首页|Researchers from New Jersey Institute of Technology Report Recent Findings in Machine Learning (Identifying the Opportunities and Challenges of Project Bundling: Modeling and Discovering Key Patterns Using Unsupervised Machine Learning)
Researchers from New Jersey Institute of Technology Report Recent Findings in Machine Learning (Identifying the Opportunities and Challenges of Project Bundling: Modeling and Discovering Key Patterns Using Unsupervised Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Asce-Amer Soc Civil Engineers
Current study results on Machine Learning have been published. According to news reporting from Newark, New Jersey, by NewsRx journalists, research stated, “Project bundling is a strategy that combines several infrastructure projects into a single contract to improve the overall performance of projects. While previous research efforts have been conducted on certain aspects of project bundling, no research particularly focused on studying the opportunities and challenges of project bundling and the associated patterns between them.” Funders for this research include US Department of Transportation, Office of the Assistant Secretary for Research and Technology (OST-R), Center for Advanced Infrastructure and Transportation (CAIT) Region 2 UTC Consortium Led by Rutgers, The State University of New Jersey. The news correspondents obtained a quote from the research from the New Jersey Institute of Technology, “To this end, this paper addresses this knowledge gap. Based on data from 30 case studies that implemented project bundling strategies in the US, various opportunities and challenges were extracted. In addition, spectral clustering was implemented to cluster the identified opportunities and challenges based on the strength of their interconnectivities. Also, association rules mining analysis was conducted to determine key patterns. The results identified a total of 27 opportunities and 27 challenges for project bundling. Furthermore, the most critical associations between the opportunities and challenges were determined within each of the obtained clusters. The outcomes also reflected that while many opportunities and challenges could individually affect the performance of bundled projects, other opportunities and challenges could also result due to a combination of factors that might not be perceived to be critical on the individual level but rather become critical when combined with other factors.”
NewarkNew JerseyUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningNew Jersey Institute of Technology
NETL
NSTL
Asce-Amer Soc Civil Engineers
