TY  - CONF
TI  - Generating Construction Knowledge With Knowledge Discovery in Databases
AU  - Soibelman, Lucio
AU  - Kim, Hyunjoo
AD  - Assistant Professor, Dept. of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
AD  - Ph.D. Candidate, Dept. of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
PY  - 2000
DA  - 2000/07/17
T2  - 8th Annual Conference of the International Group for Lean Construction
C3  - 8th Annual Conference of the International Group for Lean Construction
CY  - Brighton, UK
AB  - As the construction industry is adapting to new computer technologies in terms of hardware and software, computerized construction data becomes increasingly available. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow us to identify novel patterns in construction projects through analyzing the large amount of construction project data. Those technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. This paper presents both the steps required for the implementation of KDD and DM tools on large construction database and one case study demonstrating the feasibility of the proposed approach. In order to test the feasibility of the proposed approach, a prototype of Knowledge Discovery in Databases (KDD) system was developed and tested with a database, RMS (Resident Management System), provided by the US Corps of Engineers.
KW  - Knowledge Discovery in Databases (KDD)
KW  - Data Mining
KW  - Machine Learning
KW  - Lean Construction
KW  - Knowledge
KW  - Decision Trees
KW  - Neural Networks
L1  - https://www.iglc.net/papers/details/123/pdf
UR  - https://www.iglc.net/papers/details/123
DB  - IGLC.net
LA  - English
N1  - Export date: 04 October 2026
ER  -