Fujitsu AI Technology Helps Prevent Sinkholes in Support of Kawasaki Geological Engineering’s Road Cavity Surveys

Fujitsu AI Technology Helps
Prevent Sinkholes in Support of Kawasaki Geological Engineering’s Road Cavity
Surveys

Kawasaki Geological
Engineering Co., Ltd., Fujitsu Limited, Fujitsu Traffic & Road Data Service
Limited

Tokyo, May 15, 2017

Kawasaki Geological Engineering Co., Ltd.,
Fujitsu Limited, and Fujitsu Traffic & Road Data Service Limited today
announced that Kawasaki Geological Engineering has leveraged Fujitsu’s AI
engine to develop subsurface cavity survey technology. Being able to more
accurately detect underground cavities that can cause sinkholes in roads, this
technology will be made available as a service beginning in the summer of 2017.

Kawasaki Geological Engineering is using FUJITSU
Cloud Service K5 Zinrai Platform Service Zinrai Deep Learning —Fujitsu’s deep
learning platform service—to analyze and process the huge volume of radar
images collected with underground radar probe equipment. This increases the
efficiency of the search for underground cavities, which previously required
expert technicians to make visual determinations. In addition, by connecting
this technology with a service which displays the problem spots on a map—provided
by Fujitsu Traffic & Road Data Service—the positions of cavities beneath
road surfaces can be accurately understood, making the necessity of road
repairs clear to local governments.

Background

In Japan, there are about 3,300 incidents of
collapse caused by cavities under the surface of roads annually, making an
issue in society. The primary cause of these collapses is aging sewer pipes,
often located about three meters underground. This creates a growing need for a
system that can determine the risk of a collapse without excavating the road to
investigate.

Currently, using underground radar probe
equipment developed by Kawasaki Geological Engineering, underground surveys,
which were previously limited to about one meter, can now be done to about five
meters deep, which is expected to significantly improve the reliability of
cavity explorations beneath road surfaces going forward. Because the massive
volume of image data collected by the underground radar probe equipment must be
visually evaluated by well-practiced expert technicians, however, there have
been issues with preserving objectivity and ever increasing workloads.

Figure 1:
Kawasaki Geological Engineering’s subsurface cavity identification technology

Figure 2:
Example of image data collected from the underground radar probe equipment

Features of the Newly
Developed Technology

Analysis and processing of the massive volume of
radar images taken with Kawasaki Geological Engineering’s world-leading underground
cavity detection technology will be carried out using image recognition through
the Zinrai Deep Learning platform service, which was launched by Fujitsu in
April 2017. The AI will be trained through machine learning on images where
changes in radar reflection are displayed as waveforms, and will then determine
whether they are cavities or sewer pipes.

In a trial of cavity identification, in
comparison with existing visual identification by expert technicians, not only
was the cavity identification using AI able to accurately determine cavities
from image data, it did the analysis in one-tenth of the time.

Future Plans

Going forward, Kawasaki Geological Engineering
will link its AI-based subsurface cavity detection with unified services
visualizing investigated locations provided by Fujitsu Traffic & Road Data
Service, as well as continue to shrink and lighten its underground radar probe
equipment, with the goal of commercializing it as a service that can broadly
automate measurement tasks for organizations such as local governments,
supporting more efficient road maintenance management.

In addition, Kawasaki Geological Engineering
will work to expand its cutting-edge subsurface cavity identification
technology around the globe, beyond just Japan, to help prevent road collapses
which are becoming an issue in society.