| The construction industry is currently experiencing explosive growth in its capability to generate and collect data. Advances in data storage technology, such as faster, higher capacity, and less expensive storage devices (e.g., magnetic disks, CD-ROMS), better database management systems, and data-warehousing technology, have allowed the transformation of an enormous amount of data into computerized database systems. These data, however, have no use until they are processed and interpreted. Knowledge Discovery in Database (KDD) is a process that combine Data Mining (DM) techniques from machine learning, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from a large database. By applying KDD and DM to the analysis of construction project data, one can identify valid, useful, and previously unknown pattems. The information can be used by construction managers to avoid problems in construction projects.; 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. |
