GCF Research to Be Presented at the 9WCSCM

Monitoring insulated rail joints with optical fibre in Sheffield

Research conducted by GCF – Generale Costruzioni Ferroviarie on railway infrastructure monitoring will be presented at the 9th World Conference on Structural Control and Monitoring (9WCSCM), to be held from 19 to 22 July 2026 at the University of Sheffield, United Kingdom. At the heart of the international conference will be the scientific paper "A Field-Based Damage Index for Insulated Rail Joints Using FBG Extensometers", developed by GCF in collaboration with the Department of Mechanical Engineering of Politecnico di Milano.

The 9WCSCM brings together the scientific and industrial communities working in the field of structural control and monitoring, providing a forum for the exchange of methods, technologies and applications developed for civil infrastructure, mechanical systems, and the aerospace and energy sectors.

Assessing rail joint condition through a damage index

The study presented by GCF focuses on bonded insulated rail joints, track components that ensure the electrical separation between different track sections, a prerequisite for the correct operation of railway signalling circuits.

Over time, these joints are subjected to the stresses generated by train traffic, as well as to changing operating and environmental conditions, which may lead to deterioration. Understanding their behaviour under service conditions can help identify anomalous situations and support maintenance planning.

The methodology employs FBG (Fiber Bragg Grating) extensometers, installed directly on the rail to measure the response of the joint as trains pass. These measuring devices are part of COGI - Controllo Ottico dei Giunti Isolanti (Optical Monitoring of Insulated Rail Joints), the patented system developed by GCF's Research & Development Department to monitor the deformation of insulated rail joints. The acquired signals are processed to extract parameters that are sensitive to the structural condition of the component.

Based on the analysis of these parameters, a damage index has been developed to synthesise the information collected by the system and identify any deviations from the reference behaviour.

Field validation

The defining feature of the study is its field-based approach. The methodology was developed and validated using data collected from insulated rail joints installed along an operational railway line over an extended monitoring period.

The use of measurements acquired directly on the infrastructure made it possible to analyse the behaviour of the joints under the variability associated with railway traffic and real operating conditions. This was an essential step in assessing the applicability of the damage index beyond the controlled conditions of laboratory testing.

"Presenting our work at the 9WCSCM represents an important milestone for this research," explains Marco Cavaciuti, engineer at GCF's Research & Development Department and PhD candidate at Politecnico di Milano. "The study is based on measurements acquired daily on the railway line and aims to transform these data into useful information for assessing the condition of insulated rail joints and supporting maintenance activities."

The collaboration with the Department of Mechanical Engineering of Politecnico di Milano combines GCF's expertise in railway infrastructure and field monitoring activities with the University's scientific and methodological know-how.

Participation in the 9WCSCM forms part of the research programme launched by GCF to develop and validate, under real operating conditions, diagnostic systems capable of providing an increasingly accurate understanding of railway infrastructure conditions. This research has recently led, among other initiatives, to the innovative FUTURO – Photonics for Railway Infrastructure project, funded under the Collabora & Innova programme of the Lombardy Region, which aims to develop a safe railway monitoring and predictive maintenance system based on Artificial Intelligence and Machine Learning.

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