Knowledge Discovery and Machine Learning in Construction Project Databases
Kim, HyunJoo
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https://hdl.handle.net/2142/83195
Description
Title
Knowledge Discovery and Machine Learning in Construction Project Databases
Author(s)
Kim, HyunJoo
Issue Date
2002
Doctoral Committee Chair(s)
Lucio Soibelman
Department of Study
Civil Engineering
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Language
eng
Abstract
A KDD framework was developed to convert construction project data into knowledge. This paper shows the nine steps in the KDD process: (i) understanding and defining the problem, (ii) collecting data, (iii) exploring data, (iv) cleaning data, (v) enhancing data, (vi) selecting data attributes, (vii) mining data, (viii) analyzing the result, and (ix) evaluating the result. In this methodological procedure, the complexity of the construction data was considered to optimize the opportunities to discover valuable knowledge. To test the feasibility of the proposed approach, the KDD process framework was validated and tested with a database, RMS (Resident Management System), provided by the U.S. Army Corps of Engineers. Obviously, knowledge cannot be obtained from a database if the data have been collected inconsistently. In this thesis, the validation was conducted by comparing the results from the KDD process with estimations from a publication reference (RSMeans 2001) and project-control software (Monte Carlo simulation) used frequently by construction experts in industry. The result of the validation showed that the developed KDD framework would provide the construction manager the ability to identify possible project problems, such as causes of delays in activity, and to predict duration for dealing with the delayed activity.
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