Invited Speaker

Abstract: Accurate impact force identification is crucial for ensuring the structural integrity and safety of engineering systems, yet the inverse problem remains a formidable challenge due to its ill-posed nature. Traditional regularization techniques, such as Tikhonov regularization and singular value decomposition, have long been employed to stabilize solutions and mitigate uncertainties. However, recent advancements in machine learning, particularly hybrid neural network frameworks, are revolutionizing the field by offering robust and data-driven alternatives.

This talk will provide a comprehensive journey through impact force identification methodologies, from classical inverse problem-solving approaches to state-of-the-art hybrid deep learning techniques. We will explore how dynamic response data from piezoelectric sensors can be leveraged to reconstruct unknown impact forces and discuss the transition from deterministic mathematical frameworks to data-driven neural architectures. Case studies involving composite structures, including carbon-fiber epoxy honeycomb panels, will illustrate the efficacy of these approaches.

By bridging the gap between physics-based modeling and artificial intelligence, this talk will highlight the advantages, challenges, and prospects of hybrid methodologies in structural health monitoring. Insights will be gained into how these techniques enhance precision, reduce computational costs, and open new frontiers for real-time impact force reconstruction in complex engineering systems.

Biography: Dr. Hamed Kalhori is a lecturer in the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), Australia, and in the Department of Mechanical Engineering at Bu-Ali Sina University, Hamedan, Iran. He earned his PhD in Mechanical Engineering from the University of Sydney in 2017 and has since been recognized with multiple research grants and academic awards, including the prestigious 2019 University of Sydney Dean’s Award for Excellence in Teaching and Tutoring.

With extensive experience in research and academia, Dr. Kalhori has held key roles at the Centre for Autonomous Systems at UTS and Data61-CSIRO, where he contributed to advanced structural health monitoring projects across New South Wales in Australia. His research expertise spans inverse dynamics, linear and nonlinear vibrations, structural health monitoring, and smart structures and materials.

An active researcher, Dr. Kalhori has authored over 40 peer-reviewed journal articles and 20 international conference papers. He is a regular reviewer for leading journals such as Mechanical Systems and Signal Processing, Journal of Sound and Vibration, and Composite Structures. Beyond his publications, he has led international collaborations, conducted numerous workshops and short courses worldwide, and secured research funding in vibration-related fields.