A Digital Twin for Roads: A combined physics and data-driven approach to predict roads deterioration
Loading Events

« All Events

  • This event has passed.

A Digital Twin for Roads: A combined physics and data-driven approach to predict roads deterioration

February 24 @ 2:00 pm 3:00 pm CET

About the Event

On 24 February 2022, the International Road Federation, in collaboration with the University of Birmingham (UoB), the University of Nottingham (UoA), and the University of Manchester (UoM), was pleased to present a webinar with the theme “A digital Twin for Roads: A combined physics and data-driven approach to predict roads deterioration”.

Road infrastructure systems have been suffering from ineffective maintenance strategies, exaggerated by budget restrictions. A more holistic road asset management approach enhanced by data-informed decision-making through effective condition assessment, distress detection, future condition predictions can significantly enhance maintenance planning, prolonging asset life. Recent technology innovations such as Digital Twins have great potential to enable the needed approach for road condition predictions and proactive asset management.

The webinar provided an overview of an ongoing research project, a collaboration between UoB, UoN and UoM, that looks at the design, development, implementation, and test for a pavement Digital Twin. The project also explores the potential benefits of digital twins in monitoring health, modeling pavement deterioration, and selecting maintenance methods based on cost-benefit analysis. The developing model of the pavement includes information related to traffic, climate conditions, pavement degradation, and past maintenance activities. The data is obtained and processed using sensors, big data analytics, machine learning, and hybrid artificial intelligence-numerical simulation techniques. This will enable a real-time reflection and better understanding of the characteristics of the pavement, which could potentially enhance pavement maintenance strategy decision-making with optimum selection of maintenance type and timing.

Note: Speakers’ presentations can be found below.

Did you miss it? Watch the recording now

Speaking for You

Dr. Mehran Eskandari Torbaghan

Lecturer in Infrastructure Asset Management, University of Birmingham
See bio

Dr Mehran Eskandari Torbaghan is a lecturer in Infrastructure Asset Management at the Department of Civil Engineering with a Doctor of Philosophy (PhD) focused in risk management and linear infrastructure systems from University of Birmingham. 

Mehran spent five years in the civil engineering industry, as a geotechnical engineer, before returning to academia to study a Master in Construction Management and then the PhD both at the University of Birmingham. 

He then worked as a research fellow at the University of Birmingham for around five years, before becoming a lecturer. He was also a visiting research fellow at the University of Nottingham. 

Mehran has been engaged in supervising a number of PhD students. His research portfolio and interest lie in the field of smart management of infrastructure systems, investigating the application of robotics and autonomous systems for condition monitoring and repair of urban infrastructure. 

Kun Chen

Joint PhD student on Digital Twin for Pavement Maintenance Decision-Making, University of Birmingham, and University of Nottingham
See bio

Kun Chen is a 2nd year PhD student with University of Birmingham and Nottingham in Civil Engineering department, his current research focus is on Digital Twins and Machine Learning for pavement performance prediction, Data science, Physics-informed ML modelling, and Intelligent asset management and maintenance decision-making support 

Mingjie Chu

PhD student in Department of Electrical and Electronic Engineering, University of Manchester 
See bio

Mingjie Chu is a 2nd year PhD student supervised by Dr Long Zhang in the department of electrical and electronic engineering from the University of Manchester. Currently his research focuses on sparse sampling, system modeling as well as data-driven methods. One of his particular research interests lies in system identification with a lower sampling rate by taking advantages of the sparsity of the signal.

Alvaro Garcia Hernandez

Associate Professor, Faculty of Engineering
See bio

Alvaro worked as a researcher since 2004, he finished his master’s in civil engineering at the University of Castilla-la Mancha and started his PhD at the University of Cantabria, both in Spain. 

During his doctorate he proposed the first effective method for knowing the times of polishing industrial concrete pavements. In 2008 he moved to the TU Delft, in the Netherlands, as a postdoc. There he created the first technologies to improve the self-healing properties of asphalt concrete. In 2011, he moved to Empa, in Switzerland where after one year, he became responsible for thematic area “Innovative and Multifunctional Pavements” (EMPave) at the Road Engineering and Sealing components laboratory. The objective of this thematic area was to solve common road problems through unconventional modifications of the pavement.  

He works at the University of Nottingham since November 2013.