Research talk by Dr. Xuan Lv from the Florida International University

Time: 10:30pm-11:30pm, 14 May. 2019

Room & Place: 306 room, Tongji Building Block A

Visitor: Dr. Xuan Lv

INFORMATION

Host: Prof Yongkui Li

Host department: Department of Construction Management and Real Estate

Schedule: 45mins talk +15mins Q&A

Abstract:

The infrastructure planning process in United States has long been “criticized for resulting in frequent delays in the development of important projects designed to improve the safety and operating conditions of a region’s transportation system”; the time to complete the planning process for large-scale infrastructure projects nearly tripled since the 1970s. Based on a number of studies conducted to identify the constraints for accelerating the infrastructure planning process, two primary causes of process inefficiencies were identified: (1) infrastructure planners have limited ability to find the right information, at the right time to support mission-critical analyses; and (2) there is late identification of stakeholder concerns and support levels. This talk presents two solutions towards addressing these problems: (1) developing context-aware information retrieval methods to support the search and retrieval of relevant textual information in the infrastructure planning domain; and (2) developing stakeholder opinion mining methods to identify potential concerns and stakeholder support levels early in the project planning process.

Bio

Xuan Lv, PhD, is an Assistant Professor from Moss School of Construction, Infrastructure, and Sustainability at Florida International University (FIU). Prior joining FIU, he obtained a doctoral and a master’s degree in Civil Engineering from University of Illinois at Urbana Champaign, and a bachelor’s degree in Management Science and Engineering from Wuhan University. His research focuses on data-driven human sensitive infrastructure decision making, with special interests on applying advanced data analytical techniques, such as artificial intelligence and machine learning, to solve unique problems that challenge the architectural, engineering, and construction industry.