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Internal bridge damage visible through Fujitsu technology



Fujitsu Limited and Fujitsu Laboratories Ltd have developed a new technology that can sense the internal damage in bridge structures through the use of Artificial Intelligence.

Fujitsu’s Deep Learning Technology has accurately estimated the internal damage by using sensors attached to the bridge, by aggregating the vibration data from the sensors using FUJITSU Human Centric AI Zinrai technology.

The latest tests have validated the technology using data obtained from verification tests of fatigue degradation of bridges carried out by the Research Association for Infrastructure Monitoring System (RAIMS, a mutual aid organisation that carries out joint research into technologies used in industrial activities.

Fujitsu has developed a new technology that can sense the internal damage in bridge structures through the use of Artificial Intelligence.

By using this technology, maintenance and management of the structures can be planned more easily. In the past, visual inspections of structures needed to be carried out, with all the associated traffic management and risk to engineers, and the fact that only external damage can be assessed.

It is anticipated that these issues may now be resolved through the application of ICT to maintenance and management tasks for bridges and other social infrastructure.

During the testing, this newly developed technology was applied to vibration data collected from acceleration tests (wheel load running test) performed by RAIMS. The results validated the effectiveness of the technology, with analysis of data from an acceleration sensor at a single location on the surface of a bridge, confirming that it is possible to estimate the degree of damage across a wide area of a bridge’s interior. Additionally, detecting the occurrence of internal stress using this technology allows for the estimation of damage in its earliest stages, and can contribute to early countermeasures.

Fujitsu will conduct trials using vibration data from actual bridges, with the goal of real-world usage by around 2018.

 

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