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Trefor Williams Research Page (redirected from Construction Management Text Mining Research at Rutgers University)

Page history last edited by Trefor Williams 8 years, 3 months ago

Text and Data Mining Research

This website describes research in applying text mining and data analytics to Civil Engineering problems. I am a Professor of Civil Engineering at Rutgers University

 

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A Short Biography

Dr. Trefor P. Williams is a Professor of Civil Engineering and Senior Researcher at the Rutgers Center for Advanced Infrastructure and Transportation. He has 35 years of academic and consulting experience in the areas of construction management, and transportation engineering. Dr. Williams has five years of professional experience with Wilbur Smith and Associates as a traffic engineer responsible for the installation and initial operation of several computerized ITS systems. Dr. Williams has been a researcher at Rutgers for the last 28 years.  Dr. Williams research has focused on the application of Information Technology, Artificial Intelligence and Data Mining to transportation and construction problems. He has recently been studying the use of text mining to predict construction project cost increases. Dr. Williams has received research grants from the U.S. Army Corps of Engineers, New Jersey Department of Transportation, New York State Department of Transportation and the Port Authority of New York and New Jersey..  He won a competitive research fellowship from the IBM Endowment for the Business of Government in 2003 to study the application of Public Private Partnerships as a funding mechanism for government projects. Dr. Williams received his B.S. in Civil Engineering from Syracuse University. He received the M.S. and PhD degrees in Civil Engineering from the Georgia Institute of Technology. He is a Professional Engineer in New Jersey. Dr. Williams has authored two books and numerous journal and conference papers.

 

Text and Data Mining Research

Using Text Mining and Data Mining to Predict Project Cost Overruns

Sentiment Analysis to Rapidly Identify Construction Problems

Railroad Accidents

 

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