ABSTRACT CMS0094022 "Knowledge Discovery in Databases and Data Mining as New tools to Support Research and Educational Advances in Modern Construction Management " PI: Lucio Soibelman, University of Illinois at Urbana-Champaign
The construction industry is seeing an explosive growth in its capabilities to both generate and collect data. Advances in scientific data collection, the introduction of bar codes for almost all-commercial products, new sensor technologies, wireless computing, and new laser scanning technologies, have generated a flood of data. These advances coupled with advances in data storage technology, such as faster, higher capacity, and cheaper storage devices, better database management systems, and data warehousing technology, have increased the availability of computerized construction data. However, in most cases, these data are used only for communication purposes and stored in a file or a database without being analyzed. This project intends to study this increasing amount of available data by applying data mining and knowledge discovery in databases. Knowledge discovery in databases and data mining are technologies that combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases and visualization to automatically extract concepts, concepts interrelationships, and patterns of interest from large databases. The objectives of this CAREER program are to: 1) Generate improved methods to obtain novel knowledge from large construction databases developing model-building templates and wizards to guide novice construction knowledge model builders through the process of creating models based on their own data; 2) Improve access to past construction management experience and knowledge by practitioners and students; 3) Use active learning techniques to improve education of students at all levels by developing an educational simulation game with the knowledge generated during this research; and 4) Teach civil and environmental engineering graduate students the process of knowledge generation through the application and development of data mining, machine learning and artificial intelligence tools. Given the importance of the construction industry in the U.S economy and the large amount of money wasted in litigation due to project delays, impractical budgets, and projects that neither satisfy quality requirements nor meet performance expectations, improved management tools are critically needed. This research promises to result in valuable management tools for improving project planning and control, which, if applied to large-scale infrastructure projects, may result in substantial cost savings nationwide. These research benefits can be extended to all sub-fields of construction management
