Generating Construction Knowledge With Knowledge Discovery in Databases

Lucio Soibelman1 & Hyunjoo Kim2

1Assistant Professor, Dept. of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
2Ph.D. Candidate, Dept. of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801

Abstract

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.

Keywords

Knowledge Discovery in Databases (KDD), Data Mining, Machine Learning, Lean Construction, Knowledge, Decision Trees, Neural Networks

Files

Reference

Soibelman, L. & Kim, H. 2000, 'Generating Construction Knowledge With Knowledge Discovery in Databases' In:, 8th Annual Conference of the International Group for Lean Construction. Brigthon, UK, 17-19 Jul 2000.

Download: BibTeX | RIS Format