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Unstructured Construction Data Analytics Using R Programming - Focused on Overseas Construction Adjudication Cases -
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 Title & Authors
Unstructured Construction Data Analytics Using R Programming - Focused on Overseas Construction Adjudication Cases -
Lee, Jee-Hee; Yi, June-Seong; Son, JeongWook;
 
 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
Unstructured Data Analytics;Construction Adjudication Cases;Overseas Construction Disputes;
 Language
Korean
 Cited by
1.
텍스트 마이닝을 통한 해외건설공사 입찰정보 분석 - 해외건설공사의 입찰자 질의(Bidder Inquiry) 정보를 대상으로 -,이지희;이준성;손정욱;

한국건설관리학회논문집, 2016. vol.17. 5, pp.89-96 crossref(new window)
 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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

9.
Yim, D. (2015), Big data analysis using R, Free academy, 21-50.