Virtual Learning
CTIPS: Data-Driven Inspection Planning for Utah Culverts Using Federated Learning (CTIPS-005)

Why Attend

• A cost-efficient method for assessing culvert conditions and prioritizing maintenance tasks with limited highway infrastructure data
• The role of condition prediction models in optimizing maintenance budgets for underrepresented infrastructure such as culverts
• The efficacy of federated learning as a means of data-sharing between state DOTs, enabling prediction accuracy while ensuring data privacy

Your Presenter(s)

Abbas Rashidi, PhD, CPC, is an Associate Professor of Construction Engineering at the University of Utah. His dual educational background in the areas of construction engineering, as well as electrical and computer engineering has enabled him to conduct multidisciplinary research and adopt state-of-the-art AI/ML tools and computing solutions to tackle research problems in the construction engineering domain. Abbas has more than 20 years of experience as a researcher and practitioner within the construction industry and is currently serving as the associate editor of the ASCE Journal of Construction Engineering and Management and ASCE Journal of Performance of Constructed Facilities.