School of Civil Engineering, University College Dublin, Dublin, Ireland;Dr. Abdollah Malekjafarian is an Associate Professor in the School of Civil Engineering at University College Dublin. His research focuses on structural health monitoring (SHM) of civil infrastructure, with particular emphasis on data-driven and machine learning approaches for damage detection in bridges, railway systems, and wind energy structures. His work integrates structural dynamics, sensing technologies, and artificial intelligence to develop scalable and cost-effective monitoring solutions for infrastructure systems. He has authored over 190 peer-reviewed publications (including 90 journal articles), reflecting significant contributions to ML-based SHM and drive-by monitoring methodologies. He has secured more than €4.5 million in competitive research funding. He serves on the editorial boards of several international journals, including Journal of Infrastructure Intelligence and Resilience, Engineering Applications of Artificial Intelligence, Shock and Vibrations, Discover Energy, and the Journal of Vibroengineering. He is a member of the Young Academy of Ireland and serves on the Science Communication Committee of the European Academy of Wind Energy. He is also an executive board member of the International Society of Mechanical System Dynamics, where he serves as Vice-Chairman of the Vibration-Based Mechanical System Diagnosis Committee.

Centre for Railway Research, Nihon University, Tokyo, Japan;Prof. Hitoshi Tsunashima completed his master course in aeronautical engineering at the University of Osaka Prefecture in 1983. He joined the Kobe Steel Ltd. in 1983, where he carried out his research and development of Automated People Mover. He obtained his Doctor degree from The University of Tokyo in 1995. He joined Department of Mechanical Engineering, College of Industrial Technology, Nihon University in 1996 and currently he is a professor and the deputy director of Centre for Railway Research. His main research expertise includes dynamics of railway vehicle, condition monitoring of railway system, human factors and ergonomics. PRIZES: 1992 SAE Vincent Bendix automotive electronics engineering award, 2007 Best paper award of 15th symposium on Transportation and Logistics, The Japan Society of Mechanical Engineers, 2009 Best paper award of Japan Railway Engineers Association, 2017 Transportation and Logistics Award, The Japan Society of Mechanical Engineers.

Department of Structural and Geotechnical Engineering, University of São Paulo, São Paulo, Brazil;Cássio Bragança is Mechanical Engineer from UFMG (2019). Master of Science in Structural Engineering from UFMG (2022). Currently a Ph.D. candidate in Civil Engineering at the Polytechnic School of the University of São Paulo. He has 5 years of professional experience in the field of acoustics and vibrations. He has authored several publications in international journals and published book chapters, in addition to participating in scientific congresses and events at both national and international levels. His primary research interests include: digital signal processing, environmental acoustics, structural dynamics, bridges, SHM (Structural Health Monitoring), numerical modeling of railway systems, remote inspection methodologies assisted by UAVs (Unmanned Aerial Vehicles), vehicle-infrastructure interaction, and damage detection through drive-by methodologies.

Civil Engineering, Universidade Federal de Santa Catarina, Florianópolis, Brazil;Ph.D. in Civil Engineering from the Federal University of Santa Catarina (UFSC), Brazil, with a visiting Ph.D. period at the Instituto Superior de Engenharia do Porto (ISEP–IPP), Portugal. Currently serves as a Temporary Assistant Professor in the Department of Civil Engineering at UFSC. His research focuses on Structural Health Monitoring (SHM) of railway bridges using drive-by monitoring approaches for the detection of bridge scour and railway track defects through vibration data and machine learning techniques, encompassing finite element model updating, optimization, structural reliability, inverse problems, and uncertainty quantification.