CEE researchers will help the Department of Defense develop better systems to evaluate the structural health of rail lines and modernize pavement design and evaluation procedures in a new project funded by the U.S. Army Engineer Research and Development Center (ERDC) and led by Applied Research Associates Inc.
Within CEE, Professor Erol Tutumluer and Research Associate Professor J. Riley Edwards (MS 06, PhD 19) are leading the work for this multi-year research project called Advancing Power Projection through Lines of Communication (APPLoC). In the first year, the Department of Civil and Environmental Engineering will receive $2.1 million in grant funding.
As a whole, the research efforts will specifically address the military engineering focus area of Infrastructure maintenance, repair and reconstruction technologies. Two major tasks that CEE department researchers will be involved in are the development of a rail structural capacity rating for DOD and the development of Joint Evaluation and Design Integrated (JEDI) Solution software tools for designing new and rehabilitated pavements for DOD. APPLoC will also develop and deliver innovative prototype solutions to rapidly assess the condition of existing infrastructure and support the rapid repair of damaged infrastructure such as pavements, bridges and railways.
Tutumluer specializes in Transportation Geotechnics with research interests and expertise in characterization of pavement and railroad track geomaterials, geosynthetics, modeling granular foundation systems, and mechanistic analysis and design. He has taught graduate and undergraduate courses in transportation soils engineering, subgrade soil and aggregate behavior and stabilization,
introduction to transportation engineering, pavement analysis and design, airport facilities design, and transportation infrastructure for smart mobility. He is the Abel Bliss Professor in Engineering.
Edwards specializes in the design and performance of railway infrastructure and its components, with emphasis on railway sleeper and fastening system design for passenger, transit and freight applications, and a focus on materials and structural design. Additional research areas include the application of Artificial Intelligence, Deep Neural Networks, and machine vision technology to railroad inspection tasks.