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ISSN 2753-3239
ISSN 2753-3239
CCC: 15
PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Edited by: J. Pombo
Paper 8.12
Concept of Bio-Inspired Cyber-Physical Railway Infrastructure Towards Predictive Maintenance: Distributed Piezoelectric Sensing and Physics-Informed Digital Twins
Z. Hadas1, P. Hadraba2, F. Kšica2, J. Chalupa2, M. Mangova2, J. Kovar2, L. Babic3, M. Bim3 and O. Plasek2
1Faculty of Mechanical Engineering, Brno University of Technology, Czech Republic
2, Brno University of Technology, Czech Republic
3FORM–THERMIT, A GOLDSCHMIDT COMPANY, Brno, Czech Republic
Full Bibliographic Reference for this paper
Z. Hadas, P. Hadraba, F. Kšica, J. Chalupa, M. Mangova, J. Kovar, L. Babic, M. Bim, O. Plasek, "Concept of Bio-Inspired Cyber-Physical Railway Infrastructure Towards Predictive Maintenance: Distributed Piezoelectric Sensing and Physics-Informed Digital Twins", in J. Pombo, (Editor), "Proceedings of the Seventh International Conference on
Railway Technology:
Research, Development and Maintenance
",
Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 15, Paper 8.12, 2026, doi:10.4203/ccc.15.8.12
Keywords: railway infrastructure, IoT, sensing, cyber-physical system, digital twin, predictive maintenance.
Abstract
This paper presents a conceptual framework for bio-inspired cyber-physical railway infrastructure based on distributed piezoelectric sensing, low-power edge computing, and physics-informed digital twins. Piezoelectric elements integrated into rails, switches, and structural components generate diagnostic signals directly from train-induced dynamic excitation. Analog front-end preprocessing and edge AI enable low-power extraction of physically interpretable indicators related to stiffness changes, modal behavior, damping evolution, and infrastructure degradation. Processed state markers are transmitted to continuously evolving digital twins supporting predictive maintenance and infrastructure management. The proposed architecture aims to reduce maintenance costs, improve degradation detection, and support scalable deployment of intelligent railway infrastructure systems.
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