Kim, H (2002) Knowledge discovery and machine learning in construction project databases. PhD thesis, University of Illinois at Urbana-Champaign, USA.
Abstract
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.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Soibelman, L |
| Uncontrolled Keywords: | complexity; duration; construction project; learning; visualization; data mining; machine learning; validation |
| Index terms: | complexity, publication, visualization, machine learning, construction manager, validation, construction project, data storage, management system, pattern recognition, cleaning, duration, construction industry, statistics, causes of delay, mining, transformation, database, estimation, engineer, Monte Carlo simulation, data mining |
| Subjects: | financial and cost management, artificial intelligence, data science, modelling and simulation, mathematical modelling, business, research dissemination and communication, profession, systems engineering, industry analysis, geotechnical engineering, data management, project controls, professional development, production management, computer vision, maintenance engineering, management, design practice |
| Topics: | Business Strategy, Cost Management, Research Practice, Information Management, Roles and Professions, Design Practice, Digital Applications, Organizational Design, Time Control, Project Management, Engineering Principles |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here