). [Google Scholar]
  23. B. Frigyik, A. Kapila, M. Gupta. Introduction to the Dirichlet distribution and related processes. (2010) https://goo.gl/LKk79j [Google Scholar]
  24. R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2013) http://www.R-project.org/. [Google Scholar]
  25. C. Chae, D. Passmore. R code for topic modeling MSHA data. (2018). https://osf.io/ab8rk/ [Google Scholar]
  26. C. Chae, D. Passmore. Supplementary figures that report findings for topic modeling MSHA data. (2018). https://osf.io/ab8rk/ [Google Scholar]
  27. O. Müller, J. vom Brocke, S. Debortoli. Utilizing big data for information systems research: Challenges, promises and guidelines. European Journal of Information Systems, 25, 289–302 (2016) doi:10.1057/ejis.2016.2 [CrossRef] [Google Scholar]
  28. J. Grimmer, B. Stewart. Text as data: The promise and pitfalls of automated content analysis for political texts. Political Analysis, 21(3), 267–297 (2013) doi:10.1093/pan/mps028. [CrossRef] [Google Scholar]
  29. Borkovskaya V.G., Bardenwerper W., Roe R. Interactive teaching of risk management in the Russian construction industry. IOP Conf. Series: Materials Science and Engineering, 365 (2018) 062030 doi:10.1088/1757-899X/365/6/062030 [CrossRef] [Google Scholar]
  30. Borkovskaya V.G., Passmore D. Behavioral engineering model to identify risks of losses in the construction industry. Advances in Economics, Business and Management Research (France-Netherlands). Atlantis Press. In press. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.