Dr. Abdollah Malekjafarian
Dr. Abdollah Malekjafarian received his PhD in Civil Engineering from University College Dublin in 2016. He is currently an Assistant Professor and leader of “Structural Dynamics and Assessment Laboratory (SDA-Lab)” in the school of Civil Engineering at UCD (https://sdal.ucd.ie/). His main areas of research interest are Structural Dynamics and Random Vibrations for Civil Infrastructure including "Transport Infrastructure" and "Offshore Wind Turbines". He is currently the Principal Investigator of Di-Rail project (https://di-rail.ucd.ie/), which is funded by Science Foundation Ireland, under the Frontiers for the Future call. He is also the Coordinator of WindLEDeRR project (https://windlederr.ucd.ie/), funded by Sustainable Energy Authority of Ireland, under the RD&D call. Abdollah is a member of the Young Academy of Ireland (YAI) selected by the Royal Irish Academy (RIA). He also received the "KB Broberg" medal in 2020 and Royal Irish Academy Charlemont award in 2018 for his outstanding contribution to his field of research. He is also a member of editorial boards of “Journal of Shock and Vibration” and “Journal of Vibroengineering”.
Vibration-based Monitoring of Transport Infrastructure Using Drive-by Data Collected from Moving Vehicles
Dr. Abdollah Malekjafarian
University College Dublin, Dublin, Ireland
Deterioration and damage will inevitably occur in transport infrastructure such as road/railway bridges and railway tracks. Advances in structural health monitoring (SHM) have enabled the automated diagnosis of damage in infrastructure, but efficient and scalable solutions have been limited. Recently, SHM approaches using mobile sensing, in which data are collected from sensors installed on a moving vehicle, have received increasing attention as a practical solution due to their advantages in cost, low-maintenance, and scalability. These approaches which are also called drive-by or indirect techniques, assess infrastructure damage states using the data collected from sensors on board passing vehicles.
This talk discusses recent progress have been made in our research group at University College Dublin on this topic and provides illustrative examples. A novel bridge monitoring regime which enables the presence, type, location and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre-measured training data, will be discussed. Experimental results from a laboratory-scale vehicle-bridge interaction model will be presented. In another example, a novel framework for railway track geometry inspection using vibration data collected from a high-speed train, will be presented. In this learning-based anomaly track detection approach, a subset of features that best characterizes the track condition is defined. A data-driven based anomaly detection approach is then developed to assess and identify track geometrical defects. Results from a comprehensive dataset from field measurements using a highspeed train will be presented.