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Unstructured Construction Data Analytics Using R Programming - Focused on Overseas Construction Adjudication Cases -

R프로그래밍 기반의 비정형 건설 데이터 분석 - 해외건설 분쟁 판례 데이터를 대상으로 -

  • 이지희 (이화여자대학교 일반대학원 건축공학과) ;
  • 이준성 (이화여자대학교 건축공학과) ;
  • 손정욱 (이화여자대학교 건축공학과)
  • Received : 2016.02.04
  • Accepted : 2016.04.29
  • Published : 2016.05.30

Abstract

As construction projects are getting complex, the amount of information in order for performing project has rapidly increased. Thus, high quality of data management technique, which can promote project's productivity and profitability, is now a big issue in construction industry. Especially, as the importance of construction claim and dispute management is emphasized data analysis based risk management is becoming an important topic. This study analyzed overseas construction adjudication cases based on unstructured data analytics as a way of claim and dispute management. In order to analysis on unstructured data, which is written in text data, NLP, IR and Text Mining technique was applied, and some of meaningful results could be derived. From the text analysis written in construction case law, some construction dispute type was classified; loss of profit, document notification, practical completion, clear terms of contract, and payment. This study conducted a meaningful attempt in construction dispute research aspect as suggests a methodology which can enhance the accessibility and availability of construction adjudication cases.

Keywords

Acknowledgement

Supported by : 국토교통부, 한국연구재단

References

  1. Arcadis (2015). Global Construction Disputes Report 2015, 6-31.
  2. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  3. Kangari, R., & Riggs, L. S. (1989). Construction risk assessment by linguistics. Engineering Management, IEEE Transactions on, 36(2), 126-131. doi: 10.1109/17.18829.
  4. Lee, J., Son, J., & Yi, J. (2014). The application of text mining techniques for analysis of overseas construction dispute cases, Proceedings of Korea Institute of Construction Engineering and Management, 2014-11, 83-84.
  5. Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to information Retrieval, vol. 1, Cambridge university press Cambridge, 116-121.
  6. Meyer, D., Hornik, K., & Feinerer, I. (2008) Text Mining Infrastructure in R. Journal of Statistical Software, 25 (5). pp. 1-54. ISSN 1548-7660
  7. Fan, H., & Li, H. (2013). Retrieving similar cases for alternative dispute resolution in construction accidents using text mining techniques. Automation in Construction, 34, 85-91. https://doi.org/10.1016/j.autcon.2012.10.014
  8. Williams, T. P., & Gong, J. (2014). Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Automation in Construction, 43, 23-29. https://doi.org/10.1016/j.autcon.2014.02.014
  9. Yim, D. (2015), Big data analysis using R, Free academy, 21-50.