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Fan, H., AbouRizk, S., Kim, H. and Zaiane, O. (2008) Assessing Residual Value of Heavy Construction Equipment Using Predictive Data Mining Model. Journal of Computing in Civil Engineering, 22, 181-191. http://dx.doi.org/10.1061/(ASCE)0887-3801(2008)22:3(181)

has been cited by the following article:

• TITLE: Knowledge Discovery in Data: A Case Study

  AUTHORS: Ahmed Hammad, Simaan AbouRizk

  KEYWORDS: Construction Management; Project Management; Knowledge Management; Data Warehousing; Data Mining; Knowledge Discovery in Data (KDD); Industrial Construction; Labour Resources

  JOURNAL NAME: Journal of Computer and Communications, Vol.2 No.5, March 10, 2014

  ABSTRACT: It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.