INGENIEUR
‟
We use Huawei Cloud’s ModelArts deep learning
image recognition module to identify photographs
of grouting holes and determine which are filled,
which are not, and which are for threading pipes.
buildings will make up more than 20% of all
new buildings by 2020 and over 50% by 2025.
The prefabricated construction industry is set
for rapid development, and while there are vast
market opportunities, there will invariably be some
technical problems that need an urgent solution.
For example, ensuring the strength of floors in
the connection process requires all the grouting
holes to be filled. In the past, this required
people to inspect on-site photographs, which
was very inefficient. Now we use Huawei Cloud’s
ModelArts deep learning image recognition
module to identify photographs of grouting holes
and determine which are filled, which are not,
and which are for threading pipes. Typically, you
can upload 100 on-site images and achieve an
effective identification rate of close to 80%. This
solution has made the inspection department
over 50% faster and reduced construction errors
by 30%.
Use Case 5: Structural damage identification
The fifth use case is structural damage
identification. Sports stadiums, for example,
are impacted by fatigue, corrosion, and ageing
over time. And inevitably some damage occurs.
Locating and defining the state of the damage is
a crucial task.
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However, sensors are only installed in parts of
the structure. This is because the sensor layout
must not impact the original structural properties
of the building. Moreover, installing many sensors
is not cost effective. As a result, we have to place
as few sensors as possible, while trying to obtain
a true picture of the building’s performance. This
requires us to perform mechanical back analysis
and damage identification from the data.
In the past, this identification process would
mostly be applied to a few beams or boards. But
by harnessing Huawei Cloud ModelArts, we can
now identify more complex damage. Using Huawei
Cloud’s EI platform, we have made mechanical
simulations 100 times faster, and are able to
implement an identification solution that supports
real results from data, covers the whole structure,
and includes monitoring and prediction.
In the future, we want to expand to low-
energy, low-cost IoT applications to improve O&M
management efficiency and lower costs. We
are also seeking to develop a structural health
monitoring platform to eliminate security risks
at an early stage to support the development
of smart cities and smart transportation. We
also hope to build a big data platform in the
construction health monitoring field to promote
the development of the entire industrial chain.