We’re excited to launch a new series where we explore how Structural Health Monitoring (SHM) systems are applied in the real world — transforming high-resolution data into reliable engineering insights.
Our mission: Help civil and structural engineers make infrastructure safer, smarter, and more predictable

What happens when a bridge is intentionally damaged under controlled conditions — and every step of its dynamic behavior is tracked in high resolution?
As part of the EU-funded IRIS project, the S101 prestressed concrete bridge (built in 1960) over the A1 Westautobahn near Reibersdorf, Austria, was subjected to multiple progressive damage scenarios before being demolished. The objective was to validate SHM techniques in a real-world setting using actual structural degradation data. SVIBS where through the EU-Marie Curie, ISMS project, SVIBS was granted access to the S101 data to test the damage detection algorithms as well as the SSI-UPCX Operational Modal Analysis method developed for ARTeMIS Modal Pro.

¤ 15 triaxial accelerometers (45 channels) installed on the bridge deck 
¤ Measurements carried out by VCE and the University of Tokyo 
¤ 714 datasets recorded over 4 days (Dec 10–13, 2008) 
¤ Sampling rate: 500 Hz, totaling over 118 million data samples 
¤ SHM Software and Techniques Used
¤ ARTeMIS Modal Pro — for Operational Modal Analysis and SHM 
SSI-UPCX algorithm — for high-resolution modal parameter identification
¤ Modal Parameter History Plugin – For tracking of the identified modes 
¤ Damage Detection Plugin — including modal and statistical indicators 
¤ Automatic File Upload Plugin — for continuous, unattended data  processing

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A Reference State Model was established from the first 100 healthy measurements 
All datasets were analyzed using statistical damage indicators and merged to a single indicator using a control chart 
The system clearly detected the initiation and progression of structural damage 
The Global Health Status expressed in terms of a traffic light from safe (green) → critical (yellow) → unsafe (red) — then complete the automation for better predictive inspection planning 

This case demonstrates how advanced SHM systems can reliably detect and track structural degradation - without visual inspection, traffic disruption, or invasive testing.
It’s a powerful example of how data-driven monitoring improves infrastructure safety and decision-making.

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