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Practical Data Science for Roadway Professionals & Digital Twins for Road Infrastructure

Nov 2 - Nov 8

| $3000 – $3500

Overview

This cutting-edge course is designed for roadway professionals looking to leverage the power of data science and digital twin technology in the field of road infrastructure. It offers a practical approach to understanding and implementing digital twins – virtual replicas of physical road systems – for enhanced decision-making, predictive maintenance, and improved road safety. The course covers the fundamentals of data science and its application in creating and managing digital twins, focusing on real-world challenges in road infrastructure. Participants will learn about data collection, analysis, modeling, and the integration of IoT technologies. The course also explores the use of AI and machine learning in predictive analytics and the visualization of complex data for better infrastructure management. Through a blend of expert-led lectures, hands-on workshops, and a field trip, participants will gain the skills needed to effectively apply digital twin technology in their work.

Course Outcomes

  • Understanding of Digital Twins in Road Infrastructure: Gain a comprehensive understanding of digital twin technology and its application in road infrastructure for enhanced planning, monitoring, and maintenance.
  • Proficiency in Data Collection and Analysis: Learn methods for effective data collection, processing, and analysis, crucial for building accurate and reliable digital twins.
  • Skills in Predictive Modeling and Analytics: Develop skills in using predictive analytics and modeling to forecast infrastructure wear and tear, traffic patterns, and maintenance needs.
  • Knowledge of IoT Integration in Road Infrastructure: Understand how to integrate IoT devices and sensors into road systems for real-time data gathering and improved operational efficiency.
  • Application of AI and Machine Learning: Explore the use of artificial intelligence and machine learning algorithms in analyzing complex data sets for predictive maintenance and decision making.

Target Audience

  • National Road & Transport Agency Managers
  • Highway Engineers and Managers
  • Federal and State Road Safety Agencies
  • Road Safety Professionals
  • Traffic Management Professionals
  • Private Consultants & Contractors

Agenda

Instructor

LEAD INSTRUCTOR

Mehran Mazari, Ph.D.
Assistant Professor, California State University Los Angeles

Dr. Mehran Mazari is an Assistant Professor in the Department of Civil Engineering at Cal State LA, specializing in Transportation Infrastructure, Materials and Applied Data Science, Artificial Intelligence (AI) and Machine Learning (ML). He is the faculty director of Sikand Center for Sustainable and Intelligent Infrastructures (SITI-Center) and founder of Sustainable Infrastructure Materials Research Lab (SIM-Lab) at Cal State LA. His research interests include sustainable and resilient transportation infrastructure, transportation infrastructure materials, and non-destructive evaluation of transportation infrastructure. He is member of technical committees at the Transportation Research Board of National Academies of Science and Engineering and co-chair of the LTPP subcommittee of the Highway Pavement Committee of the American Society of Civil Engineers (ASCE). Dr. Mazari has published more than 60 peer-reviewed journal and conference papers. He has been actively involved in several national and state research projects, including the National Highway Cooperative Research Program (NCHRP) and Federal Highway Administration (FHWA), among others.

Venue & Hotel

TBD

Address: TBD

Tel: +1 703 535 1001
E-mail: info@IRF.global