Fujitsu Labs applies AI to bridge inspection
- Author:Ella Cai
- Release on:2017-08-29
Fujitsu Labs has developed sensor data analysis technology that can aggregate vibration data with sensors attached to the surface of a bridge, and then estimate the degree of the bridge’s internal damage through the application of what it calls “Fujitsu Human Centric AI Zinrai technology” – Fujitsu’s approach to artificial intelligence.
The technology was validated using data obtained from verification tests of fatigue degradation of bridges carried out by Japan’Research Association for Infrastructure Monitoring System (RAIMS), a mutual aid organisation that conducts research into technologies used in industrial activities.
The technology enables enhanced maintenance and management tasks, making it possible to remotely estimate the degree of internal damage to bridge infrastructure.
As many bridges built in Japan’s period of high economic growth deteriorate, the work required to maintain and manage this type of infrastructure has increased rapidly, accompanied by social problems including rising maintenance costs and a shortage of engineers.
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
Bridges are usually inspected visually to check structures for damage. However, inspectors can only identify abnormalities or anomalies appearing on the surface, so could miss information indicating the degree of internal damage.
There have been many using sensors attached to the surface of the bridge deck, to measure vibration data to evaluate levels of damage, although accurately understanding the degree of damage in the interior of the deck has been an issue.
Fujitsu Labs’ proprietary deep learning AI technology for time-series data has been developed to discover anomalies and express in numerical terms degrees of change that demonstrate changes in the status of objects such as structures or machinery, and detect the occurrence of abnormalities or distinctive changes. The technology learns from the geometric characteristics extracted from complex, constantly changing time-series vibration data collected by sensors equipped on IoT devices, enabling users to estimate and validate the state of degradation or failure.
This technology has now been confirmed through the application of verification test data from RAIMS.
The technology was applied to vibration data collected from acceleration tests (wheel load running test) performed by RAIMS. The results showed that the geometric characteristics extracted from the vibration data would appear as a single cluster when the bridge was intact, but the shape changes when the bridge had developed internal damage.
The degree of abnormality and the degree of change that can be calculated by converting the geometric characteristics to numerical values corresponds with the results measured by strain sensors embedded within the bridge deck, validating the effectiveness of the technology.
From the analysis results of data from an acceleration sensor at a single location on the surface of a bridge, Fujitsu confirmed that it is possible to estimate the degree of damage across a wide area of a bridge’s interior.
Fujitsu will conduct trials using vibration data from actual bridges, with the goal of real-world use by around 2018.
The technology was validated using data obtained from verification tests of fatigue degradation of bridges carried out by Japan’Research Association for Infrastructure Monitoring System (RAIMS), a mutual aid organisation that conducts research into technologies used in industrial activities.
The technology enables enhanced maintenance and management tasks, making it possible to remotely estimate the degree of internal damage to bridge infrastructure.
As many bridges built in Japan’s period of high economic growth deteriorate, the work required to maintain and manage this type of infrastructure has increased rapidly, accompanied by social problems including rising maintenance costs and a shortage of engineers.
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
Bridges are usually inspected visually to check structures for damage. However, inspectors can only identify abnormalities or anomalies appearing on the surface, so could miss information indicating the degree of internal damage.
There have been many using sensors attached to the surface of the bridge deck, to measure vibration data to evaluate levels of damage, although accurately understanding the degree of damage in the interior of the deck has been an issue.
Fujitsu Labs’ proprietary deep learning AI technology for time-series data has been developed to discover anomalies and express in numerical terms degrees of change that demonstrate changes in the status of objects such as structures or machinery, and detect the occurrence of abnormalities or distinctive changes. The technology learns from the geometric characteristics extracted from complex, constantly changing time-series vibration data collected by sensors equipped on IoT devices, enabling users to estimate and validate the state of degradation or failure.
This technology has now been confirmed through the application of verification test data from RAIMS.
The technology was applied to vibration data collected from acceleration tests (wheel load running test) performed by RAIMS. The results showed that the geometric characteristics extracted from the vibration data would appear as a single cluster when the bridge was intact, but the shape changes when the bridge had developed internal damage.
The degree of abnormality and the degree of change that can be calculated by converting the geometric characteristics to numerical values corresponds with the results measured by strain sensors embedded within the bridge deck, validating the effectiveness of the technology.
From the analysis results of data from an acceleration sensor at a single location on the surface of a bridge, Fujitsu confirmed that it is possible to estimate the degree of damage across a wide area of a bridge’s interior.
Fujitsu will conduct trials using vibration data from actual bridges, with the goal of real-world use by around 2018.
